WO2011092982A1 - Dynamic image processing system and program - Google Patents

Abstract

The objective of the present invention is to accurately calculate information relating to bloodstream flowing through the peripheral blood vessels in a lung field. A diagnostic consol (3) according to the present invention includes a controller (31) which extracts a lung field from each frame image representing the dynamic state of a chest region to identify a vessel territory from the extracted lung field and remove the identified vessel territory from each frame image. Then, the controller divides, into multiple block areas, the lung field in each frame image from which the vessel territory has been removed, to calculate a variation amount of a pixel signal value of each block area and calculate bloodstream information of the peripheral blood vessels in each block area on the basis of the calculated variation amount of the pixel signal.

Description

Dynamic image processing system and program

The present invention relates to a dynamic image processing system and a program.

In contrast to still image photography and diagnosis by chest radiation using conventional film / screen and photostimulable phosphor plate, a dynamic image of the chest is taken using a semiconductor image sensor such as FPD (flat panel detector), Attempts have been made to apply it to diagnosis. Specifically, by utilizing the responsiveness of reading / erasing of image data of the semiconductor image sensor, pulsed radiation is continuously irradiated from the radiation source in accordance with the reading / erasing timing of the semiconductor image sensor. Take multiple shots per second to capture the dynamics of the chest. By sequentially displaying a series of a plurality of images acquired by imaging, a doctor can observe a series of movements of the chest accompanying respiratory motion, heart beats, and the like.

However, it is difficult to visually recognize the abnormal part by observing the displayed dynamic image, and interpretation time is also required. Therefore, various techniques for displaying dynamic images in an easy-to-view manner have been proposed. For example, Patent Document 1 describes a technique for displaying a moving image from which bones and blood vessels are removed by capturing adjacent frame images with X-rays having different energies and subjecting them to subtraction processing.

JP 2004-328145 A

By the way, when trying to quantitatively calculate the blood flow amount flowing in the peripheral blood vessel in the lung field region from the X-ray dynamic image, the pulmonary blood vessel in which the blood flowing in the peripheral blood vessel is concentrated (for example, the pulmonary artery such as the right interlobe pulmonary artery, There is a problem that the blood flow volume of the peripheral blood vessel is calculated to be larger than the actual blood flow due to the signal change on the pulmonary vein such as the right lower pulmonary vein.

When subtraction processing is performed on a plurality of pairs of adjacent frame images using the same coefficient as in the technique described in Patent Document 1, the signal value on the pulmonary blood vessel changes with time in the X-ray dynamic image, There has been a problem that signal changes due to blood flow on the pulmonary blood vessels cannot be removed and remain as noise in the image.

An object of the present invention is to enable accurate calculation of information on blood flow flowing through peripheral blood vessels in a lung field region.

In order to solve the above-described problem, a dynamic image processing system according to claim 1 is provided.
Lung field region extraction means for extracting a lung field region from each of a plurality of frame images indicating the dynamics of the chest,
Blood vessel region recognition means for performing blood vessel region recognition processing in the extracted lung field region of each frame image;
A blood vessel region removing unit that removes the blood vessel region recognized by the blood vessel region recognizing unit from each frame image;
A dividing unit for dividing the lung field region into a plurality of block regions in each frame image from which the blood vessel region has been removed by the blood vessel region removing unit;
Calculating means for calculating a change amount of a pixel signal value in each block region between the frame images from which the blood vessel region has been removed by the blood vessel region removing unit;
Blood flow information calculating means for calculating blood flow information of peripheral blood vessels existing in each block area based on a change amount of a pixel signal value in each block area calculated by the calculating means;
Is provided.

The invention according to claim 2 is the invention according to claim 1,
An operation means for an operator to specify a part of a blood vessel region to be recognized by the blood vessel region recognition means;
The blood vessel region recognizing unit performs a blood vessel region recognition process based on the region designated by the operation unit.

The invention according to claim 3 is the invention according to claim 1,
In the extracted lung field region, comprising an operation means for an operator to specify a region for performing blood vessel recognition processing by the blood vessel region recognition means,
The blood vessel region recognizing unit performs a blood vessel region recognition process only for a region designated by the operation unit.

The invention according to claim 4 is the invention according to any one of claims 1 to 3,
Display means for displaying blood flow information calculated by the blood flow information calculating means on a display unit;
Is provided.

The invention according to claim 5 is the invention according to claim 4,
Based on the blood flow information of the peripheral blood vessels present in each block region, comprising a determination means for determining whether the blood flow of the peripheral blood vessels in each block region is abnormal,
The display means causes the position of the block area determined to be abnormal by the determination means to be displayed on the display unit so as to be identifiable on a frame image displayed as a moving image.

The invention according to claim 6 is the invention according to any one of claims 1 to 5,
The blood vessel region removing unit replaces the recognized pixel signal value of the blood vessel region with a value determined based on the pixel signal value of the non-blood vessel region in the vicinity thereof.

The program of the invention described in claim 7 is:
Computer
Lung field extraction means for extracting a lung field from each of a plurality of frame images showing the dynamics of the chest,
A blood vessel region recognition means for performing a blood vessel region recognition process in the extracted lung field region of each frame image;
A blood vessel region removing unit that removes the blood vessel region recognized by the blood vessel region recognizing unit from each frame image;
A dividing unit for dividing the lung field region into a plurality of block regions in each frame image from which the blood vessel region has been removed by the blood vessel region removing unit;
Calculating means for calculating a change amount of a pixel signal value in each block region between frame images from which the blood vessel region has been removed by the blood vessel region removing unit;
Blood flow information calculating means for calculating blood flow information of peripheral blood vessels existing in each block area based on the amount of change in the pixel signal value in each block area calculated by the calculating means;
To function as.

The invention according to claim 8 is the invention according to claim 7,
The computer,
Display means for displaying blood flow information calculated by the blood flow information calculating means on a display unit;
To function as.

