TW201941219A - Diagnosis support program - Google Patents

Abstract

Provided is a diagnostic support program that can display the movement of an area that changes shape with each respiration element that includes all or part of an inhale or an exhale. The program includes: processing in which a plurality of frame images are acquired from a database that stores images; processing in which the period of a respiration element that includes all or part of an inhale or an exhale is specified on the basis of the pixels in a specific area of the frame images; processing in which a lung field is detected on the basis of the specified period of the respiration element; processing in which the detected lung field is partitioned into a plurality of block areas, and the change in the block areas of the frame images are calculated; processing in which a Fourier transform is performed on the change in the block areas of the frame images; processing in which, of the spectra obtained after the Fourier transform, the spectra that are within a fixed band that includes the spectrum that corresponds to the period of the respiration element are extracted; processing in which an inverse Fourier transform is performed on the spectra extracted from the fixed band; and processing in which post-inverse-Fourier-transform images are displayed on a display.

Description

Diagnostic support program

The invention relates to a technology for analyzing a human body image and displaying the analysis result.

When a physician performs a lung diagnosis based on a dynamic image of the chest, it is important to observe the chronological dynamic images of the chest taken in the natural breathing state of the subject. A spirometer with easy access to physiological data, RI (Radio Isotope) examination, simple X-ray photographs to obtain morphological data, CT (Computed Tomography), and other methods used to evaluate lung function are: Known. However, it is not easy to obtain both physiological data and morphological data efficiently.

In recent years, attempts have been made to use a semiconductor image sensor such as a FPD (Flat panel detector) to capture a dynamic image of a human chest and use it for diagnosis. For example, in Non-Patent Document 1, it is disclosed that a differential image is generated that represents the difference in signal values between a plurality of frame images constituting a moving image, and the maximum value of each signal value is obtained from the differential image. And show the technology.

Further, in Patent Document 1, a technique is disclosed in which a lung field region is extracted from each frame image of a plurality of frame images representing the motion of a human chest, and the lung field region is divided into a plurality of small regions, and Correspond and analyze the divided small areas between the multiple frame images. According to this technique, a characteristic amount indicating an activity of a divided small area is displayed.
[Prior technical literature]
[Patent Literature]

[Patent Document 1] Japanese Patent No. 5874636
[Non-patent literature]

[Non-Patent Document 1] "Basic Imaging Properties of a Large Image Intensifier-TV Digital Chest Radiographic System" Investigative Radiology: April 1987; 22: 328-335.

[Problems to be solved by the invention]

However, as in the technique described in Non-Patent Document 1, it is difficult for a physician to grasp the morbid state by displaying only the maximum value of the difference between the frames of each pixel of a moving image. In addition, as in the technique described in Patent Document 1, it is not sufficient to grasp only the characteristic amount to grasp the pathological state. Therefore, it is desirable to display an image reflecting the state of the breathing or pulmonary blood vessels. That is, it is desirable to grasp the breathing state and overall vascular dynamics of the subject, that is, the human body, and display an image showing the actual movement based on the waveform or frequency of the blood vessels or blood flow in the breath, heart, and hilum, or the tendency of the image.

The present invention has been made in view of such a situation, and an object thereof is to provide a diagnostic support program capable of displaying an activity in a region whose shape varies depending on respiratory elements including all or a part of exhalation or inhalation. More specifically, the purpose is to digitize the agreement rate or other inconsistency rate with respect to the waveform and Hz obtained, and calculate the value of the auxiliary diagnosis for the data of the new object to be measured. The values are visualized to produce images that assist diagnosis.
[Technical means to solve the problem]

(1) In order to achieve the above purpose, the following method is adopted in this case. That is, a diagnostic support program of one aspect of the present invention is characterized in that it is a person who analyzes an image of a human body and displays an analysis result, and causes a computer to execute the following processing: obtaining a plurality of frame images from a database storing the above images ; Identifying at least one frequency of breathing elements including all or part of the breath or inhalation based on pixels in a specific area of each of the above frame images; detecting lung fields based on at least one frequency of the above-mentioned specific breathing elements; The detected lung field is divided into a plurality of block areas, and the image changes of the block areas in the frame images are calculated. The image changes of the block areas in the frame images are Fourier transformed. Conversion; extracting a frequency spectrum within a certain frequency band including a frequency spectrum corresponding to at least one frequency of the respiratory element in the spectrum obtained after the Fourier transform; performing inverse Fourier transform on the frequency spectrum extracted from the certain frequency band; and Each image after the inverse Fourier transform is displayed on a display.

(2) Furthermore, the diagnostic support program of one aspect of the present invention is characterized by further including the following processing: using a filter to capture the frequency of the noise in the spectrum obtained after the above Fourier transform, and including the frequency difference from the above frame A frequency other than the frequency of the respiratory element obtained from the image, or a frequency spectrum within a certain frequency band corresponding to the input frequency or frequency band.

(3) Furthermore, the diagnostic support program according to one aspect of the present invention is characterized by further including the following processing: generating an image between the frames based on the frequency of the respiratory element and the frame images.

(4) Furthermore, the diagnostic support program of one aspect of the present invention is characterized in that it is a person who analyzes the image of the human body and displays the analysis result, and causes the computer to execute the following processing: obtaining a plurality of messages from a database storing the above-mentioned images Frame image; specific at least one frequency of cardiovascular beat elements taken from the subject's heartbeat or vascular beat; based on the pixels in a specific area of each of the above frame images, specify all or At least one frequency of a part of the respiratory element; detecting the lung field based on the at least one frequency of the specified respiratory element; dividing the detected lung field into a plurality of block regions, and calculating the block regions in each frame image Fourier transform the image changes of each block area in each frame image; extract at least one frequency in the spectrum obtained after the Fourier transform including the cardiovascular pulsation element Corresponding spectrum within a certain frequency band; performing inverse Fourier transform on the frequency spectrum extracted from the above certain frequency band; and displaying each image after the inverse Fourier transform In the display.

(5) Furthermore, a diagnostic support program of one aspect of the present invention is characterized in that it is a person who analyzes an image of a human body and displays an analysis result, and causes a computer to execute the following processing: obtaining a plurality of messages from a database storing the above-mentioned images Frame image; specific at least one frequency of cardiovascular beat elements taken from the subject's heartbeat or vascular beat; detection of the lung field; dividing the detected lung field into a plurality of block areas, and calculating each of the above block diagrams The image change of the block area in the image; Fourier transform the image change of each block area in the above frame image; the frequency spectrum obtained after capturing the Fourier transform includes the cardiovascular pulse Spectrum within a certain frequency band of the spectrum corresponding to at least one frequency of the element; performing inverse Fourier transform on the frequency spectrum extracted from the fixed frequency band; and displaying each image after the inverse Fourier transform on the display.

(6) Furthermore, the diagnostic support program of one aspect of the present invention is characterized by further including the following processing: using a filter to capture the frequency of the noise in the frequency spectrum obtained after the Fourier transform is used, The frequency of the cardiovascular pulsation element obtained from the image, or the frequency spectrum within a certain frequency band corresponding to the input frequency or frequency band.

(7) Furthermore, the diagnostic support program according to an aspect of the present invention further includes the following processing: generating an image between the frames based on the frequency of the specific cardiovascular pulsation element and the frame images.

(8) Furthermore, a diagnostic support program of one aspect of the present invention is characterized in that it is a person who analyzes an image of a human body and displays an analysis result, and causes a computer to execute the following processing: obtaining a plurality of messages from a database storing the above-mentioned images Frame image; specifying at least one frequency of the pulsation element of the blood vessel from the subject's pulsation of the blood vessel; dividing the analysis range set for each frame image into a plurality of block areas, and calculating the above frame images Fourier transform of the image change of each block area in the above frame image; Fourier transform of the spectrum obtained by extracting the Fourier transform includes the cardiovascular pulsation element Spectrum in a certain frequency band of the spectrum corresponding to at least one frequency; performing inverse Fourier transform on the frequency spectrum extracted from the certain frequency band; and displaying each image after the inverse Fourier transform on the display.

(9) Furthermore, the diagnostic support program of one aspect of the present invention is characterized by further including the following processing: using a filter to capture a frequency including noise in the frequency spectrum obtained after the Fourier transform, and including a signal from the above frame A frequency other than the frequency of the blood vessel pulsation element obtained from the image, or a frequency spectrum within a certain frequency band corresponding to the input frequency or frequency band.

(10) Furthermore, the diagnostic support program according to one aspect of the present invention is characterized by further including the following processing: generating an image between the frames based on the frequency of the specified vascular pulsation element and the frame images.

(11) Furthermore, a diagnostic support program of one aspect of the present invention is characterized in that it is a person who analyzes an image of a human body and displays an analysis result, and causes a computer to execute the following processing: obtaining a plurality of messages from a database storing the above-mentioned images Frame image; based on pixels in specific areas of each frame image, specifying at least one frequency of breathing elements including all or part of the breath or inhalation; detection based on at least one frequency of the specific breathing elements described above Lung field and diaphragm; Divide the detected lung field into a plurality of block areas, calculate the change rate of pixels in the block area in each frame image; use the change rate of pixels in the block area, and The ratio of the change rate of the dynamic part of the breathing linkage is the tuning rate, and only the block areas where the tuning rate falls within a predetermined range are captured; each image including only the block areas extracted above is displayed on the display.

(12) Furthermore, a diagnostic support program according to one aspect of the present invention is characterized by further including the following processing: specifying at least one frequency of a cardiovascular pulsation element extracted from a subject's heartbeat or blood vessel pulsation, or extracted from a blood vessel pulsation. At least one frequency of the pulsatile element of the vessel.

(13) A diagnostic support program according to an aspect of the present invention is characterized in that the logarithm of the tuning rate is set to a certain range including zero.

(14) Furthermore, the diagnostic support program of one aspect of the present invention is characterized by further including the following processing: using at least one Bezier curve on the lung field detected in a specific frame, and detecting other frames In the lung field.

(15) In another aspect of the present invention, the diagnostic support program is characterized in that an internal control point is selected in the detected lung field, and the lung field is divided by a curve or a straight line passing through the internal control point in the lung field.

(16) Furthermore, a diagnostic support program according to one aspect of the present invention is characterized in that the interval between control points of the lung field extension and its vicinity is relatively enlarged, and according to the detection of each part in the lung field described above, The expansion ratio relatively reduces the interval between the internal control points.

(17) Furthermore, a diagnostic support program according to one aspect of the present invention is characterized in that, in the detected lung field, the interval between control points is relatively enlarged according to the entry of the human body toward the head and tail, or according to a specific vector The distance between the control points is relatively enlarged in the direction.

(18) Furthermore, the diagnostic support program of one aspect of the present invention is characterized by further including the following processing: using at least one Bezier surface on the lung field detected in a specific frame, and detecting other frames In the lung field.

(19) Furthermore, the diagnostic support program of one aspect of the present invention is characterized by further including the following processing: using at least one Bezier curve on a predetermined analysis range of a specific frame to detect other The range corresponding to the above analysis range in the frame.

(20) Furthermore, the diagnostic support program of one aspect of the present invention is characterized by further including the following processing: using at least one or more Bezier curve to draw at least the lung field, blood vessels, or the heart.

(21) Furthermore, a diagnostic support program of one aspect of the present invention is characterized in that it is a person who analyzes an image of a human body and displays an analysis result, and causes a computer to execute the following processing: obtaining a plurality of messages from a database storing the above-mentioned images Frame images; use Bezier curves to specify the analysis range for all the frame images obtained above; and detect analysis objects based on changes in intensity within the analysis range.

(22) Furthermore, a diagnostic support program according to one aspect of the present invention is characterized by further including a process of calculating an edge feature of the detected analysis object.

(23) Furthermore, a diagnostic support program according to one aspect of the present invention is characterized by detecting diaphragms by calculating a difference in intensity between successive images, and displaying the diaphragms or the detected diaphragms. An indicator of the position or shape of the dynamic part of the breathing linkage.

(24) Furthermore, a diagnostic support program according to one aspect of the present invention is characterized in that by changing the threshold value of intensity, a diaphragm that is obscured by a part other than the diaphragm is displayed, and the diaphragm is calculated by interpolation. Overall shape.

(25) Furthermore, the diagnostic support program according to one aspect of the present invention is characterized by further including the following processing: from the position or shape of the diaphragm detected or the position or shape of a dynamic part linked to breathing, the above-mentioned respiration is calculated. At least one frequency of the element.

(26) Furthermore, the diagnostic support program according to one aspect of the present invention is characterized by further including a process of spatially normalizing the detected lung field in space or performing temporally normalization by using reconstruction.

