JP5200656B2 - Dynamic shooting system - Google Patents

Description

  The present invention relates to a dynamic imaging system.

  When diagnosing the lung using a chest dynamic image, it is important to observe a time-series chest dynamic image in which the subject is captured in a state of natural breathing. As means for evaluating pulmonary function, there are spirometers and RI (Radio Isotope) examinations that can easily acquire physiological data, and simple X-ray photographs and CT (Computed Tomography) that can obtain morphological data. As mentioned above, it is very difficult to efficiently acquire both physiological data and morphological data due to problems in the characteristics of the device.

  In recent years, a dynamic image can be collected by digitizing an X-ray image of a subject obtained by X-ray irradiation using a solid-state imaging device represented by an FPD (Flat Panel Detector). Not only morphological data such as simple radiographs but also time-series signal values can be analyzed by dynamic images, so that physiological data can also be acquired simultaneously.

When performing dynamic observation, it is necessary to consider the burden when photographing the subject. Japanese Patent Laid-Open No. 2004-133867 provides image information useful for diagnosis while instructing a subject to breathing timing so as to coincide with a predetermined timing of dynamic imaging, and suppressing an increase in the burden during imaging of the subject as much as possible. A technique capable of obtaining the above is disclosed.
JP 2003-290184 A

  However, when the subject is instructed to breathe at a desired image capturing phase, it is difficult for a subject such as an elderly person to breathe in accordance with the instruction. Therefore, diagnosis is based on images taken in an unnatural breathing state that is different from the natural breathing state in daily life, and it is not possible to make an evaluation using periodic data on lung function in the natural breathing state. It may not be done correctly.

  The present invention has been made in view of the above problems, and an object of the present invention is to provide effective diagnosis support information based on a change between dynamic periods in diagnosis using a chest dynamic image.

In order to solve the above-mentioned problem, the invention described in claim 1
Imaging means for imaging the dynamics of the subject over a plurality of periods by irradiating the chest of the subject with X-rays, and generating signal values corresponding to the dynamic images,
A feature quantity calculating means for recognizing a plurality of periods of the dynamics from the change in the signal value generated by the photographing means, and calculating a feature quantity indicated by the dynamics for each recognized period ;
And control means for generating diagnosis support information of the dynamics of the plurality of cycles based on a feature quantity for each calculated period by the feature calculating unit,
Is provided.

The invention according to claim 2 is the invention according to claim 1 ,
The dynamic is spontaneous breathing;
The diagnosis support information is one or more pieces of information indicating whether the lung ventilation function of the subject is normal, irregular, obstructive or restrictive.

The invention according to claim 3 is the invention according to claim 1 or 2 ,
Further comprising region dividing means for dividing the lung field region of the dynamic image photographed by the photographing means into a plurality of small regions;
The feature quantity calculation means calculates the feature quantity for each small region.

  According to the present invention, in diagnosis using a chest dynamic image, effective diagnosis support information based on a change between dynamic cycles is provided.

(First embodiment)
First, the configuration will be described.
FIG. 1 shows a dynamic imaging system 1 in the present embodiment.
As shown in FIG. 1, the dynamic imaging system 1 includes an imaging device 10, an imaging console 20, a diagnostic console 30, an image processing device 40, and a server 50, each of which is connected via a network N. It is connected.

FIG. 2 shows a detailed configuration of the imaging device 10, the imaging console 20, and the diagnostic console 30.
As shown in FIG. 2, the imaging apparatus 10 includes an X-ray source 11, a detector 12, a reading unit 13, and a cycle detection unit 14. On the other hand, the imaging console 20 includes a control unit 21, a storage unit 22, an operation unit 23, a display unit 24, and a communication unit 25. Similarly, the diagnostic console 30 includes a control unit 31, a storage unit 32, an operation unit 33, a display unit 34, and a communication unit 35.

First, the imaging device 10 will be described.
The imaging apparatus 10 irradiates the subject M with X-rays and reads an X-ray image from the detector 12. The photographing apparatus 10 can perform dynamic photographing. Dynamic imaging is an imaging method in which imaging is performed continuously to obtain dynamic images at a plurality of time phases. The dynamic image refers to a captured image obtained by dynamic imaging, and in this embodiment, the dynamic image is an X-ray image.
In the present embodiment, the imaging device 10 functions as an imaging unit that performs dynamic imaging of the chest of the subject M that repeats natural breathing and acquires a plurality of dynamic images in a plurality of time phases.

The X-ray source 11 emits X-rays according to the control of the control unit 21 of the imaging console 20. Examples of controlled X-ray irradiation conditions include pulse rate, pulse width, pulse interval, irradiation start / end timing, X-ray tube current, and X-ray tube voltage during continuous imaging in dynamic imaging.
The pulse rate is the number of shootings per unit time. The pulse width is the X-ray irradiation time per imaging. The pulse interval is the time from the start of X-ray irradiation in continuous imaging until the start of X-ray irradiation in the next imaging. The irradiation start / end timing is a timing at which X-ray irradiation starts / ends on the subject M. The X-ray tube current is a current flowing through the X-ray tube constituting the X-ray source 11, and the X-ray tube voltage is a voltage applied to the X-ray tube.