According to the present invention, it is possible to accurately calculate information on blood flow flowing through peripheral blood vessels in the lung field region.

It is a figure which shows the whole structure of the dynamic image processing system in embodiment of this invention. It is a figure which shows the detailed structural example of the radiation generator of FIG. 1, and a radiation detection part. It is a figure which shows an example of the patient fixing | fixed part of the radiation detection part of FIG. 3 is a flowchart illustrating a shooting control process executed by a control unit of the shooting console of FIG. 1. It is a flowchart which shows the image analysis process performed by the control part of the diagnostic console of FIG. It is a flowchart which shows the pre-processing performed in step S11 of FIG. It is a figure for demonstrating the small area | region acquired by the lung field area | region extraction division | segmentation process of FIG. FIG. 5 is a diagram illustrating an example of a GUI for designating a target region for the blood vessel region recognition process in FIG. 4. FIG. 5 is a diagram for explaining an example of recognizing a blood vessel region according to an operation by an operator in the blood vessel region recognition process of FIG. 4. It is a figure for demonstrating the blood vessel region removal process of FIG. It is a figure which shows an example of the image before and after the process by the blood vessel region removal process of FIG. It is a figure which shows the example of a display of the blood-flow information in step S19 of FIG. It is a figure which shows the time change of the addition signal value in a certain block area | region. It is a figure which shows the example of a display of the abnormal block area | region in FIG.4 S21.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. However, the scope of the invention is not limited to the illustrated examples.

<First Embodiment>
[Configuration of Dynamic Image Processing System 100]
First, the configuration will be described.

FIG. 1 shows the overall configuration of a dynamic image processing system 100 in the present embodiment.

As shown in FIG. 1, in the dynamic image processing system 100, an imaging device 1 and an imaging console 2 are connected by a communication cable or the like, and the imaging console 2 and the diagnostic console 3 are connected to a LAN (Local Area Network). Etc., and connected via a communication network NT. Each device constituting the dynamic image processing system 100 conforms to the DICOM (Digital-Image-and-Communications-in-Medicine) standard, and communication between the devices is performed according to DICOM.

[Configuration of the photographing apparatus 1]
The imaging apparatus 1 is an apparatus that images the dynamics of the chest with periodicity (cycle), such as pulmonary expansion and contraction morphological changes, heart pulsation, and the like accompanying respiratory motion. Dynamic imaging is performed by continuously irradiating the chest of a human body with radiation such as X-rays in a pulse manner to acquire a plurality of images (that is, continuous imaging). A series of images obtained by this continuous shooting is called a dynamic image. Each of the plurality of images constituting the dynamic image is called a frame image.

As shown in FIG. 1, the imaging apparatus 1 includes a radiation generation device 11, a radiation irradiation control device 12, a radiation detection unit 13, a reading control device 14, and the like.

As shown in FIG. 2A, the radiation generation apparatus 11 includes a radiation source 111, a radiation source holding unit 112, a support base shaft 113, and the like.

The radiation source 111 is disposed at a position facing the radiation detector 131 across the subject M, and irradiates the subject M with radiation (X-rays) under the control of the radiation irradiation control device 12. The radiation source 111 is held by the radiation source holding unit 112 so as to be movable up and down along the support base axis 113, and at the time of imaging, from the floor to the focal position of the radiation source 111 based on the control from the radiation irradiation control device 12. The height (distance) is adjusted by a drive mechanism (not shown) so as to be the same as the height from the floor to the center of the radiation detector 131. The distance between the radiation source 111 and the radiation detector 131 is preferably 2 m or more.

The radiation irradiation control device 12 is connected to the imaging console 2 and controls the radiation generator 11 based on the radiation irradiation conditions input from the imaging console 2 to perform radiation imaging. The radiation irradiation conditions input from the imaging console 2 are, for example, pulse rate, pulse width, pulse interval, imaging start / end timing, X-ray tube current value, X-ray tube voltage value, filter type during continuous irradiation. Etc. The pulse rate is the number of times of radiation irradiation per second, and matches the frame rate described later. The pulse width is a radiation irradiation time per one irradiation. The pulse interval is the time from the start of one radiation irradiation to the start of the next radiation irradiation in continuous imaging, and coincides with a frame interval described later.

Further, the radiation irradiation control device 12 generates radiation so that the height from the floor to the focal position of the radiation source 111 is the same as the height from the floor to the center of the radiation detector 131 output from the reading control device 14. Control of each part of the apparatus 11 is performed.

2A, the radiation detection unit 13 includes a radiation detector 131, a detector holding unit 132, a support base 133, a patient fixing unit 134, and the like.

The radiation detector 131 is composed of a semiconductor image sensor such as an FPD. The FPD has, for example, a glass substrate or the like, detects radiation that has been irradiated from the radiation source 111 and transmitted through at least the subject M at a predetermined position on the substrate according to its intensity, and detects the detected radiation as an electrical signal. A plurality of pixels to be converted and stored are arranged in a matrix. Each pixel includes a switching unit such as a TFT (Thin-Film-Transistor). In addition to the direct type described above, an indirect type FPD having a scintillator that converts radiation into light may be used.

As shown in FIG. 2A, the radiation detector 131 is held by a detector holding unit 132 so as to be movable up and down along the support base shaft 133. At the time of imaging, the radiation detector 131 operates the subject M by operating a foot switch (not shown). The position (height from the floor surface) of the detector holding part 132 can be adjusted according to the height.