(27) A diagnostic support program according to an aspect of the present invention is characterized in that the respiratory element is corrected by changing the phase of at least one frequency of the respiratory element or smoothing the waveform of the respiratory element.

(28) Furthermore, the diagnostic support program according to one aspect of the present invention is characterized by identifying the waveform at any position within the analysis range, extracting the constituent elements of the frequency of the specified waveform, and outputting the constituent elements of the frequency of the waveform. Corresponding image.

(29) Furthermore, a diagnostic support program according to one aspect of the present invention is characterized by detecting the density in the analysis range and removing a portion where the density changes relatively greatly.

(30) Furthermore, the diagnostic support program of one aspect of the present invention is characterized by further including the following processing: the frequency spectrum obtained after the Fourier transformation is selected based on the frequency spectrum composition ratio of the periodic variation unique to the organ, and is selected for Fourier At least one frequency during inverse conversion.

(31) Furthermore, a diagnostic support program according to one aspect of the present invention is characterized by adjusting the X-ray irradiation interval according to at least one frequency of the above-mentioned breathing element, and controlling the X-ray imaging device.

(32) Furthermore, the diagnostic support program of one aspect of the present invention is characterized in that after the inverse Fourier transform described above, only the blocks with relatively large amplitude values are captured and displayed.

(33) Furthermore, the diagnostic support program according to one aspect of the present invention is characterized by further including the following processing: after identifying the lung field, specifying the diaphragm or thorax, and calculating the amount of change of the diaphragm or thorax, Calculate the rate of change.

(34) Furthermore, the diagnostic support program according to an aspect of the present invention further includes a process of multiplying a specific frequency spectrum by a coefficient, and performing an emphasis display based on the specific frequency spectrum multiplied by the coefficient.

(35) A diagnostic support program according to one aspect of the present invention is characterized in that after obtaining a plurality of frame images from a database in which images are stored, in order to specify the frequency or waveform of the respiratory element, a part to be analyzed is applied. Take a digital filter.

(36) Furthermore, a diagnostic support program according to one aspect of the present invention is characterized in that a plurality of frequencies including all or a part of breathing elements of exhalation or inhalation are specified based on pixels in a specific area of each frame image, Each image corresponding to each of the plurality of frequencies of the respiratory element is displayed on a display.

(37) In addition, a diagnostic support program according to one aspect of the present invention is characterized in that, for a specific range of a frame image or more, images clustered at a certain value are selected and displayed on a display.
[Effect of the invention]

According to an aspect of the present invention, it is possible to display an activity in an area whose shape changes according to breathing elements including all or a part of exhalation or inhalation.

First, the basic concept of the present invention will be described. In the present invention, in the breathing or blood vessels of the human body, the area and volume of the lung field, and the movement of other organisms, the activities captured in order to repeat in a certain cycle are within the whole or a certain part of the time axis. Repeated or constant motion (conventional) is captured as a wave and measured. For the measurement results of the waves, (A) the form of the wave itself or (B) the interval (frequency: Hz) of the wave is used. These two concepts are collectively referred to as "basic data."

There may be waves like the same period. For example, if it is breathing, it can be approximated to the following concept.
(Average of `` density '' changes in a rough range) ≒ (changes in thorax) ≒ (transverse diaphragm activity) ≒ (pulmonary function test) ≒ (thoracic and abdominal breathing sensor)

Regarding the "(A) wave form itself", the concept of "wave tunability" is used, and based on this, an image is displayed (Wave form tunable imaging). In addition, regarding the "(B) wave interval (frequency: Hz)", the concept of "frequency tunability" is used, and based on this, an image (Frequency tunable imaging) is displayed.

For example, in the case of the heart, as shown in FIG. 13, "an example of a waveform comparing aortic blood flow and a waveform of ventricular volume", the peak of aortic blood flow does not coincide with the peak or waveform of ventricular volume. However, in FIG. 13, if the time interval of the equal interval is set to 1 cycle as time t1 to t2, time t2 to t3, time t3 to t4, etc., then one cycle of aortic blood flow and ventricular volume One cycle is repeated many times, and it can be said that each waveform is frequency-tuned. Focusing on this waveform, a cycle is specified from the actual measured values shown in FIG. 13 and the model waveform is used to predict the waveform (Wave form). That is, as a method of generating the "waveform as basic data", it can be measured or generated by frequency (cycle). Model waveforms can also be used, and individual waveforms can be averaged and used. If you know the cycle (period) of organs with frequency such as the heart, you can predict the waveform (Wave form), so you can grasp the waveform of aortic blood flow or ventricular volume, and display dynamic images of organs based on the waveform.

In addition, a digital filter can be added in advance in order to prevent other factors from being mixed in when the "density" of breathing, heart, hilum, and the like is changed.

In the present invention, the concept of "breathing element" is used. The so-called "respiratory element" includes all or a part of exhalation or inhalation. For example, "1 breath" can be divided into "1 breath" and "1 breath", and it can also be limited to "0%, 10%, 20" of "1 breath or 1 breath" %, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100% ". Furthermore, only a certain percentage of each breath can be captured, for example, only 10% of the breath is captured for evaluation. You can use any of these materials or the combination of these materials to capture higher-precision images. At this time, it is sometimes calculated multiple times with each other.

This method of consideration is not limited to the "respiratory element" but can also be applied to the "cardiovascular element".

Here, when making the basic data, the feature quantity obtained from a single or a plurality of treatment programs (for example, a certain amount of "density", "volumetry", a variation of the thorax, Multiple diaphragm measurements such as the activity of the diaphragm, "spirometry", two or more chest and abdomen breathing sensors), or the same breathing cycle, complement each other's component acquisition to improve accuracy . Thereby, accuracy can be improved based on certain predictions such as reduction of artifacts and lines. Here, the so-called "density" translates to "density", but means the "absorption value" of pixels in a specific area in the image. For example, in CT, air is used as "-1000", bones are used as "1000", and water is used as "0".

In addition, the components of each other are used to extract the axis, width, and range of the estimated waveform and the activity and width of Hz. In other words, the axis of Hz is averaged by a plurality of overlaps, and the optimal range of the axis, width, range, and Hz is calculated from the variance. At this time, if the Hz (noise) of other actions is captured and there is a wave, the relative measurement may be performed to the extent that the wave is not mixed. That is, there is a case where only a part of the waveform of the entire waveform element is captured.

In this manual, "density" and "intensity" are used separately. "Density" means the absorption value as described above. In the original image of XP or XP animation, the case where the air permeability is high and the high permeability part is white is digitized, and the air is displayed as "-1000 "To display water as" 0 "and bones as" 1000 ". On the other hand, the "intensity" is displayed based on the relative change in "density", for example, normalized (converted) to the width of the density and the degree of the signal. That is, "intensity" is a relative value such as lightness or darkness or emphasis in an image. The period during which the absorption value of the XP image is directly processed is displayed as "Density" or "Density". And, for the convenience of image expression, this is converted as above and displayed as "intensity". For example, when the color is displayed in 256 gray levels from 0 to 255, it becomes "intensity". This term is appropriate in the case of XP or CT.

On the other hand, in the case of MRI (Magnetic Resonance Imaging), even if the air is set to "-1000", the water is set to "0", and the bones are set to "1000", there are also some problems caused by MRI. The pixel value, the type of measurement machine, the physical condition of the person during measurement, body shape, and measurement time cause a large change in the value. Even if MRI signals such as T1 emphasized images are used, they are also presented due to their facilities and types of measurement machines. Diversity, not necessarily. Therefore, in the case of MRI, it is not possible to define "density" such as in XP or CT. Therefore, in MRI, relative values are processed from the initial drawing stage and displayed as "intensity" from the beginning. Moreover, the processed signal is also "intensity".

Based on the above, basic information can be obtained. Relative to the above-mentioned basic data, for a new object to be measured, a certain width and range of the waveform and the wave Hz of the above-mentioned basic data are acquired. For example, capture the width, range, and waveform elements of breath-only capture or vascular capture. Regarding the width of the waveform and Hz, waveforms in other functions such as “artifacts” such as noise and other “modality” waveforms that are considered to be tunable are used multiple times. Reproducibility is judged relatively or comprehensively based on statistics. This requires adjustment and experience (mechanical learning is also applicable). The reason is that if the width and range are enlarged, elements of other functions will be added, and if they are too narrow, the elements of the function itself will be omitted, so the range needs to be adjusted. For example, if there are multiple pieces of data, it is easy to limit the range, the width of Hz and the measurement, etc.

[About tuning agreement rate]
In this specification, the tendency of image change will be described as the tuning coincidence rate. For example, the lung field is detected and divided into a plurality of block areas, and the "average density (pixel value x)" of the block areas in each frame image is calculated. Next, calculate the ratio (x ') of the average pixel of the block area in each frame image to the minimum to maximum change width (0% to 100%) of the "average density (pixel value x)". On the other hand, use the ratio (y ') of the ratio (y') to the width (0% to 100%) of the change from the minimum position to the maximum position of the diaphragm in each frame (y) x '/ y'), only capture the block area where the ratio (x '/ y') falls within a predetermined range.

Here, when y '= x' or y = ax (a is the coefficient of the amplitude value or "density" value of the diaphragm), it is completely the same. However, it is not only a meaningful value when they are completely consistent, but a value with a certain width should be retrieved. Therefore, in one aspect of the present invention, a log is used to determine a certain width as follows. That is, if calculated by the ratio (%) of y = x, the tuning is exactly the same as "log y '/ x' = 0". Furthermore, when the range of the tuning coincidence rate is narrower (narrower in the formula), for example, it is set to "log y '/ x' =-0.05 ~ + 0.05" in the range close to 0. If the range of the tuning coincidence rate is wider (wider in terms of numbers), for example, it is set to "log y '/ x' =-0.5 ~ + 0.5" within a range close to 0. That is, the logarithm of the tunability is set to a certain range including zero. It can be said that the narrower the range and the higher the consistent value in the range, the higher the agreement rate.

If the ratio is calculated according to each pixel of the pixel and the number is counted, in the case of a healthy person, a normal distribution with a peak value when completely consistent can be obtained. In contrast, in the case of a person with a disease, the distribution of the ratio collapses. As described above, the method of determining the width using a logarithm is only an example, and the present invention is not limited thereto. That is, the present invention performs "image acquisition" as (change in "density" in a rough range) ≒ (change in the thorax) ≒ (movement of the diaphragm) ≒ (detection of lung function) ≒ (thoracic and abdominal breathing sensing For organ activities) 肺 (area and volume of the lung field), methods other than logarithmic methods can also be applied. Tunable images can be displayed in this way.

In the case of blood vessels, there is a series of "density" changes (x) (a waveform in the hilar region) produced by a series of systoles (y), and there is a slight time delay (phase change) in the original waveform , So it is expressed as y = a '(xt) (that is, y ≒ x). In the case of complete agreement, since t = 0, y = x or y = a'x. In the case of the transverse diaphragm, when the range of the tuning coincidence rate is narrower (narrower in the formula), for example, it is set to "log y '/ x' =-0.05 in the range close to 0". "~ + 0.05", if the range of tuning coincidence rate is wider (wider in terms of formula), for example, it is set to "log y '/ x' =-0.5 ~ + 0.5" in the range close to 0. It can be said that the narrower the range and the higher the consistent value in the range, the higher the agreement rate.

In the case of other blood vessels, in addition to the above-mentioned "part that echoes the heart", the "density" on the central side of the hilar depiction is used. The situation with peripheral blood vessels can be treated in the same way.

Furthermore, the present invention can also be applied to circulatory organs. For example, the "density" change of the heart is directly related to the "density" change of blood flow to the hilar to the peripheral lung field, and a series of "density" changes of the heart or Changes in the "density" of the hilum are transmitted directly after a transformation. It is believed that there are some phase differences in the relationship between the "density" change from the heart and the "density" change in the hilar. In addition, since the "density" change of the hilum and the like is related to the "density" change of the blood flow directly to the lung field, the tunability can also be expressed by the original ratio (the relationship of the agreement rate of y 之 x). The same applies to the cervical vascular system, or the large vascular system such as the chest, abdomen, pelvis, limbs, etc., and it is considered to be directly related to changes in "density" depicted in nearby central heart blood vessels, or to be associated with slight phase differences. In addition, the "density" changes according to the background, and the change in "density" is transmitted during propagation, so it can be considered as the tuning coincidence rate.

Here, in each of the amount of change in one image and the rate of change in one image, it can be set to "total inspiratory volume / total expiratory volume". Therefore, when a relative value is obtained based on the difference in permeability from the surrounding air, if you want to display the relative value when the amount of change in "density" from the lung field is set to 1, (Standard Differential Signal Density / Intensity: Standard (Differential signal density / intensity), then the following can be described separately for the amount of change and the rate of change: (1) the difference image of each image, and each image set to 1 (usually assumed), (2) The ratio when the total value of inhalation or exhalation, or the absolute value of inhalation and exhalation plus "density (amount of change or rate of change)" added to each difference image is 1, and (3) change Proportion when the total "density" of each breath in multiple shots (when selected 10% several times) is set to 1.