  The detector 12 is disposed at a position facing the X-ray source 11 across the subject M. The detector 12 is an FPD (Flat Panel Detector) or the like in which X-ray detection sensors are arranged in a grid pattern. That is, the X-ray image is recorded on the detector 12 by converting the X-rays into electrical signals according to the intensity and accumulating them for each pixel (detection sensor).

  The reading unit 13 performs processing for reading an X-ray image from the detector 12, and transmits the read X-ray image to the imaging console 20. The reading operation is controlled by the control unit 21. The image reading conditions to be controlled include a frame rate, a frame interval, a pixel size, and the like. The frame rate and the frame interval are the same as the pulse rate and the pulse interval. The pixel size is the size of a pixel constituting an X-ray image.

The cycle detection unit 14 detects a cycle of the biological reaction for the imaging part of the subject M. For example, when the imaging region is the chest including the lung, a respiratory cycle is detected by applying a respiratory monitor belt, a CCD camera, an optical camera, a spirometer, or the like. When the imaging region is the heart, the heartbeat cycle is detected using a heartbeat meter, an electrocardiograph or the like.
The cycle detection unit 14 outputs the detected cycle information to the control unit 21 of the imaging console 20.

Next, the imaging console 20 and the diagnostic console 30 will be described.
The imaging console 20 is used for the imaging operation of the engineer, and accepts input of imaging conditions and displays the X-ray image of the imaging apparatus 10 for the engineer to confirm. The diagnosis console 30 is used for a doctor's operation, and generates diagnosis support information from an X-ray image transmitted from the imaging console 20 or displays it for diagnosis by the doctor.

  The functions of each part of the diagnostic console 30 (the control unit 31, the storage unit 32, the operation unit 33, the display unit 34, and the communication unit 35) are the same as those of the imaging console 20 (the control unit 21, the storage unit 22, the operation unit 23, The display unit 24 and the communication unit 25) are basically the same. Therefore, here, each part of the imaging console 20 will be described as a representative, and description of each part of the diagnostic console 30 will be omitted.

The control unit 21 includes a CPU (Central Processing Unit) and a RAM (Random Access Memory).
) Etc. The control unit 21 reads various programs stored in the storage unit 22 by the CPU, expands them in the RAM, performs various calculations in cooperation with the expanded programs, and centrally controls the operations of the respective units. Execute the process.
The control unit 21 has a timer function for measuring time using the CPU clock.

  The storage unit 22 is a memory such as a hard disk, and stores various programs used by the control unit 21 and parameters necessary for executing the programs. For example, the imaging conditions (such as X-ray irradiation conditions and X-ray image reading conditions) optimized for each imaging region are stored.

The operation unit 23 includes a keyboard, a mouse, and the like, generates an operation signal according to these operations, and outputs the operation signal to the control unit 21.
The display unit 24 includes a display, and displays various operation screens, X-ray images obtained by imaging, and the like according to display control of the control unit 21.
The communication unit 25 includes a communication interface, and communicates with an external device connected to the network N.

  The storage unit 32 of the diagnostic console 30 stores programs and parameters necessary for the process of generating the diagnosis support information, and the diagnosis support information is displayed on the display unit 34. Details of processing for generating diagnosis support information will be described later.

Next, the image processing apparatus 40 and the server 50 will be described.
The image processing apparatus 40 and the server 50 are used to provide an X-ray image obtained by imaging, and are configured to include a control unit and the like. The configurations of the image processing device 40 and the server 50 are the same as the configuration of the imaging console 20, and thus the description thereof is omitted.

  The image processing device 40 performs various image processing such as gradation conversion processing and frequency adjustment processing on the X-ray image so that the image quality is easy for a doctor to observe. The various types of image processing are performed according to the image processing conditions corresponding to the imaging region.

  The server 50 stores and manages the X-ray image processed by the image processing apparatus 40. The X-ray image stored in the server 50 is distributed in response to a request from the diagnostic console 30 and used for diagnosis.

Next, the operation will be described.
The dynamic imaging system 1 according to the present embodiment performs dynamic imaging of the chest, divides the obtained dynamic images in a plurality of time phases, calculates an average signal value for each of the divided areas, Data. Amplitude and period are calculated as feature quantities from the time-series average signal value data, and normal / abnormal lung ventilation function is determined based on the change between the periods of the feature quantities. Is displayed.