Also, the detector holding part 132 is provided with a patient fixing part 134. As shown in FIG. 2B, the patient fixing part 134 has at least a left upper fixing part 134a, an upper right fixing part 134b, a lower left fixing part 134c, and a lower right fixing part 134d. The upper left fixed part 134a is configured to be movable in parallel with the radiation irradiation surface of the radiation detector 131 from the left end side of the radiation detector 131 toward the right end direction. The same applies to the lower left fixing part 134c. The upper right fixed part 134b is configured to move from the right end side of the radiation detector 131 toward the left end direction by the same distance as the moving distance of the upper left fixed part 134a in parallel with the radiation irradiation surface of the radiation detector 131. Yes. The same applies to the lower right fixing part 134d. The position of the subject M can be fixed at the center of the radiation detector 131 by sandwiching the subject M between the upper left fixing portion 134a and the upper right portion fixing portion 134b, and the lower left fixing portion 134c and the lower right fixing portion 134d. As a result, it is possible to improve the analysis accuracy of the image analysis processing described later and the blood vessel detection accuracy. In addition, positioning reproducibility when shooting at different times for follow-up observation can be improved.

Note that the upper left fixed part 134a and the upper right fixed part 134b are positions (radiation detection) that can secure a region (direct X-ray region) directly irradiated with radiation on the radiation detector 131 for time-varying density correction described later. It is preferable to dispose a predetermined distance below the upper end of the vessel 131.

The reading control device 14 is connected to the imaging console 2. The reading control device 14 controls the switching unit of each pixel of the radiation detector 131 based on the image reading condition input from the imaging console 2 to switch the reading of the electrical signal accumulated in each pixel. Then, the image data is acquired by reading the electric signal accumulated in the radiation detector 131. This image data is a frame image. Then, the reading control device 14 outputs the acquired frame image to the photographing console 2. The image reading conditions are, for example, a frame rate, a frame interval, a pixel size, an image size (matrix size), and the like. The frame rate is the number of frame images acquired per second and matches the pulse rate. The frame interval is the time from the start of one frame image acquisition operation to the start of the next frame image acquisition operation in continuous shooting, and coincides with the pulse interval.

Here, the radiation irradiation control device 12 and the reading control device 14 are connected to each other, and exchange synchronization signals to synchronize the radiation irradiation operation and a series of image reading operations such as reset-accumulation-data reading-reset. It is supposed to let you. If necessary, dark image reading to reset may be performed before or after photographing, and may be synchronized by including them. Also, the reading control device 14 outputs height information (output value from the distance measuring sensor SE1) from the floor to the center of the radiation detector 131 to the radiation irradiation control device 12, and the floor to the radiation detector 131 The height to the center and the height from the floor to the focal position of the radiation source 111 are made to coincide.

[Configuration of the shooting console 2]
The imaging console 2 outputs radiation irradiation conditions and image reading conditions to the imaging apparatus 1 to control radiation imaging and radiographic image reading operations by the imaging apparatus 1, and also captures dynamic images acquired by the imaging apparatus 1. Displayed for confirmation of whether the image is suitable for confirmation of positioning or diagnosis.

As shown in FIG. 1, the photographing console 2 includes a control unit 21, a storage unit 22, an operation unit 23, a display unit 24, and a communication unit 25, and each unit is connected by a bus 26.

The control unit 21 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), and the like. The CPU of the control unit 21 reads the system program and various processing programs stored in the storage unit 22 in accordance with the operation of the operation unit 23, expands them in the RAM, and performs shooting control processing described later according to the expanded programs. Various processes including the beginning are executed to centrally control the operation of each part of the imaging console 2 and the radiation irradiation operation and the reading operation of the imaging apparatus 1.

The storage unit 22 is configured by a nonvolatile semiconductor memory, a hard disk, or the like. The storage unit 22 stores various programs executed by the control unit 21 and data such as parameters necessary for execution of processing by the programs or processing results. For example, the storage unit 22 stores a shooting control processing program for executing the shooting control process shown in FIG. The storage unit 22 stores radiation irradiation conditions and image reading conditions. Various programs are stored in the form of readable program code, and the control unit 21 sequentially executes operations according to the program code.

The operation unit 23 includes a keyboard having a cursor key, numeric input keys, various function keys, and the like, and a pointing device such as a mouse. The control unit 23 controls an instruction signal input by key operation or mouse operation on the keyboard. To 21. In addition, the operation unit 23 may include a touch panel on the display screen of the display unit 24. In this case, the operation unit 23 outputs an instruction signal input via the touch panel to the control unit 21.

The display unit 24 includes a monitor such as an LCD (Liquid Crystal Display) or a CRT (Cathode Ray Tube), and displays input instructions, data, and the like from the operation unit 23 in accordance with display signal instructions input from the control unit 21. To do.

The communication unit 25 includes a LAN adapter, a modem, a TA (Terminal Adapter), and the like, and controls data transmission / reception with each device connected to the communication network NT.

[Configuration of diagnostic console 3]
The diagnostic console 3 is a dynamic image processing device that acquires a dynamic image from the imaging console 2, displays the acquired dynamic image, and makes a diagnostic interpretation by a doctor.

As shown in FIG. 1, the diagnostic console 3 includes a control unit 31, a storage unit 32, an operation unit 33, a display unit 34, and a communication unit 35, and each unit is connected by a bus 36.

The control unit 31 includes a CPU, a RAM, and the like. The CPU of the control unit 31 reads out the system program and various processing programs stored in the storage unit 32 in accordance with the operation of the operation unit 33 and expands them in the RAM, and performs image analysis described later according to the expanded programs. Various processes including the process are executed to centrally control the operation of each part of the diagnostic console 3. The control unit 31 executes an image analysis process to be described later, thereby performing lung field region extraction means, blood vessel region recognition means, blood vessel region removal means, division means, calculation means, blood flow information calculation means, display means, and determination means. Realize.

The storage unit 32 is configured by a nonvolatile semiconductor memory, a hard disk, or the like. The storage unit 32 stores various programs including an image analysis processing program for executing image analysis processing by the control unit 31, parameters necessary for execution of processing by the program, or data such as processing results. These various programs are stored in the form of readable program codes, and the control unit 31 sequentially executes operations according to the program codes.