In the case of 3D such as MRI, the total value of "intensity (in the case of MRI)" or "density" (in the case of CT) of the entire inhalation (in this case, it is set to 1), and the "intensity The difference between "" or "density" can be converted into "peak flow volume deta" for inhalation (at rest or hard breathing), and the ratio of "intensity" or "density" to this is worth, Thereby, at least in the calculation of "3D x time" such as MRI or CT, the measured breathing volume and breathing rate in each lung field portion are converted. Similarly, it is also possible to estimate the distribution of the volume of the "capillary phase" in the "flow" of the lung field by converting the volume of the "capillary phase" in the "flow" of the lung field into the distribution of the amount of pulmonary blood flow by inputting one stroke volume value.

That is, (inhalation change amount of each image) × (number of all breaths) ≒ (expiration change amount of each image) × (number of all breaths) ≒ (inhalation at this time) Breathing: volume of natural breathing or hard breathing) ≒ (expiration breath at this time: volume of natural breathing or hard breathing) ≒ (amount of change in inspiration or expiration in the "volume" of natural breathing or hard breathing at this time) ) Is established. In the case where only one sheet is changed by 10% or 20%, the estimated value can be calculated by calculating (the number of sheets) × (the amount of change in time).

The captured change is visualized and drawn as an image. This is the respiratory function analysis and blood vessel analysis described below. Moreover, the rate of change of the rib cage or diaphragm was visualized. At this time, there are also artifacts removed from the results again, and waveforms are acquired from new data or data waveforms that have become the original reference, waveforms of other treatment programs, surrounding, multiple waveforms, and function acquisition. situation. The method of removing artifacts will be described later.

In addition, there may be a case where a person who removes a variation component of the extraction from other than the above-mentioned ones grasps the feature amount. For example, when grasping the activity of the abdominal intestine, it is sought to remove the effects of breathing and blood vessels from the abdomen, and to extract the activity of the abdominal intestine.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1A is a diagram showing a schematic configuration of a diagnosis support system according to this embodiment. The diagnostic support system performs a specific function by causing a computer to execute a diagnostic support program. The basic module 1 includes a respiratory function analysis unit 3, a pulmonary blood flow analysis unit 5, other blood flow analysis units 7, a Fourier analysis unit 9, a waveform analysis unit 10, and a visualization / numericization unit 11. The basic module 1 obtains image data from the database 15 via the input interface 13. In the database 15, images such as DICOM (Digital Imaging and COmmunication in Medicine) are stored. The image signal output from the basic module 1 is displayed on the display 19 through the output interface 17. Next, functions of the basic module of this embodiment will be described.

[Periodic analysis of respiratory elements]
In this embodiment, the cycle of the respiratory element is analyzed based on the following indicators. The "respiratory element" is a concept including all or part of exhalation or inhalation as described above. That is, at least one frequency of the respiratory element is analyzed using at least one of "density" / "intensity" in a certain area in the lung field, the activity of the diaphragm, and the activity of the thorax. The "at least one frequency of the breathing element" is a concept including a case where the frequency spectrum shown by the breathing element is more than one and has a certain frequency bandwidth. Since the lung field is considered as an aggregate of blocks, and a plurality of frequencies are extracted from each block, in this embodiment, these are treated as a frequency group. In addition, as described above, since the basic data has the concepts of "wave form itself" and "wave interval (frequency: Hz)", it can also be treated as a wave form. It is also possible to use a range of "density" / "intensity" of a certain volume measured from X-rays (multiple types of treatment programs such as CT and MRI), and other areas such as spirogram Data obtained by the measurement method or external input information.

In addition, comparing the analysis results of each breath and analyzing the tendency from multiple data can also improve the accuracy of the data.

In addition, the respiratory element may be modified by changing the phase of at least one frequency of the respiratory element or smoothing the waveform of the respiratory element. In this case, activities such as (thoracic and other diaphragmatic activities) ≒ (thoracic activities) ≒ (density) ≒ (precision lung function) ≒ (thoracic sensor) are used to make the waves uniform in phase. In addition, the average "density" of the lung field is tracked, and the final change is used to approximate the square of the wave as the shape of the wave to identify the wave. Here, in the case of "density" of the chest, etc., since the largest change is the "density" of the lungs, there are also cases where the "density" of the lungs is evaluated by evaluating the "density" of the entire screen. In the case of drawing a wave, there is a case of actual movement and a case where a phase shift occurs in a measured value. In this case, there are cases where the phase is corrected by the position of the maximum value of the phase difference, the minimum value, and the overall shape of the wave.

[Waveform Analysis]
The frequency component of the waveform can be calculated from the waveform of the breathing element. Thereby, the above-mentioned "waveform tunable image" is obtained. Specifically, a waveform at an arbitrary position in the analysis range is specified, a component of the frequency of the specified waveform is extracted, and an image corresponding to the component of the frequency of the waveform is output.

[Analysis of Cardiovascular Pulsation and Analysis of Vascular Pulsation]
In the present embodiment, cardiovascular pulsation and vascular pulsation analysis are performed based on the following indicators. That is, from the measurement results of other treatment programs such as electrocardiogram or pulse meter, or the contour of the lungs, the heart / hilar position / main blood vessels are specified, and the pulsation of blood vessels is analyzed using the "density" / "intensity" changes of each part. It is also possible to manually draw on the image and analyze the change in "density" / "intensity" of the target part. In addition, at least one frequency (waveform) of a cardiovascular pulsation element derived from a heartbeat or blood vessel pulsation is specified. In addition, it is desirable to compare the analysis results of each pulsation and analyze the tendency of multiple data to improve the accuracy of the data. In addition, the "density" / "intensity" of each part can be extracted multiple times or performed for a certain range to improve accuracy. In addition, there are methods for inputting the frequency or frequency of cardiovascular beats.

[Lung field identification]
Capture images from the database (DICOM), and use the periodic analysis results of the above respiratory elements to automatically detect the lung contour. As for the automatic detection of the lung contour, a technique known heretofore can be used. For example, the technique disclosed in Japanese Patent Laid-Open No. 63-240832 or Japanese Patent Laid-Open No. 2-250180 can be used. Then, the lung field is divided into a plurality of blocks, and the change of each block is calculated. Here, the size of the block area can be determined according to the shooting speed. When the shooting speed is slow, it is difficult to identify the corresponding part of a frame image below a frame image, so the block area is enlarged. On the other hand, when the shooting speed is fast, since the number of frame images per unit time is large, the tracking can be performed even if the block area is small. In addition, the size of the block area may be calculated according to which timing in the cycle of the respiratory element is selected. Here, it is necessary to correct the displacement of the lung field area. At this time, the activity of the thorax, the activity of the diaphragm, and the positional relationship of all blood vessels in the lung field are identified, and the relative position of the lung contour is grasped and evaluated based on its activity. In addition, when the block area is too small, image flicker may occur. To prevent this flicker, it is necessary for the block area to have a certain size.

At least one Bezier curve can be used in the automatically detected lung field area to display the lung field as coordinates of points and control points. Moreover, the lung field can be displayed by using a plurality of closed curves surrounded by at least one Bezier curve, the so-called "pure closed curve". Similarly, you can use one or more pure closed curves to display the analysis object.

The lung field in each frame can also use at least one Bezier curve on the lung field detected in a specific frame to detect the lung field in other frames. For example, the method of detecting the largest and smallest two lung fields and using the two lung fields to calculate the lung fields of other frames is listed. Here, variables of "rate of change" are defined in other frames. The "change rate" is the value representing the size of the lung field, that is, the value of the breathing state, and can be calculated from the position of the diaphragm or the average value of the "intensity" of the entire image. Measured data from external devices such as a spirograph can also be used to calculate or use a modeled rate of change. In this way, since the variable of the "rate of change" can be arbitrarily determined, for example, it can be calculated assuming that the lung field changes at a certain ratio (10%, 20%, 30%, etc.). Since the rate of change thus defined may include errors, there may also be cases where subsequent processing is performed using the results of automatic / manual removal of errors, or the results of approximation using the least square method, etc. It is assumed that the linear deformation reaches the maximum lung field and the minimum lung field, the change rate of each frame is used, and the method of linear transformation is used to calculate the lung field in each frame.

In addition, the above processing can be applied to any range of continuous frames. For example, in breathing, the lung field repeatedly changes to the maximum and minimum, but in actual measurement, the shape at the maximum is not always fixed. For example, by applying the above processing to the ranges of the maximum to the minimum and the minimum to the maximum, it is expected that the lung field can be calculated with higher accuracy than the two lung fields that define the largest and the smallest. In addition, here, as a specific example, although the maximum and minimum values have been described, the present invention is not limited to this, but because it is an "arbitrary range", it can also be used during breathing, 0% and 30%, 30 % And 100%.

Also, although the accuracy is reduced, the lung field of each frame can be calculated from one lung field. For example, the change vector of the lung field can be specified by analogy from the shape of the thorax and the like. Specifically, a method of specifying a change vector for each control point of the Bezier curve is adopted, but the present invention is not limited to this. And, using the detected one lung field and the change vector and the change rate of each frame, the lung field of each frame is calculated. Correcting the calculation result automatically or manually can further improve the accuracy. The above-mentioned method is effective even in 3D. That is, in the case of 3D, it is also possible to use at least one Bezier surface on the lung field detected in a specific frame to perform processing for detecting lung fields in other frames. With this, an image of the lung field between the frames can be obtained.

FIG. 6C is a graph showing the cycle of respiratory elements. The white vertical line shown in the image of FIG. 6C is a straight line (indicator) indicating the position of the current time point in the breathing element cycle, and the animation of the lungs according to FIG. 6B is used to display the current position in the breathing element cycle. Way activity. By indicating the current position of the respiratory element cycle, the current position in the lung activity cycle can be clearly grasped. In addition, in the present invention, not only the periods of the respiratory elements are displayed in a graph, but also the "density" of the blood flow, the rib cage, the diaphragm, and the like that are linked to the movement of the lungs can be all graphed.

In addition, when the subject "is in a state of stopping breathing", there is a case where the frequency of the breathing element cannot be specified. In this case, a Fourier analysis to be described later is performed using at least one frequency of a cardiovascular pulsation element extracted from the subject's heartbeat or blood vessel pulsation. In this case, the division method of the block area described later may be changed in response to the activity of the heart, the diaphragm, or the active part in association with breathing.

[Edge detection and evaluation]
The present invention can detect the edge of the lung and evaluate the edge. For example, after the lung field is calculated by the method described above, the position and shape of the edge can be detected again with high accuracy. Draw points at any position in the calculated lung field, and then extend the line in all directions, and evaluate the change in pixel value in each line. For example, as shown in FIG. 14, if pixel values are calculated along the line segment S that cuts off the lungs, it can be seen that the edge pixels move greatly, but the absolute values of the activities are different. For example, the accuracy of edge detection is improved by adjusting the thresholds when detecting the left and right edges. In addition, the characteristics of the pixel value activity of each region can also be used. As shown in FIG. 14, even if the difference between the edges of the S2 area and the S3 area is small, the edges of the S2 area and the S3 area can be specified from the variance of the pixel value activity. Although the focus is on the variance here, the present invention is not limited to this.

Furthermore, the same thinking method can also be used to detect the edges of the analysis range of organs, blood vessels, tumors, etc. other than the lungs. For example, when a contrast agent is present in a blood vessel, the inside of the blood vessel can be clearly visualized, but it is not easy to explicitly calculate the outer side or thickness of the blood vessel. In this embodiment, since the edges can be accurately detected, the shape and characteristics of blood vessels falling within the analysis range can be calculated. Thereby, it is possible to quantitatively grasp the thickness or periphery of a blood vessel that was previously difficult to grasp, and to use it for diagnosis.

[Creation of Block Area]
A method of dividing the lung field into a plurality of block regions will be described. FIG. 1B is a diagram showing a method of radially dividing the lung field from the hilum. Since the diaphragmatic side of the lung moves more strongly than the apical side of the lung, the closer to the diaphragmatic side, the rougher the point of division can be drawn. In addition, in FIG. 1B, a vertical line (dashed line) may be additionally drawn and divided into a plurality of rectangular (square) block areas. Thereby, the movement of the lungs can be displayed more accurately. In addition, the following methods can also be used to segment the lung field: "the method of drawing points on the lungs longitudinally and dividing the lungs horizontally", "the method of drawing points on the lungs transversely and dividing the lungs vertically", "drawing the apex of the lungs A method of dividing the tangent line with the tangent line at the diaphragm, and setting the point where these tangent lines intersect as a center point, and drawing a line segment at a certain angle from a straight line (such as a vertical line) containing the point, and dividing the lung by the line segment ""A method of cutting the lung with a plurality of planes orthogonal to a straight line connecting the end of the diaphragm from the apex (or hilum) of the lung" and the like. These methods can also be applied to three-dimensional stereo images. In the case of 3D, each organ is captured in a space surrounded by a plurality of curved surfaces or planes. Organs can be further subdivided. For example, when considering a 3D model of the right lung, it can be divided into upper lobe, middle lobe, and lower lobe for processing.