FIG. 3 shows the flow of processing in the imaging apparatus 10 and the diagnostic console 30.
As shown in FIG. 3, first, dynamic photographing is performed on the subject M in the photographing apparatus 10, and dynamic images in a plurality of time phases are generated (step S101).
In step S101, specifically, the following processing is performed.

In the present embodiment, since dynamic imaging is performed on the subject M in the natural breathing state, the dynamic imaging is performed so as to generate a plurality of temporal phase dynamic images for at least one respiratory cycle.
At the time of imaging, the imaging engineer inputs patient information regarding the subject M, specifies an imaging region, and the like via the operation unit 23 of the imaging console 20. In addition to the name of the subject M (patient), the patient information includes information indicating patient attributes such as age, sex, weight, and height.

In the imaging console 20, the imaging conditions corresponding to the imaging region of the subject M designated by the control unit 21 are read from the storage unit 22, and the X-ray irradiation conditions in the X-ray source 11 of the imaging apparatus 10 and the reading unit 13. Are set as image reading conditions.
In the following description, it is assumed that the imaging region of “lung (ventilation)” is designated by the imaging technician. When the lungs are imaged to diagnose the ventilation ability, the respiratory cycle is about 0.3 times / second on average, so that it is possible to capture dynamic images of a plurality of time phases for at least one respiratory phase. Thus, for example, the following shooting conditions are set.
Frame rate (pulse rate): 2 frames / second (that is, 3 shots per second)
Pixel size: 150 μm
Image size: 40cm x 40cm
Tube voltage: 120 kV
Tube current: 50 mA
Shooting timing: Every frame interval from the timing of the conversion point from inspiration to expiration (shooting start timing)

  Note that conditions such as the frame rate are corrected based on the information on the respiratory cycle detected by the cycle detection unit 14 by the control unit 21. For example, based on the detected respiratory cycle, the frame rate is calculated and reset so that one respiratory phase is captured with a predetermined number of frames (for example, 10 frames). In the above frame rate condition example, when the number of respiratory cycles detected by the cycle detector 14 is 0.25 times / second, the frame rate is corrected to 2.5 frames / second. .

  After setting the imaging conditions, based on the information of the respiratory cycle detected by the cycle detection unit 14 by the control unit 21, whether or not it is the imaging start timing, that is, the timing at which one respiratory phase starts (for example, inspiration → expiration It is determined whether or not it is a conversion point. At the imaging start timing, the X-ray source 11 and the reading unit 13 are controlled by the control unit 21 to start dynamic imaging. In addition, the control unit 21 sets the shooting time required from the start to the end of shooting in accordance with the start of dynamic shooting.

In the imaging apparatus 10, X-rays are irradiated at a predetermined pulse rate according to the set X-ray irradiation conditions. Similarly, the reading unit 13 performs X-ray image reading processing from the detector 12 at a predetermined frame rate in accordance with the set image reading conditions. The X-ray irradiation operation and the image reading operation are synchronized by the control unit 21.
Through the processing in step S101 described above, dynamic images at a plurality of time phases are generated and output to the imaging console 20.

  In the imaging console 20, the dynamic image of each time phase obtained in step S101 is displayed on the display unit 24. This is because the photographer confirms the image quality and the like. When an approval operation is performed by the imaging engineer via the operation unit 23, the control unit 21 attaches an ID for identifying a series of imaging, a patient information, and imaging time information to the dynamic image of each time phase. The dynamic image is transmitted to the diagnostic console 30.

  In the diagnostic console 30, the control unit 31 divides the dynamic image received via the imaging console 20 into regions according to a predetermined division method (step S102). Specifically, in this embodiment, since a dynamic image of the chest is taken, for example, the lung field region is divided into regions for each anatomical structure of the lung such as the upper right lobe and the upper left lobe, or the dynamic image The entire area is divided into a grid pattern. By the processing in step S102, the control unit 31 functions as an area dividing unit. Note that the processing in steps S103 to S109 described below is executed in cooperation with the control unit 31 and the program stored in the storage unit 32.

FIG. 4 shows an example in which the lung field region is divided into regions for each anatomical structure.
As shown in FIG. 4, the lung field region is divided into anatomical structures such as an upper right lobe, an upper left lobe, a right middle lobe, a lower right lobe, and a lower left lobe. When the region is divided as shown in FIG. 4, it is possible to determine the lung ventilation function for each anatomical structure.
At the time of division in this case, in step S102, using the reference image in which the position and name of the anatomical structure are determined in advance, the lung field region of the reference image and the lung field region of each dynamic image are substantially matched. In addition, it is only necessary to recognize and divide regions of each anatomical structure by performing image conversion by nonlinear warping processing or the like.

FIG. 5 shows an example in which a lung field region of a certain dynamic image is divided into a grid pattern.
As shown in FIG. 5, the dynamic image is divided into a grid pattern for every predetermined pixel. In the example of FIG. 5, the example divided | segmented into the 10x10 grid | lattice form is shown. Specifically, since the image size of the dynamic image is 40 cm × 40 cm in the above example, one divided region is a region surrounded by 4 cm × 4 cm.
When the region is divided as shown in FIG. 5, unlike the case where the region is divided for each anatomical structure, the lung ventilation function can be determined for each region surrounded by a grid.