The operation unit 33 includes a keyboard having cursor keys, numeric input keys, various function keys, and the like, and a pointing device such as a mouse. The control unit 33 controls an instruction signal input by key operation or mouse operation on the keyboard. To 31. The operation unit 33 may include a touch panel on the display screen of the display unit 34, and in this case, an instruction signal input via the touch panel is output to the control unit 31.

The display unit 34 is composed of a monitor such as an LCD or CRT, and displays an input instruction, data, or the like from the operation unit 33 in accordance with an instruction of a display signal input from the control unit 31 as a display unit.

The communication unit 35 includes a LAN adapter, a modem, a TA, and the like, and controls data transmission / reception with each device connected to the communication network NT.

[Operation of Dynamic Image Processing System 100]
Next, the operation in the dynamic image processing system 100 will be described.

(Operation of the photographing apparatus 1 and the photographing console 2)
First, the photographing operation by the photographing apparatus 1 and the photographing console 2 will be described.

FIG. 3 shows a photographing control process executed by the control unit 21 of the photographing console 2. The photographing control process is executed in cooperation with the photographing control processing program stored in the control unit 21 and the storage unit 22.

First, the operation section 23 of the imaging console 2 is operated by the imaging technician, and patient information (patient name, height, weight, age, sex, etc.) of the imaging target (subject M) is input (step S1).

Next, the radiation irradiation conditions are read from the storage unit 22 and set in the radiation irradiation control device 12, and the image reading conditions are read from the storage unit 22 and set in the reading control device 14 (step S2). Here, the frame rate (pulse rate) is preferably set to 7.5 frames / second or more in consideration of a human heartbeat cycle.

Next, a radiation irradiation instruction by the operation of the operation unit 23 is on standby.

The imaging engineer prepares for imaging such as patient positioning in the imaging apparatus 1. Specifically, according to the height of the subject M (patient), the height of the detector holding unit 132 to which the radiation detector 131 is attached is adjusted by a foot switch (not shown), and the subject M is detected by the detector holding unit 132. The patient is fixed by the patient fixing part 134. Also, two or more X-ray non-transparent markers for body motion correction (here, two places, marker M1 and marker M2) are attached to the subject M. Then, the patient is instructed to perform rest ventilation (breathing) in a relaxed state. The patient may also be instructed to hold his breath to obtain a dynamic image with little movement of the diaphragm and ribs.

In the imaging apparatus 1, when the height of the detector holding unit 132 is adjusted by the operation of the imaging engineer, the distance from the floor to the center of the radiation detector 131 is acquired by the distance measuring sensor SE 1 and output to the reading control apparatus 14. Is done. In the reading control device 14, the output value of the distance measuring sensor SE1 is output to the radiation irradiation control device 12 as height information. In the radiation irradiation control device 12, a driving mechanism (not shown) is provided so that the value of the distance from the floor output from the distance measuring sensor SE2 to the focal position of the radiation source 111 is the same as the value output from the reading control device 14. When driven, the height of the radiation source holding unit 112 is adjusted.

When the positioning of the subject M is completed, the imaging engineer inputs a radiation irradiation instruction through the operation unit 23 of the imaging console 2.

When a radiation irradiation instruction is input by the operation unit 23 (step S3; YES), a photographing start instruction is output to the radiation irradiation control device 12 and the reading control device 14, and dynamic photographing is started (step S4). That is, radiation is emitted from the radiation source 111 at a pulse interval set in the radiation irradiation control device 12, and a frame image is acquired by the radiation detector 131. When a predetermined time has elapsed from the start of dynamic imaging, the control unit 21 outputs an instruction to end imaging to the radiation irradiation control device 12 and the reading control device 14, and the imaging operation is stopped.

Frame images acquired by shooting are sequentially input to the shooting console 2, and correction processing such as offset correction processing, gain correction processing, defective pixel correction processing, and lag (afterimage) correction processing using the above-described dark image is necessary. It is performed accordingly (step S5). However, these correction processes may be omitted to give priority to shortening the processing time. In this case, the process skips step S5 and proceeds to step S6. Next, the frame image is stored in the storage unit 22 in association with a number indicating the shooting order (step S6) and displayed on the display unit 24 (step S7). The imaging engineer confirms the positioning and the like based on the displayed dynamic image, and determines whether an image suitable for diagnosis is acquired by imaging (imaging OK) or re-imaging is necessary (imaging NG). Then, the operation unit 23 is operated to input a determination result.

When a determination result indicating that the shooting is OK is input by a predetermined operation of the operation unit 23 (step S8; YES), an identification ID for identifying the dynamic image or each of a series of frame images acquired by the dynamic shooting is displayed. Information such as patient information, examination target region, radiation irradiation condition, image reading condition, number indicating the imaging order is attached (for example, written in the header area of the image data in DICOM format) and diagnosed via the communication unit 25 Is transmitted to the console 3 (step S9). Then, this process ends. On the other hand, when a determination result indicating photographing NG is input by a predetermined operation of the operation unit 23 (step S8; NO), a series of frame images stored in the storage unit 22 is deleted (step S10), and this processing is performed. finish.

(Operation of diagnostic console 3)
Next, the operation in the diagnostic console 3 will be described.

When the diagnostic console 3 receives a series of frame images of the dynamic image from the imaging console 2 via the communication unit 35, it cooperates with the control unit 31 and the image analysis processing program stored in the storage unit 32. As a result, the image analysis processing shown in FIG. 4 is executed.

In the image analysis processing, first, preprocessing is performed (step S11).

FIG. 5 shows a flowchart of the preprocessing executed in step S11.

In the preprocessing, first, logarithmic conversion processing is performed, and pixel values (density values, hereinafter referred to as signal values) of each frame image of the dynamic image are converted from a true number to a logarithm (step S101).