The area of the lung field is to identify the activities of the thorax, the diaphragm, and the positional relationship of all blood vessels in the lung field, to grasp the relative position of the lung contour, and to evaluate the relative position based on these activities. Therefore, in the present invention, after the lung contour is automatically detected, the area specified by the lung contour is divided into a plurality of block regions, and the change values (pixel values) of the images contained in each block region are averaged. For example, as shown in FIG. 10, points can be drawn on the Bezier curve on the edge of the opposing lung, and the points are connected, and then a curve passing through the intermediate point is used. As a result, as shown in FIG. 1C, even if the shape of the lung changes with time, the change over time in the region of interest can be tracked. On the other hand, FIG. 1D is a diagram showing changes over time when segmenting into a block region without considering the shape of the organ (the lungs) to be analyzed. As mentioned above, the so-called lung field area should identify the activities of the thorax, the diaphragm, and the positional relationship of all blood vessels in the lung field, grasp the relative position of the lung contour, and evaluate the relative position of these activities based on these activities. As shown, if the lung field area is not specified and divided into block areas, the area of interest deviates from the lung field area due to changes in the lungs over time, and becomes a meaningless image. In particular, since the activity of the diaphragm is a strong action of contracting the lung field, it is better to incorporate the thoracic component or other multiple elements to modify the lung field area rather than only the diaphragm or the whole value. There is also a method for inputting the frequency or frequency band of the respiratory element. 3D can similarly perform region division calculations.

Furthermore, as shown in FIG. 11, in the lung field A, a Bezier curve can also be used to select internal control points in the detected lung field and be divided by a curve or a straight line passing through the internal control points in the lung field. Lung field. That is, control points are set not only on the border of the lung field, but also inside the lung field region, and the control points are used to divide the lung field region (A). In this case, as shown in FIG. 12, the interval (1) of the detected extension of the lung field and its nearby control points can be relatively enlarged, and it can be relatively decreased according to the expansion ratio of each part in the detected lung field. Interval of small internal control points (2). In addition, in the lung field A, the interval between the control points may be relatively enlarged as it enters the head and tail with respect to the human body, or may be relatively enlarged according to a specific vector direction. The method of determining this vector is arbitrary, but it may be determined, for example, from the apex of the lung to the opposite side of the lung field, or as shown in FIG. 1B, from the hilum to the opposite side of the lung field. The vector may be determined in a direction corresponding to the structure of the lung. In this way, the segmentation method of the lung field is set to "unequal segmentation", whereby an image that takes into account the characteristics of each region can be displayed. For example, the area of the lung field is larger and the offset increases, so the block is enlarged. On the other hand, the area of the lung field is smaller and the offset is smaller, so the refinement block is reduced. And, for example, the area on the diaphragm side of the lung field has a large movement and a large offset, so the block is enlarged. On the other hand, the movement on the head side of the lung field is small, and the offset is small, so the refinement area is reduced. Piece. This can improve the accuracy of the display. This method is not limited to the lung field, and can also be applied to active parts that are linked to breathing. This method can also be applied to the case where the lung is divided into three dimensions according to the lung lobe. It can also be used to surround and display the lower part of the diaphragm, such as the heart or other organs, with a Bezier curve. In this case, the vector can also be determined in a direction corresponding to the structure of the heart or other organs, instead of waiting to divide the region.

Next, the artifacts are eliminated and the image data is interpolated. That is, if bones or the like are included in the analysis range, noise is displayed. Therefore, it is desirable to remove noise using a noise cutoff filter. In the X-ray image, generally, the air is set to -1000, and the bones are set to 1000, so the pixel value of the part with higher transmittance is lower, and it is displayed as black, and the pixel value of the part with lower transmittance is higher. And displayed in white. For example, when displaying pixel values in 256 gray levels, black is 0 and white is 255. In the lung field area, the X-ray image has a low pixel value and the X-ray image becomes black because the periphery of the location where there is no blood vessel or bone is easy to pass through the X-ray. On the other hand, since it is difficult for a place where a blood vessel or a bone exists to pass through the X-ray, the pixel value of the X-ray image becomes high, and the X-ray image becomes white. It can be said that it is the same in other CT and MRI. Here, from the analysis result of the period of the above-mentioned respiration element, based on the waveform of each respiration, the same phase value is used to interpolate the calculation data to eliminate artifacts. When detecting "different coordinates", "extreme pixel value activity", "abnormally high frequency or density", these are cut off, and the remaining images are identified using, for example, the least square method to identify continuous And smooth waveform, which can be used for Hz calculation of diaphragm and adjustment of lung field. In addition, in the case of overlapping images, there are the following methods: (1) obtaining the comparison image of a single image before and after so that its coordinates are directly overlapped, and (2) after obtaining the single image of the front and back using a base , Expand the image relatively and overlap its relative position information with the datum. By the above method, the shape of the lung field can be corrected, or the image change of the block area can be corrected. At this time, artifacts are removed from the result again, waveforms are acquired from the new data or the waveforms that became the original basic data, the waveforms of other treatment programs, etc., the surrounding and multiple waveforms are acquired, and the function is acquired. At this time, the number of times may be one or plural.

Here, the "reconstruction" of the time axis will be described. For example, when the inhalation time of 15f / s is 2 seconds, 30 + 1 images can be obtained. In this case, as long as only 3 sheets are overlapped each time, "reorganization" per 10% can be implemented. At this time, for example, in the case where only 10 seconds and 10% of the images are taken, and the images are 0.07 seconds and 0.12 seconds, a "reorganization" of 0.1 seconds is required. In this case, "reorganize" the median value (average of the two) of the images before and after 10%. It is also possible to capture on the time axis and change the coefficient in accordance with the time scale. For example, if there is a time axis difference and there is no shooting value of 0.1 second, and there is a shooting time of 0.07 second and 0.12 second, it can be recalculated as ｢(its value of 0.07 second) × 2/5 + (0.12 second value ) × 3/5 ｣to" reorganize ". Furthermore, the change position relationship of the second is identified from the change in the average of self-breathing or the coefficient of the diaphragm, and the value is set as the coefficient to obtain a digital ratio. In addition, it is expected to include 0 to 100% of the "Maximum Differential Intensity Projection", such as "reorganization" of 10% to 20%, or "reorganization" of 10% to 40%, and so on. In this way, it is also possible to perform a "reorganization" of the breathing ratio once for the unphotographed part. In addition, the present invention can be similarly "reorganized" not only for breathing, but also for blood flow, thorax activity, diaphragm, and other series of activities associated with these. It is also possible to "reorganize" by block or pixel. In addition, it is expected to include 0 to 100% of the "maximum differential intensity projection", such as "reorganization" of 10% to 20%, or "reorganization" of 10% to 40%, and so on.

The lung field can be detected by the above method, and the detected lung field can be normalized. That is, the detected lung field is spatially normalized, or it is temporally normalized by reconstruction. Although the size or shape of the lung field varies from body to body, it can be displayed in a certain area by normalizing it.

[Diaphragm and thorax]
If the lung field is identified as described above, it is also possible to grasp the activities of the diaphragm or the thorax. That is, the diaphragm film or thoracic curve on the Xp (2D image) of the diaphragm recognized is calculated as a set of fine coordinates, and the average or local downward change rate or amount of the curve, and The diaphragm is set to a curve and a "curve fitting" is performed to digitize the deformation rate, whereby the position where the function can be evaluated from the image can be given. In addition, the edge curve drawn by the chest other than the diaphragm surface can be similarly calculated as a set of fine coordinates, and the average or the rate of change of the curve is digitized to perform a function evaluation from the image. The above two change rates and changes are evaluated as relative / interconnected movements, and different change rates (parts that move in the same way without moving in the same way, etc.) are digitized and imaged to perform function evaluation of movements.

Here, the "diaphragm and thoracic evaluation method" will be described. First, with respect to the diaphragm, the horizontal and right horizontal lines orthogonal to the axis of the body (the so-called midline) are shown as the axis. Next, the line of the diaphragm is flattened to a baseline. That is, the line of the diaphragm is aligned with a horizontal straight line. Next, the activity of the diaphragm was evaluated. Furthermore, the line of the diaphragm was stretched and flattened, and the activity of the orthogonal curve was evaluated. Next, on the outside of the thorax, activity was evaluated using the line connecting the diaphragmatic thorax angle from the lung apex as the baseline (axis). The thoracic line was flattened to the baseline, that is, the activity was evaluated by aligning the thoracic line with the line of the "apex-costal diaphragm angle". The chest line was stretched and flattened along the baseline to assess the orthogonal motion of the curve. And, the curvature or radius of curvature of the thoracic and diaphragmatic lines was evaluated. Then, the above-mentioned change is calculated as a "change amount", and the change amount is differentiated and evaluated as a "change rate".

6B and 6C are diagrams showing examples of images displayed on a display. In FIG. 6B, the activity of the left lung is displayed as an animation. In the image of FIG. 6B, a white horizontal line is displayed, which is a straight line (indicator) indicating the position of the diaphragm. If an animation is played, it follows the movement of the diaphragm and moves up and down. In this way, an image diagnosis can be performed by a physician by detecting the diaphragm and displaying an index indicating the position of the diaphragm detected, that is, a white horizontal line indicating the location of the diaphragm. In addition, not only a part of the diaphragm, but also the identification of the lung field-diaphragm line, and recognition of all points, can diagnose a region of the diaphragm, such as left and right, medial and lateral, and the entire diaphragm. Similarly, the activities of not only the diaphragm, but also the dynamic parts such as the thorax that are associated with breathing, can be determined by a straight line such as the position of the tangent or a straight line of the thorax identified by the lung field. In this way, assuming edge movement, edges can also be detected by obtaining differences in continuous images. For example, in most cases the tumor is firmer and its surroundings are softer. Therefore, because the tumor is less active and its surroundings are actively active, the edges of the tumor can be detected by obtaining a difference.

Also, in 3D images such as MRI or CT, the surface of the diaphragm can also be captured as a coordinate or three-dimensional curved surface, and the coordinate or surface can be calculated as a fine set of coordinates (edge contour, plane of the diaphragm) And the set of coordinates), the average or the local change rate or amount of the surface downward, and the diaphragm as a curved surface to perform "surface fitting" to digitize the deformation rate, thereby enabling self-image The functional evaluation position is given. In addition, the edge surface drawn on the chest other than the diaphragm surface can be similarly calculated as a fine set of coordinates, and the average or the rate of change of the surface can be digitized, so that the function can be evaluated from the image. The above two change rates and changes are evaluated as relative and interconnected, and different change rates (parts that are not linked in the same way, etc.) are digitized and imaged to perform the function evaluation of the activity.

[Fourier analysis]
The Fourier analysis is performed on the "density" / "intensity" value of each block area or the amount of change thereof based on the cycle of the respiratory element and the vascular pulsation cycle analyzed as described above. FIG. 2A is a graph showing a change in "intensity" of a specific block and a result of Fourier analysis thereof. FIG. 2B is a graph showing a Fourier transform result obtained by removing a frequency component close to a heartbeat, and a “intensity” change of a frequency component close to a heartbeat by inverse Fourier transform. For example, if the "intensity" change of a specific block is Fourier transformed (Fourier analysis), the result shown in FIG. 2A is obtained. Next, if the frequency component close to the heartbeat is extracted from the frequency component shown in FIG. 2A, the result shown on the right side of the paper surface of FIG. 2B is obtained. The "intensity" change after tuning the heartbeat change can be obtained by performing inverse Fourier transform as shown on the left side of the paper surface of FIG. 2B.

As shown in FIG. 9, a specific frequency spectrum may be multiplied by a coefficient and weighted. This method can be used, for example, to achieve waveform tuning. That is, as a frequency selection method when inverse Fourier transform is performed, a plurality of frequencies are selected and multiplied by the ratio, and then inverse Fourier transform is performed. For example, when the frequency spectrum with the highest frequency in the captured frequency band is to be highlighted, the spectrum intensity can be set to 2 times. In this case, the frequency may have no continuity. You can select spectrums that do not exist in order.