The specific process of step S102 in the case where the area is divided into a grid will be described below.
First, a certain dynamic image is designated as the reference image (for example, the dynamic image of the first frame is designated as the reference image). Then, a lung field region is detected from the reference image, and the region is divided into a certain substantially rectangular shape so as to circumscribe the lung field region. An area corresponding to the divided area is obtained. Specifically, the dynamic image whose temporal phase is adjacent to the reference image is divided into, for example, an area twice as large as the reference image. Then, the corresponding divided area of the reference image is moved in the divided area having the double size, the matching degree is calculated for each movement, and the position where the matching degree is maximized is obtained. The degree of matching refers to the degree of image matching, and can be obtained by the least square method or cross-correlation. By repeating this process between the dynamic images adjacent in time phase, it is possible to determine which region of the dynamic image in another time phase corresponds to each divided region of the reference image.
By performing the above processing in step S102, for example, when the reference image is divided into regions as shown in FIG. 5, the divided region corresponding to the region surrounded by 4 cm × 4 cm is also calculated for other dynamic images. can do.

Hereinafter, in step S102, a case where the lung field region is divided into a grid as shown in FIG. 5 will be described. In addition, even if it is a case where a lung field area | region is divided | segmented as shown in FIG. 4, the subsequent basic processes are the same.
In step S103, the average signal value is calculated for each of the divided areas divided in step S102 (step S103). Specifically, the average value of the signal values of the pixels included in each divided region in all dynamic images received by the diagnostic console 30 is calculated.

なお、解剖学的構造毎に領域が分割された場合も、分割領域に含まれる画素の信号値の総和を当該分割領域に含まれる画素数で割ることにより同様に算出することができる。 Hereinafter, the calculation method of the average signal value in step S103 will be described.
FIG. 6 schematically shows one divided region when the region is divided in a lattice shape. As shown in FIG. 6, the pixels constituting one divided region are N pixels × N pixels, and the signal value of the i-th pixel in the horizontal direction and the j-th pixel in the vertical direction is I (i, j).
In the example shown in FIG. 6, since N × N pixels are included in one divided region, the average signal value for each divided region is calculated by the following equation in step S103. .

Note that even when a region is divided for each anatomical structure, it can be calculated in the same manner by dividing the sum of the signal values of the pixels included in the divided region by the number of pixels included in the divided region.

  In step S104, the average signal value for each divided region calculated in step S103 is acquired as time series data (step S104). Specifically, since the plurality of dynamic images received by the diagnostic console 30 are taken in time series for each time phase, the average signal value for each divided region calculated for the plurality of dynamic images is also the time. It becomes the data of the series.

FIG. 7A shows an example in which the average signal value of a certain divided region is schematically represented in time series. In dynamic imaging in the present embodiment, the change in average signal value calculated for each time phase can be approximated to a waveform as shown in FIG.
The signal value of the X-ray image is determined by the X-ray transmission amount of the subject M, and the X-ray transmission amount changes depending on the thickness of the subject M. This is because the greater the thickness of the subject M, the more difficult it is to transmit X-rays. Therefore, a change in signal value can be considered as a change in lung thickness, that is, a change in lung ventilation. The change in the average signal value is correlated with the ventilation. is there.
In the case of a normal lung ventilation function, the average signal value changes with a substantially constant time phase interval (hereinafter referred to as period T (n)) from the maximum value (peak) of the average signal value to the next maximum value. .
Accordingly, the period T (n) of the average signal value indicates the time of the natural breathing period, and the difference between the maximum value and the minimum value of the average signal value for each period T (n) (hereinafter simply referred to as amplitude A (n)). Is proportional to lung ventilation. Note that “n” in the period T (n) and the amplitude A (n) means the nth period, that is, the period T (1) and the amplitude A (1) are the period and amplitude in the first period, respectively. Will show.

Of the time series data acquired in step S104, the period T (n) and the amplitude A (n) are calculated using one set of time series data from inspiration to next inspiration as one respiration data (step S105). . Specifically, as described above, the period from the maximum value of the average signal value to the next maximum value is identified as one period, and the period T (n) is calculated from the time phase interval at which the average signal value takes the maximum value. The amplitude A (n) is calculated from the difference between the maximum value and the minimum value of the average signal value in one cycle. In the present embodiment, the period T (n) and the amplitude A (n) are calculated for each divided region. In step S105, the period T (n) and the amplitude A (n) are respectively calculated by the number of waveform periods that can be taken by the average signal value of the divided areas.
By the processing in step S105, the control unit 31 functions as a feature amount calculation unit.