Next, time-varying density correction processing (processing for removing the influence of irradiation dose variation at the time of acquisition of each frame image) is performed, and correction is performed so that the signal value of the direct X-ray region of each frame image becomes the same value. (Step S102).

In step S102, first, a reference frame image is selected from a series of frame images input from the photographing console 2, and a correction value of each frame image is calculated by the following (Equation 1).
(Expression 1) Correction value of each frame image = (Average signal value of direct X-ray region of each frame image) − (Average signal value of direct X-ray region of the reference frame image)
Next, in each frame image, the correction value calculated by (Equation 1) is subtracted from the signal value for each pixel.

Next, grid eye removal processing is performed on each frame image (step S103). The grid eye removal process is a process of removing a stripe pattern caused by the grid arrangement of the scattered radiation removal grid provided to remove scattered radiation. The grid eye removal process can be performed using a known technique. For example, each frame image is subjected to a frequency transform process such as discrete Fourier transform, followed by a low-pass filter process to remove a high frequency region including the frequency of the grid image and an inverse Fourier transform process. Yes (see Takayuki Ishida, “Introduction to Medical Image Processing”, 3.4 Removal of Vertical Striped Shadows by Grid of X-ray Images).

Next, body motion correction processing is performed on each frame image (step S104). In the body motion correction process, each frame image is rotated and translated so that the line segments of the two X-ray opaque markers M1 and M2 in the entire frame image coincide with each other, and alignment is performed.

Next, lung field extraction / division processing is performed on each frame image (step S105).

The lung field extraction / division process is performed according to the following procedures (1) to (4).
(1) An arbitrary frame image (in this case, the frame image with the first shooting order) is set as a reference image from a series of frame images.
(2) A lung field region is extracted from the reference image, and a substantially rectangular region circumscribing the extracted lung field region is divided into a plurality of small regions A1 as indicated by dotted lines in FIG. The small area A1 to be divided is, for example, 0.4 to 4 cm square.

Note that the lung field region extraction method may be any method. For example, a threshold value is obtained from a histogram of signal values of the reference image by discriminant analysis, and a region having a signal higher than the threshold value is primarily extracted as a lung field region candidate. Next, edge detection is performed in the vicinity of the boundary of the first extracted lung field region candidate, and the boundary of the lung field region can be extracted by extracting along the boundary the point where the edge is maximum in a small region near the boundary. it can.
(3) The position corresponding to the center point of each small area A1 of the reference image is extracted from the other frame images by the local matching process.

The local matching process can be performed by a method described in, for example, Japanese Patent Application Laid-Open No. 2001-157667. Specifically, first, the initial value of the counter n is set to 1, and the search area of each small area A1 in the frame image with the shooting order nth is set to the frame image with the shooting order nth. Here, each search area has the same center point (x, y), where the coordinates of the center point in each small area A1 in the nth frame image are (x, y). The vertical and horizontal widths are set to be larger than each small area A1 (for example, the vertical and horizontal widths are each doubled). Next, for each small area A1 of the nth frame image, a position having the highest matching degree in the search area set in the (n + 1) th frame image is calculated, and the position on the (n + 1) th frame image is calculated. Calculated as the corresponding position. As the degree of matching, a least square method or a cross-correlation coefficient is used as an index.

When it is calculated by the local matching processing which position (small area) of the (n + 1) th frame image corresponds to each small area A1 of the nth frame image in the shooting order, in the (n + 1) th frame image The coordinates (x ′, y ′) of the center point of each small area A1 are acquired as positions corresponding to the coordinates (x, y) of the center point of each small area A1 of the nth frame image and stored in the RAM. . The counter n is incremented and repeated until (n + 1)> the number of frame images.
(4) As shown by a solid line in FIG. 6, each frame image is divided into new small areas A2 having the central point of each small area A1 as a vertex.

Note that the local matching process may be performed on the frame image itself, or may be performed on an image extracted (emphasized) by a gradient filter or the like.

Furthermore, local matching processing may be performed with an image in which lung blood vessel shadows are emphasized or extracted. As a technique for emphasizing pulmonary vessel shadows, for example, “Constructing a filter bank for detecting circular and linear patterns in medical images”, IEICE Transactions D-II Vol.J87-D-II No.1 pp175- As described in 185, after multi-resolution decomposition, a pulmonary vessel shadow is emphasized by extracting an image of a resolution level corresponding to the pulmonary blood vessel from an image in which a circular / linear pattern is selectively emphasized for each resolution level. Images can be obtained. In addition, as described in, for example, “Automatic extraction procedure of blood vessel shadow from chest X-ray image using Deformable Model” MEDICAL IMAGING TECHNOLOGY Vol. 17 No. 5, September 1999 Extraction of pulmonary blood vessel shadows is possible by using a tree-like figure model that can be deformed such as expansion, merging, and division considering the shape of the shadow, and using a method of extracting a tree-like structure that maximizes the evaluation function . At this time, the division of the small region may be performed by dividing the reference image at equal intervals or by dividing the reference image into rectangular regions having a central characteristic point such as an extracted pulmonary blood vessel branch point. In addition, since the clavicle / rib area moves differently from the pulmonary vascular shadow, a part of the lung excluding the clavicle / rib area is recognized by recognizing the clavicle / rib from the reference image using a clavicle / rib shape model. The field region may be divided into small regions and local matching may be performed. In this way, when local matching was performed by dividing the reference image into rectangular regions centered on structurally characteristic points, or local matching was performed by dividing some lung field regions into small regions. In this case, after the following warping process, after the regions in which the same part of the lung field region is drawn are associated between the frame images, the lung field regions of the associated frame images may be divided at equal intervals.

Next, warping processing (non-linear distortion conversion processing) is performed on each frame image other than the reference image, and the shape of the lung field region of each frame image is matched with the reference image (step S106).

The warping process is performed as follows, for example.