In addition, the shape of the left lung (the area may also be the right core when the visceral is inverted) (based on the area of the lung field to extract the shape to the recessed part of the left lung) and the position of the vertebral body and diaphragm are analogized "Density" position. In this case, the ROI of the heart is obtained for "density" acquisition. When performing the acquisition, analogy is performed using regions where the relative spectral values of breathing and blood flow are approximate. In addition, there may be cases where "filtering" is performed in advance using a frequency band (heartbeat 40 to 150 Hz, ≒ 0.67 Hz to 2.5 Hz) and the like generated by cardiovascular pulsation, thereby removing the frequency of breathing or other "artifacts". In addition, since the position of the heart also changes according to the breathing condition, sometimes the position of the heart is changed relatively based on the morphological value of the thorax, so that more accurate acquisition of cardiovascular pulsations or hilar, large Retrieval of blood vessels, etc. Furthermore, similar to the activity of the diaphragm, there is a method of calculating the frequency based on the contour of the heart with regular activity.

Here, when performing inverse Fourier transform on a frequency spectrum including frequency components, the frequency components (breathing frequency, cardiovascular pulse frequency) specified by the "density" of self-breathing or blood flow, and the frequency band of the frequency spectrum (available for use) BPF (band pass filter), or inverse Fourier transform based on any of these elements. In addition, at least one frequency at which the inverse Fourier transform is performed may be selected based on the periodic variation spectrum composition ratio peculiar to the organ. Furthermore, the waveform of a specific organ or a region to be analyzed can be specified based on the composition ratio of a plurality of frequencies obtained after Fourier transformation (to create a waveform-tunable image).

In addition, when performing Fourier transform, an AR (Autoregressive Moving average model) method can be used to enable short-term calculation. In the AR method, there is a method using a Yule-walker equation or a Kalman filter in an autoregressive moving average model. Therefore, the derived Yule-walker estimates, PARCOR can be used. Method and least square method. In this way, near real-time images can be obtained more quickly, or calculation aids or artifact corrections can be performed. With this Fourier analysis, the nature of the image in each block area can be extracted and displayed.

In the Fourier analysis, a method using a "digital filter" can also be adopted. That is, a Fourier transform is performed on the original waveform to obtain parameters of each frequency spectrum, and a "digital filter" that performs an arithmetic process on the original wave is used. In this case, instead of performing inverse Fourier analysis, a digital filter is used.

Here, Fourier transform can be performed on the image change of each block area in each frame image, and the frequency spectrum obtained after the Fourier transform is included in a certain frequency band corresponding to the period of the respiratory element. Spectrum. FIG. 2C is a diagram showing an example of capturing a certain frequency band in the frequency spectrum obtained after Fourier transform. The frequency f of the frequency spectrum of the synthetic wave is between the frequencies f ₁ (breathing component) and f ₂ (blood flow component) that become the synthetic source, and the relationship of ｢1 / f = 1 / f ₁ + 1 / f ₂成立 is established. Therefore, when acquiring spectrum, the following methods can be used.

(1) Capture the part with higher frequency proportion of blood flow
(2) Dividing between the peak of the frequency spectrum corresponding to the respiration / blood flow and the peak of a plurality of synthetic waves in the vicinity to acquire the frequency spectrum.
(3) Divide the peak of the spectrum corresponding to the respiration / blood flow and the trough of the spectrum of multiple synthetic waves in the vicinity to capture the spectrum.

As described above, in the present invention, instead of using a fixed BPF, a frequency spectrum in a certain frequency band including a frequency spectrum corresponding to the period of the respiratory element is captured. Furthermore, in the present invention, it is also possible to capture frequencies other than the respiratory elements obtained from the frame image in the spectrum obtained after the Fourier transform (for example, the "density" / "intensity", A heartbeat or a heartbeat element obtained from a pulsation of a blood vessel), or a frequency spectrum within a certain frequency band of a frequency spectrum (such as a spectrum model) corresponding to a frequency input by an operator from the outside.

Here, if the spectrum component of the synthetic wave is only two components (breathing and blood flow), it is 50% + 50%, and in the case of three components, each is assigned 1/3. Therefore, the spectrum of the synthetic wave can be calculated according to the percentage of the spectrum of the respiratory component, the percentage of the spectrum of the blood flow component, and the composition of the spectrum and its level. Spectrum can be captured at a higher ratio (%). That is, the ratio of the blood flow component / respiratory component to the synthetic wave component is calculated, and the high-spectrum value of the blood flow component / respiratory component is calculated. In addition, in the identification of diaphragms, etc., there are data from the frequency of breathing or heart blood vessels, and only the parts where the Hz (frequency) is relatively constant, that is, the areas with less changes in Hz are captured. Corresponding spectrum or its overlap. In addition, when determining the frequency band of the frequency spectrum, when performing diaphragm identification, etc., the frequency band of the frequency spectrum may be determined in the range where the Hz changes and the surrounding area. Sometimes the components of the waveform are also considered.

In addition, regarding the spectrum when inverse Fourier transform is performed, the following cases can be selected: "Only the modeled frequencies and bands use higher positions (one or more) for acquisition (analogism)", and "based on The actual frequency or frequency band corresponds to the higher frequency or lower frequency (spotism) of the spectral value acquisition. " When the frequency of the heart is A and the frequency of the lung is B, B can be obtained by subtracting A from the entire frequency band. In addition, regarding the frequency spectrum obtained from the Fourier transform, a plurality of positions on the frequency axis may be acquired instead of only one position.

According to the above, it is not limited to the case where the cycle of respiratory elements or the pulsation cycle of blood vessels are completely the same, but it is also possible to capture the spectrum that is best considered together, which can help image diagnosis. In addition, it is known that "breathing" or "heartbeat" is included in a specific frequency band. Therefore, in the case of breathing, use a filter of "0 to 0.5 Hz (0 to 30 breaths per minute)", and in the case of a circulator, use "0.6 to 2.5 Hz (heartbeat / pulse number 36 to 150). Times per minute) "filter, the breathing frequency or the frequency of the circulator can also be specified in this filter in advance. Thereby, a frequency-tunable image can be displayed. The reason lies in the following situations: when the "density" of the heart is changed, the "density" change of the breath (lung) is picked up, or when the "density" of the lung is changed, the "density" of the heart is picked up.

[Visualization and Numericalization]
The results analyzed as described above are visualized and numericalized. During visualization and digitization, in this specification, a "modeled lung" is defined. When the lungs are displayed in a dynamic image, it is difficult to make a relative judgment because of the positional relationship activity. Therefore, the shift of the positional relationship is spatially unified / averaged. For example, when a figure such as a fan shape is applied, the shape of the lungs is displayed in a state where the shape is complete. And, use the concept of reorganization to unify in time. For example, "the condition of 20% of lungs in multiple breaths" can be retrieved and set as "the condition of 20% of lungs in one breath". In this way, the lungs unified in space and time are called "modeled lungs". This makes it easy to make relative judgments when comparing different patients with each other, or comparing the present and past times of a patient.

For example, as a standard uptake, a relative / logarithmic value may be expressed by setting the average value to 1 based on the measured "density" / "intensity" of the entire lung field. In addition, since only the direction of blood flow is used, there are cases where changes are cut out in a specific direction. In this way, only the information of meaningful methods can be retrieved. The results of lung field identification were used to track changes in the analysis range for pseudo-colorization. That is, the analysis result of the individual (subject) is applied to the relative area along the specific shape (minimum, maximum, average, median value) that matches the phase.

In addition, a plurality of analysis results are transformed into specific shapes and phases that are comparable. Furthermore, when a modeled lung is created, the relative positional relationship in the lung field is calculated using the results of the periodic analysis of the above-mentioned breathing elements. In addition, the modeled lungs were created by averaging the thoracic contour, "density", and diaphragm of multiple patients. When creating a modeled lung, in the case of pulmonary blood flow, the distance can be measured radially from the hilum to the end of the lung. In addition, when breathing, it must be corrected based on the activities of the thorax or diaphragm. Furthermore, the distance from the apex of the lungs can be considered in a compound calculation.

In addition, after inverse Fourier transform, only blocks with relatively large amplitude values can be captured and displayed. That is, in the case of Fourier analysis for each block, after the inverse Fourier transform, there are a block with a larger amplitude of the wave and a block with a smaller amplitude of the wave. Therefore, it is also effective to capture and visualize only the relatively large amplitude blocks. After the inverse Fourier transform, the real and imaginary parts of each value can be used separately. For example, the image may be reconstructed from only the real part, or the image may be reconstructed from only the imaginary part, or the image may be reconstructed from the absolute value of the real part and the imaginary part.

Fourier analysis can also be performed on modeled lungs. That is, when comparing images of several breaths, or Fourier analysis or grasping relative positions, a modeled lung can also be used. By using the modeled lung, the obtained multiple frames can be applied to the modeled lung. In the case of blood vessels, it can be calculated by applying to the corresponding heartbeat (for example, the heartbeat obtained from the hilum). The lungs are modeled, and the relative position during Fourier analysis is fixed. When obtaining a baseline breathing state, a stable calculation result can be obtained by using a modeled lung. In addition, by modeling the lungs and fixing the differences in space, it is easy to observe the movement of the lungs.

In the visualization, the relative evaluation method is as follows. That is, the images are relatively marked in black and white and color maps. In some cases, the value of the "density" / "intensity" obtained by the difference is cut off by a few%, and the upper and lower margins are displayed relatively. Or, there may be a case where the value of the obtained difference is about a few percent before and after the difference, so sometimes it is removed as an "artifact" and the rest is displayed relatively. In addition to 0 to 255 gray scales, there are also cases where the value is displayed as 0 to 100%.

In addition, the pixels may be displayed in a fuzzy manner to some extent, and the whole may be displayed in a blurred state. In particular, in the case of pulmonary blood vessels, low signal values are mixed between high signal values, but as long as the high signal values can be grasped roughly, even if the whole is blurred. For example, in the case of blood flow, a signal above the threshold may be extracted, and in the case of breathing, a signal above the threshold may not be extracted. Specifically, in the case of using the numbers in the table below as 1 pixel to obtain a positive intermediate value, if the proportion of the positive intermediate value is obtained and averaged within 1 pixel, it can be smoothed between adjacent pixels. To display. This method can also be used when calculating the average intensity of each block.
[Table 1]

This method can be used not only in the lung field, but also in detecting density in any analytical range and removing relatively large changes in density. In addition, a point where the threshold value is significantly exceeded is cut off. In addition, rib morphology identification, such as identifying the sudden appearance of high / low signal lines and removing them. Also, similarly, there may be a case where a signal that suddenly appears from the phase is removed, for example, a sudden signal that is different from a normal wave change, such as a patient characteristic that is considered to be an artifact, where the reconstructed phase is at about 15% to 20%, may be removed . In addition, when the basic information was first obtained, there were calculation phases such as (transverse diaphragm) ≒ (thoracic) ≒ (thoracic activity) ≒ (spirometer) ≒ (lung field), field density (density) ≒ (volume), etc. In different cases, there are cases where the phase is applied to an identifiable form (the outline of XP).

If a modeled lung can be created, as described above, the tunability, coincidence rate, and disagreement rate can be numerically presented (displaying a frequency-tunable image or a wavelength-tunable image). This allows the display to deviate from the normal state. According to this embodiment, a new lesion may be found by performing Fourier analysis, a self-comparison with a normal state, a comparison of hands and feet, or a comparison of hands and feet on the opposite side may be realized. In addition, suspicious features such as foot movement and swallowing can be grasped based on the digitization of tunability. In addition, it can be judged whether the person in the state of illness has changed after a certain period of time, or when there is a change, the situation before and after the change is compared. In addition, by setting the lung field to a fixed distance from the periphery and making it easy to observe radially (circular to quasi-circular), it is easy to evaluate the inner layer to the middle layer and the outer layer. "Peripheral dominance" or "mid-level dominance" manifests itself.

In addition, during visualization, the image after the Fourier transform and the image before the Fourier transform can be switched or displayed, or both can be displayed side by side on the same screen.

As shown in FIG. 2D, when the modeled lung is set to 100, it is possible to grasp the percentage difference in the human body and display the rate of change. In addition, the difference is not limited to the entire lung, even if it is a part of the lung. In particular, as shown above, only the activities of the diaphragm can be specified, while the shape of the lung field other than the diaphragm can be fixed, the activity of the diaphragm can be displayed, and the coincidence rate or rate of change can be displayed. Furthermore, the entirety of the lung field may be fixed and the tuning coincidence rate or the change rate may be displayed. It is also possible to specify a standard blood flow by performing "variation classification". That is, the cycle of the respiratory element can be specified, the relative positional relationship of the blood vessels can be calculated, and the blood flow dynamics of the subject can be specified as the standard blood flow.