  Next, the normal / abnormal amplitude is determined from the amplitude A (n) obtained in step S105 (step S106). Specifically, whether or not there is an amplitude that is equal to or smaller than a threshold value determined based on the value of A (1) that is the amplitude of the first cycle, or a threshold value and amplitude A (n) that are determined in advance as absolute quantities The normality / abnormality of the amplitude is discriminated by comparing these values.

  FIG. 7B shows a case where the amplitude gradually decreases as time elapses. As shown in FIG. 7 (b), in dynamic imaging in the present embodiment, patient image data is collected in a state of spontaneous breathing, but time elapses when there is an abnormality in the lung ventilation function. Ventilation may decrease with time. That is, the amplitude gradually decreases with time.

  FIG. 7C shows a case where the absolute amount of amplitude is low regardless of the passage of time. As shown in FIG. 7C, when the lung ventilation function is abnormal, the ventilation amount may be absolutely low. That is, the amplitude is small regardless of the passage of time.

Specifically, the determination in step S106 is, for example, that the value of the amplitude A (1) is 100%, the threshold is set to 90% of the amplitude A (1), and the amplitude A (n) is less than or equal to the threshold. If there is such an amplitude, it is determined that the lung ventilation function is as shown in FIG. 7B (hereinafter referred to as “ventilation amplitude malignant (1)”), By setting a threshold value for the amplitude and comparing the threshold value with the amplitude A (n) to determine whether or not the amplitude A (n) equal to or lower than the threshold value is greater than or equal to a certain number, FIG. (Hereinafter referred to as “ventilation amplitude malignant (2)”) or the like.
The case where the lung ventilation function is not determined to be abnormal by the determination in step S106 is hereinafter referred to as “ventilation amplitude benign”.

  Next, whether the cycle is normal or abnormal is determined from the cycle T (n) obtained in step S105 (step S107). Specifically, the normality / abnormality of the cycle is determined based on whether or not there is a cycle that is equal to or less than a threshold value determined based on the value of T (1) that is the cycle of the first cycle.

  FIG. 7D shows a case where the breathing cycle is disturbed as time elapses. As shown in FIG. 7D, in dynamic imaging in the present embodiment, patient image data is collected in a state of spontaneous breathing, but time elapses when there is an abnormality in the lung ventilation function. As a result, the periodicity of breathing is disturbed.

Specifically, the determination in step S107 is, for example, by setting the value of T (1), which is the cycle of the first cycle, to 100% and setting the threshold value to 90% of T (1), and setting the cycle T (n) If there is a cycle that is less than or equal to the threshold value, it is determined that the lung ventilation function is as shown in FIG. 7D (hereinafter, this case is referred to as “ventilation cycle malignancy”). .
The case where the lung ventilation function is not determined to be abnormal by the determination in step S107 is hereinafter referred to as “ventilation cycle benign”.

  Next, the correspondence table G1 stored in the storage unit 32 is referred to, and the lung ventilation function is determined from the determination result of the amplitude and period obtained in steps S106 and S107 (step S108).

  FIG. 8 shows an example of the correspondence table G1 referred to in step S108. As shown in FIG. 8, the correspondence table G1 includes the ventilation amplitude benign, the ventilation amplitude malignant (1), and the ventilation amplitude malignant (2) determined in step S106, and the ventilation cycle benign and ventilation cycle determined in step S107. The state of lung ventilation function is associated with malignancy.

In the example of FIG. 8, the lung ventilation function is classified into “normal findings”, “irregular respiratory pace”, “occlusion”, “restraint”, and “occlusion + restraint”.
“Normal findings” indicate that the lung ventilation function is normal.
“Irregular respiratory pace” means that the periodicity of the patient's natural breathing is disturbed.
“Occlusive” indicates that air remains in the lungs in the expired state, so that the amplitude decreases and the periodicity of spontaneous breathing is disturbed.
“Restrictive” indicates that the amplitude is reduced and the periodicity of the respiratory cycle is disturbed due to a decrease in lung volume or vital capacity.
“Occlusion + restraint” indicates “occlusion” and “restraint”.
For example, if it is determined in step S106 that the ventilation amplitude is malignant (1) and it is determined in step S107 that the ventilation cycle is benign, then in step S108, the correspondence table G1 is referred to. The patient will be determined to be “obstructive”.
Note that the processing in steps S106 to S108 is performed for each divided region.

Next, the diagnosis support information generated in step S108 is displayed on the display unit 34 (step S109). Specifically, since the processing of step S103 to step S108 is performed for each divided region divided in step S102, diagnosis support information is displayed on the display unit 34 for each divided region. For example, the diagnosis support information is displayed on the display unit 34 by displaying the divided area suspected of “occlusive” in red and displaying the divided area suspected of “restraint” in blue. Become.
The control unit 31 functions as a control unit by the processing in step S108 and step S109.