First, assuming that the initial value of the counter n is 1, the center point (center pixel) of each small area A1 of the frame image of the nth shooting order and the center point of each corresponding small area A1 of the (n + 1) th frame image. Shift values (Δx, Δy) are calculated. Each shift value (Δx, Δy) is set to the center point of each small region A1 of the nth frame image (x, y), and the center point of each corresponding small region A1 of the (n + 1) th frame image (x ′, Y ′), it is obtained by calculating (Δx = x′−x, Δy = y′−y).

Next, a shift value (Δx, Δy) for each pixel of the (n + 1) th frame image is calculated by an approximation process using a two-dimensional 10th order polynomial using the obtained shift values (Δx, Δy). Then, each pixel of the (n + 1) th frame image is shifted based on the obtained shift value (Δx, Δy) of each pixel, and a new frame image is created separately from the frame image before the warping process. The The correspondence relationship of each pixel before and after the warping process in each frame image is stored in the RAM. This makes it possible to determine which pixel before the warping process corresponds to each pixel after the warping process.

Next, the counter n is incremented by 1, and the above processing is repeatedly executed until (n + 1)> the number of frame images.

Then, the integrated image G is created by adding the signal value of the pixel for each corresponding region of each frame image created by the reference image and the warping process (step S107), the preprocessing is completed, and the processing is illustrated in FIG. 4 moves to step S12. Here, since the amount of movement of the pulmonary blood vessel position due to pulsation is very small at the time of breath holding, the local image matching and warping processes are skipped, and the signal value of the pixel at the same position on the detector is added to the integrated image G To reduce the processing time. The integrated image G is an image with reduced noise and an image in which the blood vessel region can be easily recognized. Note that the integrated image G may be created by adding the signal values of corresponding pixels of each frame image and then dividing the result by the number of frames and averaging.

In step S12 of FIG. 4, a blood vessel recognition process is performed on the integrated image G (step S12). A blood vessel as used herein refers to a relatively thick pulmonary blood vessel in which blood flowing in peripheral blood vessels in the lung field is concentrated. For example, the right interlobar pulmonary artery, left interlobar pulmonary artery, upper right pulmonary vein, middle lobe vein, lower right Pulmonary vein, left upper pulmonary vein, left lower pulmonary vein, pulmonary artery of A ¹ -A ¹⁰ , pulmonary vein of V ¹ -V ¹⁰ .29).

Before executing the blood vessel recognition process, a GUI for the operator to designate a region to be recognized by the operator is displayed on the display unit 34, and the blood vessel recognition process is performed only for the region designated from the GUI. It is preferable because the time required for blood vessel recognition processing can be reduced and erroneous recognition can be reduced. As an example of a GUI for designating a region, for example, as shown in FIG. 7, when the integrated image G is displayed on the display unit 34 and two vertices are designated by the operation unit 33, the designated two vertices are displayed. A rectangular region that is a diagonal line is set as a region for blood vessel recognition. Alternatively, when three or more vertices are designated by the operation unit 33, a polygonal region surrounded by the designated vertices may be set as a region for blood vessel recognition. When the designated region protrudes from the lung field region, blood vessel recognition processing is performed only within the lung field region.

As the blood vessel recognition processing, for example, as described above, a method of extracting a linear structure using the maximum eigenvalue calculated from each element of the Hessian matrix (“filter bank for detecting circular / linear patterns in medical images” Construction ”IEICE Transactions D-II Vol.J87-D-II No.1 pp 175-185) can be used. In addition, by constructing a band division filter bank that generates each element of the Hessian matrix, it is possible to extract a linear structure at each resolution level, and it is possible to extract blood vessel regions having different sizes (diameters). . By performing blood vessel extraction at a resolution level coarser than the predetermined threshold, only blood vessels having a predetermined diameter or more can be extracted. In this method, the clavicle / rib is extracted at the same time as the blood vessel shadow, but the rib is recognized by using the clavicle / rib shape model and deleted from the extraction result. In addition, as described above, for example, as described above, a method of extracting a dendritic structure using a deformable dendritic figure model as a blood vessel structure model (“automatic extraction of blood vessel shadow from chest X-ray image using Deformable Model” By using the procedure “MEDICAL IMAGENG TECHNOLOGY Vol.17 No.5 September 1999), blood vessel shadows can be extracted. The region extracted by these methods is recognized as a blood vessel region.

Also, a GUI for the operator to designate a part of the blood vessel portion to be recognized may be displayed on the display unit 34, and the blood vessel recognition process may be performed based on the designated position information. The blood vessel recognition process in this case can be performed as follows.

First, the integrated image G is displayed on the display unit 34. As shown in FIG. 8 (a), a part of a blood vessel (indicated by BV in FIGS. 8 (a) to 8 (f)) to be recognized by the operation unit 33 on the integrated image G in the traveling direction of the blood vessel. When designated along, in the blood vessel recognition processing, as shown in FIGS. 8B and 8C, the direction perpendicular to the traveling direction designated by the operation unit 33 (the arrow direction in FIG. 8B). A signal value profile is generated. Next, as shown in FIG. 8C, the width W of the portion including the position P designated by the operation unit 33 and where the signal value is lower than the predetermined threshold TH1 with respect to the surroundings counts the number of pixels. Is calculated by When the calculated width W is equal to or greater than a predetermined value, the low signal region is recognized as a blood vessel region. Further, when a blood vessel is recognized, as shown in FIG. 8D, an ROI centered on the end of the recognized blood vessel region is set, and is calculated by a discriminant analysis method or the like from a histogram of signal values in the ROI. Binarization is performed based on the threshold value. The size of the ROI is determined according to the width W. For example, an ROI having a side length of α × W (α is a predetermined coefficient) is set. Then, as shown in FIG. 8 (e), a low signal region that includes the center of the ROI and is equal to or less than the calculated threshold value is set as a blood vessel candidate region, and the size of the blood vessel candidate region is a predetermined value with respect to the area of the ROI. When it is within the range, it is recognized as a blood vessel region. Further, when the newly recognized blood vessel region touches the end of the ROI, as shown in FIG. 8 (f), a new ROI centered on that end is set and the recognition of the blood vessel region is repeated in the same manner. .