In addition, a pattern matching method can be used to detect the lungs. 2E to 2H are diagrams showing an example of a pattern image of a lung field region. As shown in FIG. 2E to FIG. 2H, the shapes of the lungs can be classified into patterns, and the closest ones of these can be captured. According to this method, it is possible to specify whether the image of the object represents a single lung or a double lung. It is also possible to specify whether it is a right lung or a left lung. The number of patterns is not limited, but it is assumed that there are 4 to 5 patterns. In addition, there are methods for identifying the right lung, the left lung, and the two lungs based only on the shape (shape) of the lung field. Furthermore, it is also possible to use a "thickness-reducing site" with a thick band shape that identifies the vertebral body / mediastinum, based on the positional relationship with the band-like reduced-permeability site, and the "super-permeability site" with the lung field Positional relationship, and the method of identifying left and right or both lungs. As shown in FIG. 2H, this method can also be applied to the area below the diaphragm. Thereby, the lower part of the diaphragm or the heart can be identified.

Furthermore, since air has the highest permeability, and the permeability is higher than that of the lung field, it is desirable to calculate it by considering air as well. That is, the following judgments can be made based on the air position on the screen.
In the case of (air area at the upper right of the screen)> (air area at the upper left of the screen), the left lung is recognized. This is because the air area outside the human body around the shoulders is widened during shooting.
In the case of (air area at the upper left of the screen)> (air area at the upper right of the screen), it is identified as the right lung. This is the same as above because the air area outside the human body around the shoulder is widened during shooting.
Then, in the case of (air area at the upper right of the screen) ≒ (air area at the upper left of the screen), it is recognized as two lungs. This is because the air area is about the same from left to right.

In addition, intestinal air may enter under the diaphragm, and it may not be recognized at this time. Therefore, from the center of the lung field to areas such as the mediastinum side, the heart side, and the diaphragm side, etc., the rough lung field and the surrounding areas with reduced permeability can be initially identified, and the depth of the lung field can be identified on the line. This method can also use, for example, the techniques disclosed below.
｢Https://jp.mathworks.com/help/images/examples/block-processing-large-images_ja_JP.html｣

In this way, comparison or digitization of a patient with other patients can be achieved. In addition, comparison or quantification of normal lungs or blood vessels with typical abnormal lung functions or blood vessels can be achieved. Furthermore, as a relative assessment of lung function or pulmonary blood flow in a patient at different times, modeled lungs and standard blood flow can be used. Such modeled lungs and standard blood flow can be used as indicators for collecting and setting modeled lungs and standard blood flow for typical examples of various types of typical patients and healthy people and morphologically applied to a patient for evaluation. .

[Description of the lung field]
In general, because the lung field contains ribs with low permeability, it is difficult to mechanically identify the contour of the lung using only "density" as an indicator. Therefore, in this specification, a method of temporarily describing the contours of the lung field using a combination of Bezier curves and straight lines is adopted, and the lung contour is adjusted in a manner that improves consistency.

For example, if the contour of the left lung is represented by 4 Bezier curves and 1 straight line, the lung contour can be drawn by obtaining 5 points and 4 control points on the lung contour. The positions of the points are shifted, and a plurality of lung contours are drawn, and the conditions such as "the total value of the" density "in the contour is the largest", "the difference between the" density "of the pixels inside and outside the contour is the largest" are consistent with the evaluation This makes it possible to detect the lung contour with high accuracy. In fact, the position of several points can be identified based on the contour of the upper part of the lung that is relatively easy to detect at the edges, or the position of the diaphragm that is detected by the method described later, and the trial can be suppressed. The number of simulations mentioned above. The position of the control point of the Bezier curve can also be adjusted by using the traditional binarization contour extraction to capture points near the outer edge, and by using the least square method.

3A and 3B are diagrams showing an example in which the contour of the lung field is drawn using both a Bezier curve and a straight line. FIG. 3A shows the case where the area of the lung is the largest (maximum outline), and FIG. 3B shows the case where the area of the lung is the smallest (minimum outline). In each figure, ｢cp1 ~ cp5｣ represent control points, and ｢p1 ~ p5｣ represent points on the Bezier curve or a straight line. In this way, if the maximum contour and the minimum contour can be grasped, the halfway contour can be obtained by calculation. For example, the status of 10%, 20%, etc. of exhalation can be displayed. Thus, according to this embodiment, at least one Bezier curve can be used to draw at least the lung field, blood vessels, or the heart. In addition, the above method is not limited to the lungs, but can also be applied to other organs as a detection organ for organs. In addition, for example, at least one Bezier curve may be used on a predetermined analysis range (tumor, hypothalamus, basal ganglia, boundary of endosomes, etc.) in a specific frame, Perform the process of detecting the range corresponding to the analysis range in other frames.

Moreover, it is not limited to a planar image, but it can also be applied to a stereo image (3D image). You can define the curve equation, set its control points, and set the range surrounded by multiple surfaces as the object of analysis.

[Detection of diaphragmatic activity or dynamic parts associated with breathing]
In the continuously captured images, it is possible to detect the movement of the diaphragm or the dynamic part linked to breathing. In the images captured continuously, if the images are selected at arbitrary intervals and the difference between the images is calculated, especially the difference in the area with a large contrast is enlarged. Areas with activity can be detected by appropriately visualizing the difference. In the visualization, the continuity of the area where the absolute value of the difference is emphasized can be eliminated by removing the noise of the threshold value or using the curve fitting of the least square method.

In the lung field, the contrast between the line connecting the diaphragm and the heart is more obvious, as shown in Figure 4A. If a difference is taken in two lung images, a certain threshold is set and the difference is visualized, as shown in Figure 4B To visualize the line connecting the diaphragm or the heart.

[Presumed activity of diaphragm]
In this method, although the position of the diaphragm can be detected when the diaphragm is moving between the target images, it is difficult to detect the part where the diaphragm's movement is relatively gentle. That is, it is difficult to detect when the timing of exhalation and inhalation is switched, or when the breath is stopped, after the start of shooting or before the end of shooting. In this method, an arbitrary interpolation method is used to estimate the activity of the diaphragm.

Using the above method, as shown in FIG. 4B, after visualizing the diaphragm line, the 1024px vertical image is divided into 128 rectangles by 8px vertical, and the signal values contained in each rectangular area are totaled, as shown in FIG. 4C. To form a bar chart. It is expected that the position of the diaphragm is represented by the peak at the lowermost coordinate of the plurality of peaks shown by a dotted rectangle. In the normal standing XP image, the diaphragm is shown as a curve, but the coordinates are approximated to the location of the diaphragm.

If the diaphragm position is detected for all images in this method, as shown in FIG. 5, the “peak position” is detected. By correcting the detected value, the activity of the diaphragm is estimated. First, when the difference is larger than a certain value, it is regarded as a deviation value and excluded (the thin solid line in FIG. 5). The data after the deviation value is excluded is divided into arbitrary clusters, and the curve regression is performed 4 times for each cluster, and the results are connected (the thick solid line in Fig. 5). Although regression analysis is performed in this analysis, the present invention is not limited to this, and arbitrary interpolation methods such as spline interpolation can also be used.

[Refinement of dynamic part detection]
The contrast of dynamic parts along the line may be different. In this case, the shape of the dynamic part can be detected more accurately by changing the threshold used for noise removal and performing multiple detection processes. For example, in the left lung, the contrast of the diaphragmatic line tends to weaken as it enters the body. In FIG. 4B, only the right half of the diaphragm can be detected. At this time, the remaining part of the left half of the diaphragm can be detected by changing the setting of the threshold for noise removal. By repeating this process several times, the shape of the entire diaphragm can be detected. According to this method, instead of digitizing only the position of the diaphragm, it is possible to digitize the rate of change or the amount of change of a line or a surface with respect to the shape, which can be useful for new diagnosis.

In this way, the detected position or shape of the diaphragm can be used for diagnosis. That is, in the present invention, the coordinates of the diaphragm can be graphed, and the coordinates of the thorax or diaphragm can be calculated using the curve (situation) calculated as described above, or the heartbeat, blood vessel pulsation, and lung field can be calculated. The "density" and the like are graphed as positions and coordinates corresponding to the period. This method can also be applied to dynamic parts linked to breathing.

According to this method, it is not limited to the Hz of inhalation and exhalation. When the frequency (Hz) of the diaphragm or the dynamic part linked to breathing changes, it can be measured in the frequency band corresponding to the change. In addition, when capturing the spectrum of a BPF (band pass filter), a variable BPF can be made by combining the following conditions: within a certain range, the position axis of the BPF is set according to each state of the breathing at each "recombination phase" of the breathing Activities and produce the best. Thereby, even if the breathing rhythm is stopped or stopped (Hz = 0), the breathing rhythm changes, and an image corresponding thereto can be provided.

In addition, the frequency of the entire exhalation or inhalation may be calculated based on the proportion of the respiratory element to the entire exhalation or inhalation. In addition, in the detection of diaphragm, it can be performed multiple times, and the one with stable signal or waveform can be selected. Based on the above, at least one frequency of the respiratory element can be calculated based on the detected position or shape of the diaphragm, or the position or shape of a dynamic site associated with breathing. If the position or shape of the diaphragm or dynamic part can be grasped, then the frequency of the respiratory elements can be grasped. According to this method, even if a part of the waveform is cut out, subsequent waveforms can be tracked. Therefore, even if the frequency of the respiratory element changes halfway, the original respiratory element can be traced. In addition, although there may be sudden changes such as the beating of the heart, the same can be applied to cardiovascular disease. Next, the operation of each module in this embodiment will be described.

[Analysis of respiratory function]
First, the respiratory function analysis will be described. FIG. 6A is a flowchart showing an outline of a respiratory function analysis according to this embodiment. The basic module 1 captures a DICOM image from the database 15 (step S1). Here, at least one frame image included in the breathing cycle is obtained. Next, in each frame image obtained, at least a certain area in the lung field is used for density (density / intensity) to specify the period of the respiratory element (step S2). In addition, a specific breathing cycle or a waveform specified from the breathing cycle can be used in the following steps.

The specificity of the cycle of the respiratory elements can be further used for the activities of the diaphragm and the thorax. It is also possible to use data obtained by other measurement methods, such as a certain volume, a range consisting of "density" / "strength", and a spirometry chart, which are measured at a location where X-rays have high permeability. In addition, the frequency of each organ (here, the lung) can be specified in advance, and the "density" / "intensity" corresponding to the specified frequency can be extracted.

Next, in FIG. 6A, the lung field is automatically detected (step S3). Because the lung contour changes continuously, as long as the largest and smallest shapes can be detected, the shape between them can be interpolated by calculation. Based on the period of the respiratory elements specified in step S2, each frame image is interpolated to identify the lung contour in each frame image. In addition, pattern matching as shown in FIGS. 2E to 2H can be performed to detect the lung field. In addition, noise can be removed from the detected lung field by cutting. Next, the detected lung field is divided into a plurality of block regions (step S4). Next, the change of each block area in each frame image is calculated (step S5). Here, the change values in each block area are averaged and expressed as one piece of data.

In addition, noise can be removed from the change value in each block area by cutting. Next, the "density" / "intensity" value of each block area and the amount of change thereof are subjected to a Fourier analysis or an analysis of the tuning coincidence rate based on the period of the breathing element (step S6).

Next, noise removal is performed on a result obtained by Fourier analysis or analysis of tuning coincidence rate (step S7). Here, the truncation as described above, or the removal of artifacts can be performed. Perform the above steps S5 to S7 more than once, and determine whether it is completed (step S8). Here, with regard to the characteristic amount displayed on the display, due to the mixing of synthetic waves or other waves, there is a spectral acquisition that cannot display high-purity elements, such as the frequency-tunable images of respiratory elements, blood flow elements, and other elements. situation. At this time, the feature value displayed on the display may be a pixel value, and the whole or a part of the person displayed on the display may be re-analyzed several times. By this operation, it is possible to obtain a high-purity image related to the tunability or consistency of the respiratory element or the blood flow element. With regard to this operation, the operator can manually recognize the image on the display while viewing it, or can automatically extract the spectrum from the output result and recalculate the distribution ratio. Furthermore, after the calculation, the buried holes (interpolation) using the noise reduction process, the least square method, and the surrounding "density" can be corrected according to the situation.

In step S8, if it is not completed, move to step S5. When it is completed, the result obtained by Fourier analysis or tuning coincidence rate analysis is displayed on the display as a pseudo-color image (step S9). Black and white images can also be displayed. Thus, there are cases where the accuracy of the data is improved by repeating a plurality of cycles. Thereby, a desired animation can be displayed. Moreover, a desired animation can be obtained by correcting the image displayed on the display.