  As described above, according to the dynamic imaging system 1 in the present embodiment, a dynamic image captured in a state of spontaneous breathing can be obtained, and thus the dynamic image is useful in evaluating the lung ventilation function of the subject M. Information. Since dynamic images are taken in time series, it is possible to analyze not only morphological information but also the amplitude and period calculated as feature values for each period, compared to simple X-ray photographs, etc. Information can also be obtained.

  In addition, the average signal value is calculated for each segment of the dynamic image, and the amplitude and period of the average signal value is calculated to determine the lung ventilation function. The lung ventilation function can be determined for each divided area. Therefore, diagnosis support information indicating normality / abnormality of the lung ventilation function can be displayed on the display unit 34 for each divided region, and the diagnosis support information when the user diagnoses the lung ventilation function of the subject M is displayed in the divided region. Can be provided every time. In addition, by displaying the diagnosis support information superimposed on the dynamic image, the user can be more efficiently supported for diagnosis.

  In addition, since the lung ventilation function is determined from the amplitude and period of the dynamic image using the correspondence table G1, the lung ventilation function is more accurate than when the lung ventilation function is determined from only one information of amplitude or period. Function discrimination can be performed. Since the correspondence table G1 determines the lung ventilation function based on the amplitude and the period, the correspondence table G1 is stored in the storage unit 32 for each user or subject M, and the characteristics for each user or subject M are obtained by referring to them appropriately. The lung ventilation function can be determined accordingly.

In addition, the description in this Embodiment mentioned above is an example of the suitable dynamic imaging | photography system which concerns on this invention, and is not limited to this.
For example, in the present embodiment, the determination process of the lung ventilation function is performed in the diagnostic console 30. However, the determination process may be performed by the devices constituting the dynamic imaging system 1. For example, the determination processing of the lung ventilation function may be performed by the image processing device 40, the determination result may be transmitted to the diagnostic console 30, and the determination result may be displayed on the display unit 34. .

  In the present embodiment, the method for dividing the region of the dynamic image is shown in FIGS. 4 and 5, but the method for dividing the region is not limited to this. For example, the dynamic image is not grid-like, and the user may designate an arbitrary area in advance, and the dynamic image may be divided for each designated area to determine the lung ventilation function.

  Further, the normal / abnormal determination method of the lung ventilation function based on the amplitude and the period is not limited to the method described in the present embodiment. For example, the user may be able to set the amplitude and period thresholds depending on the sex of the subject M. Similarly, the state of the lung ventilation function obtained by the determination method is not limited to the example shown in FIG. A determination result obtained from the amplitude and period may be used, and an abnormal state of the lung ventilation function other than the diagnosis support information shown in the list G1 of FIG. 8 may be determined.

  In the above description, an example in which a hard disk, a semiconductor nonvolatile memory, or the like is used as a computer-readable medium of the program according to the present invention is disclosed, but the present invention is not limited to this example. As another computer-readable medium, a portable recording medium such as a CD-ROM can be applied. A carrier wave is also applied as a medium for providing program data according to the present invention via a communication line.

  In addition, the detailed configuration and detailed operation of each device constituting the dynamic imaging system 1 can be appropriately changed without departing from the gist of the present invention.

(Second Embodiment)
Next, a second embodiment of the dynamic imaging system 1 according to the present invention will be described with reference to FIGS. Note that the configurations of the imaging device 1, the imaging console 20, the diagnostic console 30, the image processing device 40, and the server 50 constituting the dynamic imaging system 1 are the same as those in the first embodiment, and thus the description thereof is omitted. .

The dynamic imaging system 1 in the first embodiment discriminates whether the lung ventilation function is normal or abnormal by analyzing the dynamic image, but the dynamic imaging system 1 in the second embodiment analyzes the dynamic image. Thus, the normal / abnormal pulmonary blood flow function is discriminated.
The point that the diagnosis support information is created by using the amplitude and period of the average signal value as the feature amount is the same as in the first embodiment.

FIG. 9 shows a flow of processing in the imaging apparatus 10 and the diagnostic console 30 in the second embodiment.
As shown in FIG. 9, first, dynamic imaging of the chest is performed in the imaging device 10, and dynamic image data in a plurality of time phases is generated (step S201).
Note that in order to diagnose the pulmonary blood flow function, the subject M is held in breath and dynamic imaging is performed in step S201 in order not to change the lung field region. Since specific processing in the photographing apparatus 10 is the same as that in step S101, the description thereof is omitted.

  In the diagnostic console 30, the dynamic image received via the imaging console 20 is divided into regions according to a predetermined division method (step S202). Specifically, for example, the lung field region is divided into regions by, for example, polar coordinate division using the left and right lung fields as the origin.