Next, in each frame image after the warping process, the blood vessel region removal processing is executed, and the signal value of each pixel in the region located at the same position as the blood vessel region recognized from the integrated image G is in the vicinity of the pixel in the blood vessel region. It is replaced with the average value of the signal values in the non-blood vessel region (step S13).

In the blood vessel region removal processing, for example, a pixel excluding the blood vessel region and the lung field region with respect to a block of N pixels × N pixels (N is an odd number, for example, 5, 11, etc.) centering on the pixel of the blood vessel region. The average value is obtained, and the signal value of each pixel in the blood vessel region is replaced with the obtained average value. For example, when a block of 5 pixels × 5 pixels is used, if the shaded area shown in FIG. 9 is a blood vessel area, the P22 pixels at the center are P00, P01, P02, P10, P11, P12, P20, It is replaced with the average value of the signal values of P21, P30, P31, P40, P41. When N is small, first, the signal value is replaced only for the pixels at the boundary of the non-blood vessel region in the blood vessel region, and then, among the pixels that have not been replaced yet, The replacement of the signal value for only the pixels may be sequentially repeated until the signal values of all the pixels in the blood vessel region are replaced.

By the processing in step S13, the blood vessel region is removed from the lung field region as shown in FIG.

Next, a high-pass filter is applied to each frame image from which the blood vessel region has been removed, and only high-frequency components are extracted (step S14). For example, the cut-off frequency is 1 Hz, and only high frequency components of 1 Hz or more corresponding to the human heartbeat cycle are extracted.

Next, in each frame image after the high-pass filter processing, the lung field region is divided into a plurality of block regions (step S15), the signal values in the regions are added for each block region, and the added signal value (added signal value) The time change is calculated (step S16). For example, it is divided into six block areas, namely, upper right lung field R1, middle right lung field R2, lower right lung field R3, upper left lung field L1, middle left lung field L2, and lower left lung field L3 (see FIG. 11). The addition signal value is calculated for each block area, and the time change is calculated.

FIG. 12 shows the time change of the added signal value in a certain block area. In FIG. 12, the horizontal axis represents the elapsed time t from the start of dynamic imaging, and the vertical axis represents the added signal value in the block area.

Next, a signal change amount corresponding to one heartbeat period is calculated for each block region (step S17). Specifically, as shown in FIG. 12, with respect to the time change of the signal value of each block region, the maximum value equal to or greater than a first threshold value that is predetermined and the second value that is predetermined. A minimum value equal to or less than the threshold is extracted, and a signal change amount corresponding to one heartbeat period is calculated by calculating a difference between the maximum value and the next minimum value. For a plurality of heartbeat cycles, a signal change amount corresponding to one heartbeat cycle is calculated (D0 to D3 in FIG. 12), and an average value of the calculated signal change amounts is calculated. The signal value in each block area changes according to the blood flow volume in the block area. Further, from each frame image, a blood vessel region where blood flowing through peripheral blood vessels is concentrated is removed, and a frequency component of blood flow is extracted by a high-pass filter. Therefore, the calculated signal change amount corresponding to one heartbeat period in each block area is information indicating the blood flow rate of the peripheral blood vessels in one heartbeat period in the block.

Next, for each block region, blood flow information of peripheral blood vessels in each block region is calculated based on the signal change amount corresponding to one heartbeat period calculated in step S17 (step S18). As the blood flow information of the peripheral blood vessels in each block region, here, the ratio of the blood flow rate of the peripheral blood vessels in each block region to the blood flow rate of the peripheral blood vessels in the entire lung field is calculated. For example, it is calculated by (signal change amount corresponding to one heartbeat period in the block region / total signal change amount corresponding to one heartbeat period in all block regions) × 100 (%).

Next, the calculated ratio is displayed on the display unit 34 as the blood flow information of the peripheral blood vessels in each block region (step S19).

FIG. 11 shows an example of blood flow information displayed on the display unit 34 in step S19. As shown in FIG. 11, the display unit 34 displays the blood flow information of the peripheral blood vessels in each block region as numerical values, and a series of frame images are displayed as a moving image. Each block region displayed as a moving image is displayed as a peripheral blood vessel. The color of the saturation according to the value of the blood flow information (or the luminance value according to the value of the blood flow information of the peripheral blood vessel) is displayed. In this way, each block area is displayed with a color of saturation corresponding to the value of blood flow information of the peripheral blood vessels, so that the doctor can grasp the distribution of blood flow in the peripheral blood vessels of each block area at a glance. It becomes possible to do.

Next, it is determined whether or not the blood flow information of the peripheral blood vessels in each block region is below a predetermined threshold value. If it is determined that the block region is below, it is determined that the block region is abnormal (step S20). If there is a block area determined to be abnormal, the corresponding block area on the series of frame images displayed as moving images is displayed in an identifiable manner (for example, by emphasizing the outline) (step S21). ). FIG. 13 shows an example in which block areas determined to be abnormal are displayed in an identifiable manner.

As described above, according to the diagnostic console 3 according to the present invention, the control unit 31 extracts a lung field region from each of a plurality of frame images showing the dynamics of the chest, and blood vessels in the extracted lung field region Region recognition processing is performed to remove the recognized blood vessel region from each frame image. Then, the lung field region of each frame image from which the blood vessel region is removed is divided into a plurality of block regions, the amount of change in the pixel signal value in each block region is calculated, and the calculated pixel signal value in each block region Based on the change amount, peripheral blood flow information existing in each block region is calculated and displayed on the display unit 34.