In this embodiment, a desired frequency or frequency band is calculated by calculation, but if it is observed as an actual image, a better image may not necessarily be displayed. Therefore, there are cases where the following method is adopted.
(1) The method of prompting several frequency bands for people to choose
(2) A method of prompting several frequency bands multiple times and using AI technology to identify better images by pattern recognition
(3) It is selected based on the tendency and form of HISTGRAM. That is, the value of the "Histgram" central part in the result signal tends to increase, and because the value of "histgram" changes depending on the activity, it can also be selected based on the tendency and form of HISTGRAM.

[Pulmonary blood flow analysis]
Next, lung blood flow analysis will be described. FIG. 7 is a flowchart showing an outline of pulmonary blood flow analysis in the present embodiment. The basic module 1 captures a DICOM image from the database 15 (step T1). Here, a plurality of frame images included in at least one heartbeat cycle are obtained. Next, based on the acquired frame images, a blood vessel pulsation cycle is specified (step T2). The specific pulsation cycle or the waveform specified from the pulsation cycle can be used in the following steps. The pulsation cycle is as described above, and the vascular pulsation is analyzed using measurement results of other treatment programs such as an electrocardiogram or a pulsometer, and changes in "density" / "strength" of any part of the heart, hilum, and main blood vessels. In addition, the frequency of each organ (here, pulmonary blood flow) can be specified in advance, and the "density" / "intensity" corresponding to the specified frequency can be extracted.

Next, in FIG. 7, the cycle of the respiratory element is specified by the method described above (step T3), and the lung field is automatically detected using the cycle of the respiratory element (step T4). When automatically detecting the contour of the lung, there may be a difference in each frame image, but based on the cycle of the respiratory elements specified in step T3, each frame image is interpolated to identify each frame. Lung outline in the image. In addition, pattern matching as shown in FIGS. 2E to 2H can be performed to detect the lung field. In addition, noise can be removed from the detected lung field by cutting. Next, the detected lung field is divided into a plurality of block regions (step T5). Next, the change of each block area in each frame image is calculated (step T6). Here, the values of changes in each block area are averaged and represented as one piece of data. In addition, noise can be removed by cutting the value of each block area. Next, the value of "density" / "intensity" of each block area and the amount of change thereof are subjected to Fourier analysis or analysis of tuning coincidence rate based on the vascular pulsation cycle described above (step T7).

Next, noise removal is performed on the result obtained by Fourier analysis or analysis of tuning coincidence rate (step T8). Here, the truncation as described above, or the removal of artifacts can be performed. Perform the above steps T6 to T8 more than once, and determine whether it is completed (step T9). Here, as for the characteristic amount displayed on the display, there is a frequency spectrum tuned image that cannot display high-purity elements, such as the respiratory element, blood flow element, and other elements, due to the mixing of synthetic waves or other waves. Situation. At this time, the feature value displayed on the display may be used as the pixel value, and the whole or a part of the person displayed on the display may be re-analyzed several times. By this operation, it is possible to obtain a high-purity image related to the tunability or consistency of the respiratory element or the blood flow element. With regard to this operation, the operator can manually recognize the image on the display while viewing it, or can automatically extract the spectrum from the output result and recalculate the distribution ratio. In addition, after calculation, the buried holes (interpolation) using the noise reduction process, the least square method, and the surrounding "density" can be corrected according to the situation.

In step T9, when it is not completed, move to step T6, and when it is completed, the result obtained by Fourier analysis or tuning coincidence rate analysis is displayed on the display as a pseudo-color image (step T10). Black and white images can also be displayed. In this way, the accuracy of the data can be improved. Moreover, a desired animation can be obtained by correcting the image displayed on the display.

In this embodiment, a desired frequency or frequency band is calculated by calculation, but if it is observed as an actual image, a better image may not necessarily be displayed. Therefore, there are cases where the following method is adopted.
(1) The method of prompting several frequency bands for people to choose
(2) A method of prompting several frequency bands multiple times and using AI technology to identify better images by pattern recognition
(3) It is selected based on the tendency and form of HISTGRAM. That is, the value of "Histgram" at the center of the result signal tends to increase, and the value of "histgram" varies depending on the activity, so it can be selected based on the tendency and form of HISTGRAM.

[Other blood flow analysis]
Next, other blood flow analysis will be described. An aspect of the present invention is shown in FIG. 15 and can also be applied to blood flow analysis of the heart, aorta, pulmonary blood vessels, upper limb arteries, and cervical blood vessels. In addition, blood flow analysis can be performed similarly for abdominal blood vessels, peripheral blood vessels, and the like not shown. FIG. 8 is a flowchart showing the outline of another blood flow analysis according to this embodiment. The basic module 1 captures a DICOM image from the database 15 (step R1). Here, a plurality of frame images included in at least one heartbeat cycle are obtained. Next, based on the acquired frame images, a blood vessel pulsation cycle is specified (step R2). The specific pulsation cycle or the waveform specified from the pulsation cycle can be used in the following steps. As described above, the pulsation cycle of blood vessels is analyzed using measurement results of other treatment programs such as an electrocardiogram or a pulsometer, and changes in "density" / "intensity" of any part of the heart, hilum, and main blood vessels. In addition, the frequency of each organ (for example, the main blood vessel) can be specified in advance, and the "density" / "intensity" corresponding to the specified frequency can be extracted.

Next, the analysis range is set (step R3), and the set analysis range is divided into a plurality of block areas (step R4). Next, the change values in each block area are averaged and represented as one piece of data. In addition, noise can be removed by cutting the value of each block area. Next, the "density" / "intensity" value of each block area and the change amount thereof are subjected to a Fourier analysis or an analysis of the tuning coincidence rate based on the above-mentioned pulsation cycle of the blood vessel (step R5).

Next, noise removal is performed on a result obtained by Fourier analysis or analysis of tuning coincidence rate (step R6). Here, the truncation as described above, or the removal of artifacts can be performed. Perform the above steps R5 to R6 more than once, and determine whether it is completed (step R7). Here, as for the characteristic amount displayed on the display, there is a frequency spectrum tuned image that cannot display high-purity elements, such as the respiratory element, blood flow element, and other elements, due to the mixing of synthetic waves or other waves. Situation. At this time, the feature value displayed on the display may be used as the pixel value, and the whole or a part of the person displayed on the display may be re-analyzed several times. By this operation, it is possible to obtain a high-purity image related to the tunability or consistency of the respiratory element or the blood flow element. With regard to this operation, the operator can manually recognize the image on the display while viewing it, or can automatically extract the spectrum from the output result and recalculate the distribution ratio. Furthermore, after the calculation, the buried holes (interpolation) using the noise reduction process, the least square method, and the surrounding "density" can be corrected according to the situation.

In step R7, when it is not completed, move to step R5, and when completed, the result obtained by Fourier analysis or tuning coincidence rate analysis is displayed on the display as a pseudo-color image (step R8). Black and white images can also be displayed. In this way, the accuracy of the data can be improved. Moreover, a desired animation can be obtained by correcting the image displayed on the display.

In this embodiment, a desired frequency or frequency band is calculated by calculation, but if it is observed as an actual image, a better image may not necessarily be displayed. Therefore, there are cases where the following method is adopted.
(1) The method of prompting several frequency bands for people to choose
(2) A method of prompting several frequency bands multiple times and using AI technology to identify better images by pattern recognition
(3) It is selected based on the tendency and form of HISTGRAM. That is, the value of "Histgram" at the center of the result signal tends to increase, and the value of "histgram" varies depending on the activity, so it can be selected based on the tendency and form of HISTGRAM.

In addition, in the case of 3D analysis, the breathing volume, stroke volume, and central blood flow can be measured by other devices, and the relative volume, which is the result of Fourier analysis, is used to calculate the breathing volume and stroke volume of each block area. 3. Central blood flow. That is, in the case of respiratory function analysis, the lung ventilation volume can be estimated from the breathing volume, and in the case of pulmonary blood flow analysis, the pulmonary blood flow can be estimated from the cardiac (pulmonary blood vessel) stroke volume. In this case, the estimated blood flow rate (ratio) in the branch blood vessel drawn from the central side blood flow rate (ratio) can be estimated.

In addition, as described above, if all the obtained databases can be calculated, the judgment can be performed with higher accuracy, but it may take time even if the computer analysis is performed. Therefore, only an arbitrary number of sheets (for example, a specific phase) can be extracted for calculation. Thereby, the analysis time can be shortened, and furthermore, an irregular portion observed at the beginning of breathing can be cut out. When displaying the analysis result, an arbitrary range can be displayed. For example, when the playback is repeated by displaying the range of the "expiration / inhalation" transition point to the "inhalation / expiration" transition point, so-called "continuous playback" can be realized, which can be easily diagnosed by a physician.

As described above, according to this embodiment, an X-ray animation device can evaluate an image of a human body. If digital data is available, existing facilities can be calculated roughly, so the introduction cost is low. For example, in an X-ray animation device using a flat panel detector, the inspection of a subject can be easily performed. Moreover, regarding pulmonary blood flow, screening for pulmonary thromboembolism can also be performed. For example, in an X-ray animation device using a flat panel detector, a diagnostic support program of this embodiment is executed before CT, thereby eliminating useless inspections. In addition, since the examination is relatively simple, diseases with higher urgency can be detected early, and the response can be given priority. In addition, in the current method of photography, there are still some problems in other treatment programs such as CT and MRI, but as long as this can be solved, detailed diagnosis of each area can be achieved.

In addition, it can also be used for screening of various blood vessels such as neck blood flow, and it can also be used for large blood vessel evaluation or screening. Moreover, the pulmonary breathing data is effective as a partial function test of the lung, and can be used as a pulmonary function test. In addition, diseases such as COPD (Chronic Obstructive Pulmonary Disease) and emphysema can be identified. Furthermore, it can also be applied to shape control before and after surgery. Furthermore, the Fourier analysis of the cycle of the respiratory elements and the blood flow cycle can be performed, and the waveform of the breath and the blood flow can be removed from the X-ray image of the abdomen, thereby observing the variation of the movement of the remaining organism, such as intestinal obstruction Wait.

In addition, when the image obtained first is high-resolution to some extent, it may take time in calculation time because the number of pixels is large. In this case, the image can be calculated after reducing it to a certain number of pixels. For example, "4096 x 4096" pixels are actually calculated as "1024 x 1024" to suppress the calculation time.

[other]
When capturing X-ray images, a prediction algorithm such as an AR method (Autoregressive Moving average model) can be used. When at least one frequency of the respiratory element can be specified, the X-ray irradiation interval can be adjusted in accordance with the frequency to control the X-ray photographing device. For example, when the frequency of the respiratory element is small (when the period is longer), the number of X-ray photography can be reduced. This can reduce the amount of human exposure. In addition, when the frequency of respiratory elements such as frequent respiration or frequency pulses or cardiovascular elements is large (cases with shorter periods), the frequency of irradiation can be increased for optimal image creation.

In addition, although it is a storage form of DICOM data, there is a case where the image quality is degraded if compressed, so it is desirable to save it without compression. Also, the calculation method can be changed according to the compressed form of the data.

1‧‧‧ Basic Module

3‧‧‧Respiratory function analysis department

5‧‧‧Pulmonary blood flow analysis department

7‧‧‧ Other blood flow analysis department

9‧‧‧Fourier analysis department

10‧‧‧Waveform Analysis Department

11‧‧‧Visualization and Numerical Department

13‧‧‧Input interface

15‧‧‧Database

17‧‧‧ output interface

19‧‧‧ Display

(1) ‧‧‧Control point interval

(2) ‧‧‧Interval between control points

A‧‧‧ Lung Field

cp1 ～ cp4‧‧‧control points

p1 ～ p5‧‧‧‧points

R1 ～ R8‧‧‧‧steps

S1 ～ S9‧‧‧‧ steps

S‧‧‧Segment

S1‧‧‧ area

S2‧‧‧ area

S3‧‧‧ area

T1 ～ T10‧‧‧‧Steps

t1 ～ t4‧‧‧time

FIG. 1A is a diagram showing a schematic configuration of a diagnosis support system according to this embodiment.

FIG. 1B is a diagram showing an example of a method for segmenting a lung region.

FIG. 1C is a graph showing changes in the shape of the lungs over time.

FIG. 1D is a diagram showing a change in the shape of the lungs with the passage of time.

FIG. 2A is a graph showing a result of "intensity" change of a specific block and Fourier analysis thereof.

FIG. 2B is a graph showing a Fourier transform result of a frequency component close to the heartbeat and an “intensity” change of the frequency component close to the heartbeat by inverse Fourier transform.

FIG. 2C is a diagram showing an example of capturing a certain frequency band in the frequency spectrum obtained after Fourier transform.