FIG. 10 shows an example in which the lung field region is divided into regions by polar coordinate division with the lung pattern of both the left and right lung fields as the origin.
As shown in FIG. 10, in the lung field region, the lung pattern of one lung field is set as the origin O1, and the coordinate r1 (θ1) of the lung field region is determined by the distance r1 from the origin O1 and the angle θ1. Similarly, in the other lung field, the lung pattern is the origin O2, and the coordinate r2 (θ2) of the lung field is determined by the distance r2 from the origin O2 and the angle θ2. The angles θ1 and θ2 refer to angles from a straight line that passes through the origin O1 and the origin O2, respectively, and is perpendicular to the dynamic image.
In the region division in step S202, when the polar coordinates of the lung field having the origin as O1 are divided, the region division is performed every constant distance of r1 (for example, every 3 cm), every angle θ1 is every constant angle (for example, every 30 °), or the like. It will be.
Since the specific process in step S202 is the same as that in step S102, description thereof is omitted.

Since the processes in step S203 and step S204 are the same as those in step S103 and step S104, respectively, the description thereof is omitted.
Next, in the time series data acquired in step S204, one set of dilation from the expansion of the heart (showing from the timing when the heart changes from diastole to systole to the timing from the next diastole to systole) Are used as one heartbeat data, and the amplitude and period are calculated (step S205). In step S105 in the first embodiment, the change in the average signal value is associated with the ventilation amount, but in the second embodiment, the amplitude of the change in the average signal value is the cardiac output of the cardiac function, The period is evaluated as a heartbeat period. That is, the maximum value of the average signal value to the next maximum value is defined as one cycle of the heartbeat cycle, and the amplitude of the maximum value and the minimum value of the average signal value in one cycle is evaluated as the cardiac output. Since the process in step S205 is the same as the process in step S105, the specific process is omitted.

  Next, the normal / abnormal amplitude is determined from the amplitude A (n) obtained in step S205 (step S206). Specifically, the normal / abnormal amplitude is determined from the relationship between the distance r1 from the origin O1 and the amplitude A (n).

FIG. 11A shows an example of the relationship between the distance r1 and the amplitude A (n) in the divided region having the same angle θ1 in a certain heartbeat period n. Since the amplitude A (n) is calculated for each divided region, the number of divided regions A (n) having the same angle θ1 is compared.
In FIG. 11B, an example of a divided region having the same angle θ1 is indicated by hatching. The relationship shown in FIG. 11A shows the relationship between the amplitude of the average signal value of the divided area included in the shaded area shown in FIG. 11B and the distance r1 from the origin.
As shown in FIG. 11A, the amplitude A (n) increases as the distance from the origin O1 is generally closer to the origin O1, which is a lung pattern, and the amplitude A (n) tends to decrease as the distance r1 increases.
Specifically, the processing in step S206 is calculated in step S205 with the data of a normal example having a normal pulmonary blood flow function stored in the storage unit 32 (in the case of FIG. 11, the line indicated by a solid line). The amplitude A (n) is compared, and the distance r1 from the origin O1 is abruptly decreasing compared to the normal example after a certain point (hereinafter referred to as the occlusion site) (FIG. 11). In this case, it is a line indicated by a broken line.Hereinafter, this case is referred to as blood flow amplitude malignancy (1)), and the distance r1 has a sharper amplitude A (n) than a normal example only around a certain point. (In the case of FIG. 11, this is a line indicated by a one-dot chain line. This case is hereinafter referred to as blood flow amplitude malignancy (2)). The data of the normal example shown in FIG. 11 is stored in the storage unit 32 for each user or for each subject M, and the data can be read out.
The case where the determination in step S206 does not determine that the pulmonary blood flow function is abnormal is hereinafter referred to as “blood flow amplitude benign”.

Next, normality / abnormality of the period T (n) is determined from the period T (n) obtained in step S205 (step S207). Specifically, as in step S107 of the first embodiment, when the cycle T (1) is set to 100% and the threshold value is set to 90% of this value, and there is a cycle that is equal to or less than the threshold value, The pulmonary blood flow function is determined to be abnormal (hereinafter, this case is referred to as “blood flow cycle malignancy (1)”) or the like.
The case where the determination in step S207 does not determine that the pulmonary blood flow function is abnormal is hereinafter referred to as “blood flow cycle benign”.

  Next, the correspondence table G2 stored in the storage unit 32 is referred to, and the pulmonary blood flow function is determined from the determination results of the amplitude and the period obtained in steps S206 and S207 (step S208).