Therefore, it is possible to prevent the blood flow volume in the peripheral blood vessels from being calculated larger than the actual blood flow due to signal changes on the pulmonary blood vessels, so that blood flow information flowing through the peripheral blood vessels in the lung field region can be accurately calculated. It becomes. Further, blood flow information similar to pulmonary blood flow scintigraphy can be acquired without administering a radioisotope into the body of the subject M.

In the blood vessel region recognition processing, a GUI is provided for the operator to specify a part of the blood vessel region to be recognized, and the blood vessel region recognition processing is performed based on the region specified by the GUI, thereby The pulmonary blood vessels can be accurately recognized.

In the blood vessel region recognition processing, a GUI is provided for the operator to specify a region for performing the blood vessel region recognition processing, and the blood vessel region recognition processing is performed only for the region specified by the GUI, thereby reducing the blood vessel recognition processing time. It can be shortened and the recognition accuracy can be improved.

In addition, based on the blood flow information of the peripheral blood vessels in each block area, it is determined whether or not the blood flow in the peripheral blood vessels in each block area is abnormal, and the position of the block area determined to be abnormal is displayed as a moving image. Since the information is displayed on the frame image so as to be identifiable, it is possible for the doctor to easily grasp the portion where the blood flow in the peripheral blood vessels is abnormal.

Note that the description in the present embodiment described above is an example of a suitable dynamic image processing system according to the present invention, and the present invention is not limited to this.

For example, in the above embodiment, as a method for displaying blood flow information of peripheral blood vessels, a series of frame images are displayed as moving images, and each block region in the frame images displayed as moving images corresponds to the value of blood flow information of peripheral blood vessels. The block area determined to be abnormal is displayed in an identifiable color (or luminance value corresponding to the blood flow information value of the peripheral blood vessel) and is displayed in an identifiable manner. Display one of the images (for example, a reference image) or a still image such as the integrated image G, and display each block area in a color corresponding to the value of blood flow information of peripheral blood vessels on these images Alternatively, an abnormal block area may be displayed in an identifiable manner.

For example, 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 other computer-readable media, 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 image processing system 100 can be changed as appropriate without departing from the spirit of the present invention.

DESCRIPTION OF SYMBOLS 100 Dynamic image processing system 1 Imaging device 11 Radiation generation apparatus 111 Radiation source 112 Radiation source holding part 113 Support base axis 12 Radiation irradiation control apparatus 13 Radiation detection part 131 Radiation detector 132 Detector holding part 133 Support base axis 134 Patient fixing part 134a Upper left Section fixing section 134b upper right section fixing section 134c lower left section fixing section 134d lower right section fixing section 14 reading control device 2 photographing console 21 control section 22 storage section 23 operation section 24 display section 25 communication section 26 bus 3 diagnostic console 31 control section 32 storage unit 33 operation unit 34 display unit 35 communication unit 36 bus

Claims (8)

1. Lung field region extraction means for extracting a lung field region from each of a plurality of frame images indicating the dynamics of the chest,
   Blood vessel region recognition means for performing blood vessel region recognition processing in the extracted lung field region of each frame image;
   A blood vessel region removing unit that removes the blood vessel region recognized by the blood vessel region recognizing unit from each frame image;
   A dividing unit for dividing the lung field region into a plurality of block regions in each frame image from which the blood vessel region has been removed by the blood vessel region removing unit;
   Calculating means for calculating a change amount of a pixel signal value in each block region between the frame images from which the blood vessel region has been removed by the blood vessel region removing unit;
   Blood flow information calculating means for calculating blood flow information of peripheral blood vessels existing in each block area based on a change amount of a pixel signal value in each block area calculated by the calculating means;
   A dynamic image processing system comprising:
2. An operation means for an operator to specify a part of a blood vessel region to be recognized by the blood vessel region recognition means;
   The dynamic image processing system according to claim 1, wherein the blood vessel region recognition unit performs a blood vessel region recognition process based on a region designated by the operation unit.
3. In the extracted lung field region, comprising an operation means for an operator to specify a region for performing blood vessel recognition processing by the blood vessel region recognition means,
   The dynamic image processing system according to claim 1, wherein the blood vessel region recognition unit performs a blood vessel region recognition process only on a region designated by the operation unit.
4. Display means for displaying blood flow information calculated by the blood flow information calculating means on a display unit;
   The dynamic image processing system according to any one of claims 1 to 3, further comprising:
5. Based on the blood flow information of the peripheral blood vessels present in each block region, comprising a determination means for determining whether the blood flow of the peripheral blood vessels in each block region is abnormal,
   The dynamic image processing system according to claim 4, wherein the display unit displays the position of the block area determined to be abnormal by the determination unit on the display unit so as to be identifiable on a frame image displayed as a moving image.
6. The dynamic state according to any one of claims 1 to 5, wherein the blood vessel region removing unit replaces the pixel signal value of the recognized blood vessel region with a value determined based on a pixel signal value of a non-blood vessel region in the vicinity thereof. Image processing system.
7. Computer
   Lung field extraction means for extracting a lung field from each of a plurality of frame images showing the dynamics of the chest,
   A blood vessel region recognition means for performing a blood vessel region recognition process in the extracted lung field region of each frame image;
   A blood vessel region removing unit that removes the blood vessel region recognized by the blood vessel region recognizing unit from each frame image;
   A dividing unit for dividing the lung field region into a plurality of block regions in each frame image from which the blood vessel region has been removed by the blood vessel region removing unit;
   Calculating means for calculating a change amount of a pixel signal value in each block region between frame images from which the blood vessel region has been removed by the blood vessel region removing unit;
   Blood flow information calculating means for calculating blood flow information of peripheral blood vessels existing in each block area based on the amount of change in the pixel signal value in each block area calculated by the calculating means;
   Program to function as.
8. The computer,
   Display means for displaying blood flow information calculated by the blood flow information calculating means on a display unit;
   The program of Claim 7 for functioning as.

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