FIG. 2D is a graph schematically showing the rate of change of the lungs.

FIG. 2E is a diagram showing an example of a pattern image of a lung field region.

FIG. 2F is a diagram showing an example of a pattern image of a lung field region.

FIG. 2G is a diagram showing an example of a pattern image of a lung field region.

FIG. 2H is a diagram showing an example of a pattern image of a lung field region.

FIG. 3A is a diagram showing an example where the contour of the lung field is drawn using both a Bezier curve and a straight line, and shows a state where the lung field is the largest.

FIG. 3B is a diagram showing an example where the contour of the lung field is drawn using both a Bezier curve and a straight line, and shows a state where the lung field is the smallest.

FIG. 4A is a diagram in which lung field images between the previous frame and the next frame are overlapped.

FIG. 4B is a diagram showing a state where a “line with a stronger gap” is generated as a result of obtaining the difference between the two original images in FIG. 4A.

FIG. 4C is a diagram showing the difference value of the total “density” of the “intensity” values at each position in the vertical direction of the image in FIG. 4B.

FIG. 5 is a graph showing a result of performing curve regression to approximate the relative positions of diaphragms.

FIG. 6A is a flowchart showing an outline of a respiratory function analysis according to this embodiment.

FIG. 6B is a diagram showing an example of an image displayed on the display.

FIG. 6C is a diagram showing an example of an image displayed on the display.

FIG. 7 is a flowchart showing an outline of pulmonary blood flow analysis according to this embodiment.

FIG. 8 is a flowchart showing the outline of another blood flow analysis according to this embodiment.

FIG. 9 is a diagram showing an example of multiplying a fixed spectrum in a spectrum obtained by Fourier transform by a coefficient.

FIG. 10 is a diagram illustrating an example of a lung field using a Bezier curve.

Fig. 11 is a diagram showing an example of dividing a lung field using a Bezier curve.

FIG. 12 is a diagram showing an example of dividing a lung field using a Bezier curve.

FIG. 13 is a diagram showing an example of a waveform of aortic blood flow and a waveform of ventricular volume.

FIG. 14 is a diagram showing an example of the pixel values of the lung and its vicinity.

FIG. 15 is a diagram schematically illustrating a schematic configuration of a human blood vessel.

Claims (37)

A diagnostic support program, characterized in that it analyzes an image of a human body and displays an analysis result, and causes a computer to perform the following processing: Obtaining a plurality of frame images from a database storing the above images; Based on the pixels in a specific area of each of the above frame images, specify at least one frequency of breathing elements including all or a part of exhaled or inhaled; Detecting the lung field based on at least one frequency of the specified respiratory element; Dividing the detected lung field into a plurality of block regions, and calculating the image change of the block regions in each frame image; Fourier transform the image changes of each block area in each frame image; Extracting a frequency spectrum within a certain frequency band in the frequency spectrum obtained after the Fourier transform includes a frequency spectrum corresponding to at least one frequency of the respiratory element; Inverse Fourier transform the spectrum extracted from the above-mentioned certain frequency band; and Each image after the inverse Fourier transform is displayed on a display. For example, if the diagnostic support program of item 1 is requested, it further includes the following processing: using a filter to capture the frequency of noise in the spectrum obtained after the Fourier transform, and including the respiratory elements obtained from the frame image. A frequency outside a frequency, or a frequency spectrum within a certain frequency band corresponding to an input frequency or frequency band. If the diagnostic support program of item 1 or 2 is requested, it further includes the following processing: generating an image between the frames based on the frequency of the respiratory element and the frame images. A diagnostic support program, characterized in that it analyzes an image of a human body and displays an analysis result, and causes a computer to perform the following processing: Obtaining a plurality of frame images from a database storing the above images; Specific at least one frequency of cardiovascular pulsation elements derived from the subject's heartbeat or vascular pulsation; Based on the pixels in a specific area of each of the above frame images, specify at least one frequency of breathing elements including all or a part of exhaled or inhaled; Detecting the lung field based on at least one frequency of the specified respiratory element; Dividing the detected lung field into a plurality of block regions, and calculating the image change of the block regions in each frame image; Fourier transform the image changes of each block area in each frame image; Extracting a frequency spectrum within a certain frequency band in the frequency spectrum obtained after the Fourier transform includes a frequency spectrum corresponding to at least one frequency of the cardiovascular pulsation element; Inverse Fourier transform the spectrum extracted from the above-mentioned certain frequency band; and Each image after the inverse Fourier transform is displayed on a display. A diagnostic support program, characterized in that it analyzes an image of a human body and displays an analysis result, and causes a computer to perform the following processing: Obtaining a plurality of frame images from a database storing the above images; Specific at least one frequency of cardiovascular pulsation elements derived from the subject's heartbeat or vascular pulsation; Detection of lung field; Dividing the detected lung field into a plurality of block regions, and calculating the image change of the block regions in each frame image; Fourier transform the image changes of each block area in each frame image; Extracting a frequency spectrum within a certain frequency band in the frequency spectrum obtained after the Fourier transform includes a frequency spectrum corresponding to at least one frequency of the cardiovascular pulsation element; Inverse Fourier transform the spectrum extracted from the above-mentioned certain frequency band; and Each image after the inverse Fourier transform is displayed on a display. If the diagnostic support program of item 4 or 5 is requested, it further includes the following processing: using a filter to capture the frequency of the noise in the spectrum obtained after the Fourier transform described above, and including the heart obtained from the frame image A frequency other than the frequency of the blood vessel pulsation element, or a frequency spectrum within a certain frequency band corresponding to the input frequency or frequency band. If the diagnostic support program of item 4 or 5 is requested, it further includes the following processing: An image between the frames is generated based on the frequency of the specified cardiovascular pulsation element and the frame images. A diagnostic support program, characterized in that it analyzes an image of a human body and displays an analysis result, and causes a computer to perform the following processing: Obtaining a plurality of frame images from a database storing the above images; At least one frequency of the pulsatile elements specified by the pulsatile extraction of the subject; Divide the analysis range set for each frame image into a plurality of block areas, and calculate the image change of each block area in each frame image; Fourier transform the image changes of each block area in each frame image; Extracting a frequency spectrum within a certain frequency band in the frequency spectrum obtained after the Fourier transform includes a frequency spectrum corresponding to at least one frequency of the cardiovascular pulsation element; Inverse Fourier transform the spectrum extracted from the above-mentioned certain frequency band; and Each image after the inverse Fourier transform is displayed on a display. If the diagnostic support program of item 8 is requested, it further includes the following processing: using a filter to capture the frequency of noise included in the frequency spectrum obtained after the Fourier transform, and including the pulsation element of the blood vessel obtained from the frame image. A frequency other than the frequency, or a frequency band within a certain frequency band corresponding to the input frequency or frequency band. If the diagnostic support program of item 8 or 9 is requested, it further includes the following processing: generating an image between the frames based on the frequency of the specified pulsation element of the blood vessel and the frame images. A diagnostic support program, characterized in that it analyzes an image of a human body and displays an analysis result, and causes a computer to perform the following processing: Obtaining a plurality of frame images from a database storing the above images; Based on the pixels in a specific area of each of the above frame images, specify at least one frequency of breathing elements including all or a part of exhaled or inhaled; Detecting the lung field and diaphragm based on at least one frequency of the specified respiratory elements; Divide the detected lung field into a plurality of block areas, and calculate the change rate of pixels in the block areas in each frame image; Use the ratio of the change rate of the pixels in the above block area to the change rate of the dynamic part that is linked to breathing, that is, the tuning rate, and only extract the block area where the above tuning rate falls within a predetermined range; Each image including only the extracted block area is displayed on the display. If the diagnostic support program of item 11 is requested, it further includes the following processing: specifying at least one frequency of the cardiovascular pulsation element extracted from the subject's heartbeat or vascular pulsation, or at least one of the vascular pulsation elements extracted from the vascular pulsation. frequency. If the diagnostic support program of item 11 or 12 is requested, the logarithm of the above tuning rate is set to a certain range including zero. The diagnostic support program of any one of 4, 5, 8, and 11 further includes the following processing: using at least one Bezier curve on the lung field detected in a specific frame, and detecting other frames In the lung field. For example, the diagnostic support program of item 14, wherein an internal control point is selected in the detected lung field, and the lung field is divided by a curve or a straight line passing through the internal control point in the lung field. If the diagnostic support program of item 15 is requested, wherein the interval between the detected extension of the lung field and the nearby control points is relatively enlarged, and the above is relatively reduced according to the expansion rate of each part in the detected lung field. Interval between internal control points. If the diagnostic support program of item 15 is requested, in the detected lung field, the interval of the control points is relatively enlarged according to the entry of the human body toward the head and tail, or the interval of the control points is relatively enlarged according to the specific vector direction . The diagnostic support program of any one of 4, 5, 8, and 11 further includes the following processing: using at least one Bezier surface on the lung field detected in a specific frame, and detecting other frames In the lung field. The diagnostic support program of any one of 4, 5, 8, and 11 further includes the following processing: using at least one Bezier curve on a predetermined analysis range of a specific frame to detect other The range corresponding to the above analysis range in the frame. The diagnostic support program of any one of 4, 5, 8, and 11 further includes the following processing: using at least one Bezier curve to describe at least the lung field, blood vessels, or the heart. A diagnostic support program, characterized in that it analyzes an image of a human body and displays an analysis result, and causes a computer to perform the following processing: Obtaining a plurality of frame images from a database storing the above images; Use Bezier curves to specify the analysis range for all the frame images obtained above; and The analysis target is detected based on the intensity change in the analysis range. If the diagnostic support program of item 21 is requested, it further includes a process of calculating the characteristics of the edge of the detected analysis target. The diagnostic support program of any one of 4, 5, 8, 11, and 21, wherein the diaphragm is detected by calculating a difference in intensity between successive images, and An index indicating the position or shape of the diaphragm or a dynamic site associated with breathing detected above is displayed. For example, the diagnostic support program of item 23, wherein the diaphragm is blocked by a portion other than the diaphragm, and the entire shape of the diaphragm is interpolated by changing the threshold of the intensity. If the diagnostic support program of item 23 is requested, it further includes the following processing: calculating at least one frequency of the above-mentioned respiratory element from the position or shape of the diaphragm detected above or the position or shape of a dynamic part linked to breathing. The diagnostic support program according to any one of 4, 5, 8, 11, and 21, further including a process of spatially normalizing the detected lung field in space or using time to normalize by using reconstruction. The diagnostic support program according to any one of 4, 5, 8, 11, and 21, wherein the respiratory element is modified by changing a phase of at least one frequency of the respiratory element or smoothing a waveform of the respiratory element. The diagnostic support program of any of 4, 5, 8, 11, and 21, wherein the waveform at any part in the analysis range is specified, the component of the frequency of the specified waveform is extracted, and the frequency of the waveform is output. The image corresponding to the component. The diagnostic support program of any of 4, 5, 8, 11, and 21, wherein the density of the analysis range is detected, and the relatively large density changes are removed. The diagnostic support program of any one of 4, 5, 8, 11, and 21 further includes the following processing: the spectrum obtained after the Fourier transform is selected based on the frequency component ratio of the periodic variation specific to the organ, At least one frequency at the time of the inverse transformation of the leaf. The diagnostic support program according to any one of 4, 5, 8, 11, and 21, wherein the X-ray irradiation interval is adjusted according to at least one frequency of the above-mentioned breathing element, and the X-ray photographing device is controlled. The diagnostic support program of any one of 4, 5, and 8, wherein after the inverse Fourier transform described above, only the blocks with relatively large amplitude values are captured and displayed. The diagnostic support program of any of 4, 5, 8, 11, and 21 further includes the following processing: after identifying the lung field, specifying the diaphragm or thorax, and calculating the change amount of the diaphragm or thorax, from the above The amount of change calculates the rate of change. The diagnostic support program of any of 4, 5, 8, 11, and 21 further includes a process of multiplying a specific frequency spectrum by a coefficient, and performing an emphasis display based on the specific frequency spectrum multiplied by the coefficient. The diagnostic support program of any of 4, 5, 8, 11, and 21, wherein after obtaining a plurality of frame images from a database of stored images, in order to specify the frequency or waveform of the respiratory element, A digital filter is applied to the part. The diagnostic support program of any one of 4, 5, 8, 11, and 21, wherein a plurality of breathing elements including all or part of the breath or inhale are specified based on the pixels in a specific area of each of the frame images described above. frequency, Each image corresponding to each of the plurality of frequencies of the respiratory element is displayed on a display. A diagnostic support program according to any of 4, 5, 8, 11, and 21, wherein for a specific range of more than one frame image, images clustered at a certain value are selected and displayed on a monitor.