FIG. 12 shows an example of the correspondence table G2 referred to in step S208. As shown in FIG. 12, the correspondence table G2 includes the blood flow amplitude benign, the blood flow amplitude malignant (1), and the blood flow amplitude malignant (2) determined in step S206, and the blood flow cycle determined in step S207. The state of pulmonary blood flow function is associated with benign and malignant blood flow cycles.
In the example of FIG. 12, the pulmonary blood flow function is classified into “normal findings”, “obstructive”, “lung cancer”, “arrhythmia”, “occlusive + arrhythmia”, and “lung cancer + arrhythmia”.
“Normal findings” indicate that the pulmonary blood flow function is normal.
“Occlusive” means that the amplitude is reduced by decreasing the blood flow after the occlusion site (hereinafter, the distance r1 is larger than the occlusion site in the region where θ1 is the same as the occlusion site). Indicates to do.
“Lung cancer” indicates that the amplitude is increased by the formation of blood vessels by cancer cells and the increase in blood flow.
“Arrhythmia” indicates that the periodicity of the heartbeat is disturbed.
“Occlusive + arrhythmia” indicates “occlusive” and “arrhythmic” state.
“Lung cancer + arrhythmia” indicates “lung cancer” and a state of “arrhythmia”.
For example, if it is determined in step S206 that the blood flow amplitude is malignant (1), and if it is determined in step S207 that it is periodic benign, in step S208, the correspondence table G2 is referred to. The patient will be determined to be “obstructive”.

  Next, the diagnosis support information generated in step S208 is displayed on the display unit 34 (step S209). Specifically, for example, the diagnosis support information is displayed on the display unit 34 by displaying the divided area suspected of “obstructive” in red and displaying the divided area suspected of “lung cancer” in blue. Will be.

  As described above, according to the dynamic imaging system 1 in the second embodiment, the information for diagnosing the pulmonary blood flow function is morphologically determined from the amplitude and period of the dynamic image compared to a simple X-ray photograph or the like. In addition to simple information, physiological information can be acquired by analyzing time-series signal values.

In addition, the description in this Embodiment mentioned above is an example of the suitable dynamic imaging | photography system which concerns on this invention, and is not limited to this.
For example, in the second embodiment, the region is divided by polar coordinates. However, as in the first embodiment, the region may be divided for each anatomical structure or in a lattice shape. . Also in this case, diagnosis support information is generated from the amplitude and period calculated for each of the divided regions, and whether the pulmonary blood flow function is normal or abnormal can be displayed on the display unit 34.

  In addition, the detailed configuration and detailed operation of each device constituting the dynamic imaging system 1 can be appropriately changed without departing from the gist of the present invention.

It is a figure which shows the dynamic imaging | photography system in this embodiment. It is a figure which shows the functional structure of the imaging device of FIG. 1, an imaging console, and a diagnostic console. It is a figure which shows the flow of a process in a dynamic imaging | photography system. It is a figure which shows the example of a division | segmentation of an area | region. It is a figure which shows the example of a division | segmentation of an area | region. It is a figure which shows typically the pixel which comprises a division area. It is a figure which shows the average signal value of a division area in time series, (a) is a case where it has a normal lung ventilation function, (b) is a case where an amplitude reduces as time passes, (c) Is the case where the absolute value of the amplitude is small, and (d) is the case where the period is not constant. It is a figure which shows an example of the correspondence table used when discriminating a lung ventilation function. It is a figure which shows the flow of a process in the dynamic imaging | photography system in 2nd Embodiment. It is a figure which shows the example of a division | segmentation of an area | region. It is a figure which shows the amplitude of the average signal value in 2nd Embodiment, (a) is a figure which shows the amplitude of the average signal value of a division area in relation to the distance from the origin of a polar coordinate, (b) is an amplitude. It is a figure which shows the division area which compares. It is a figure which shows an example of the correspondence table used when determining a pulmonary blood flow function.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Dynamic imaging | photography system 10 Imaging device 11 X-ray source 12 Detector 13 Reading part 14 Cycle detection part 20 Imaging console 21 Control part 22 Storage part 23 Operation part 24 Display part 25 Communication part 30 Diagnosis console 31 Control part 32 Storage part 33 Operation unit 34 Display unit 35 Communication unit 40 Image processing device 50 Server

Claims (3)

Imaging means for imaging the dynamics of the subject over a plurality of periods by irradiating the chest of the subject with X-rays, and generating signal values corresponding to the dynamic images,
A feature quantity calculating means for recognizing a plurality of periods of the dynamics from the change in the signal value generated by the photographing means, and calculating a feature quantity indicated by the dynamics for each recognized period ;
And control means for generating diagnosis support information of the dynamics of the plurality of cycles based on a feature quantity for each calculated period by the feature calculating unit,
Dynamic shooting system with The dynamic is spontaneous breathing;
2. The dynamic imaging according to claim 1 , wherein the diagnosis support information is one or more pieces of information indicating that the pulmonary ventilation function of the subject is normal, periodic irregularity, obstructiveness, or restrictiveness. system. Further comprising region dividing means for dividing the lung field region of the dynamic image photographed by the photographing means into a plurality of small regions;
The dynamic imaging system according to claim 1 , wherein the feature amount calculating unit calculates the feature amount for each of the small regions.

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