JP6418881B2 - Parameter processing apparatus, parameter processing method, and parameter processing program - Google Patents

Description

  The present invention relates to a parameter processing device, a parameter processing method, and a parameter processing program.

  As one of the heart diseases, valvular heart disease is known. As heart valve disease, aortic valve stenosis (AS) and aortic regurgitation (AR) are known. FIG. 14 (A) is a left ventricular short-axis cross-sectional view of the heart diagnosed as aortic valve stenosis. FIG. 14B is a left ventricular short-axis cross-sectional view of the heart diagnosed with aortic regurgitation.

  Aortic stenosis is a disease in which the aortic valve does not open sufficiently and blood is hardly ejected from the left ventricle. Aortic insufficiency is a disease in which the aortic valve does not close sufficiently and blood ejected from the left ventricle is likely to flow backward. When aortic stenosis or aortic regurgitation is diagnosed, for example, a procedure for replacing the aortic valve with an artificial valve is performed. In FIG. 15, the position of the arrow tip corresponds to the position of the aortic valve in the heart.

  Known methods for diagnosing valvular heart disease include, for example, echocardiography, cardiac catheterization, and cardiac CT (Computed Tomography) examination.

  In echocardiography, images of the heart and large blood vessels are extracted by an ultrasound examination apparatus that uses ultrasound. The examiner can check the blood flow by echocardiography. One of the echocardiographic examinations is TTE (Transoracic echocardiogram). In TTE, an ultrasonic contact is applied to each part of the heart, and an image around the heart is extracted. TTE is also referred to as transthoracic echocardiography.

  In a cardiac catheter test, a catheter is inserted into a heart from a blood vessel such as a thigh, and the blood pressure, cardiac output, and oxygen content in the blood are measured by a cardiac catheter test device, for example. . In cardiac catheterization, a contrast medium is injected into the body as needed.

  In the cardiac CT examination, X-rays are irradiated in the vicinity of the heart, and the cardiac CT examination apparatus collects the difference in the X-ray dose that has passed through the body as data, thereby imaging the heart. In cardiac catheterization, a contrast medium is injected into the body as needed.

  As a cardiac CT examination, there is a cardiac CT angiography (CCTA: Cardiac Computed Tomography Angiography). By using CCTA, the person in charge of the examination can easily recognize the movement of the entire heart from the video created from volume data consisting of multiple phases, and directly observe the movement and shape of the valve (aortic valve, etc.) near the heart. it can. For example, the person in charge of the inspection can visually check the size of the ventricle at the maximum time and the size of the ventricle at the minimum time, and can confirm the left ventricular ejection fraction (LVEF).

  Further, a medical image processing apparatus that assists in the treatment policy for aortic stenosis using the volume data of the CT image is known (see, for example, Patent Document 1). This medical image processing apparatus determines the influence of the calcified portion on the change in the valve opening area by separately interpolating and synthesizing the aortic valve body region and the calcified region in the volume data image.

JP 2013-106870 A

  In each examination of the conventional valvular heart disease, the burden on the examiner and the patient may be large. In addition, the accuracy of the parameters obtained by the examination is insufficient, and the diagnostic accuracy of valvular heart disease may be reduced. In addition, some of the results varied depending on the person in charge of the inspection.

  The present invention has been made in view of the above circumstances, and a parameter processing apparatus and a parameter processing method that can reduce the burden on the person in charge of the examination and the patient in the examination of valvular heart disease and improve the accuracy of parameters obtained by the examination. And a parameter processing program.

The parameter processing device of the present invention includes a region of the heart in a plurality of phases arranged in time series, an image acquisition unit that acquires volume data obtained by a CT (Computed Tomography) device, and the volume data of each phase, A cross-sectional image deriving unit for deriving a cross-sectional image cut at a predetermined cross section, a contour setting unit for setting a contour of a ventricular myocardium in the cross-sectional image of each phase, and a cardiac cycle based on the contour of each phase A change information deriving unit for deriving change information indicating a change in the length of the contour during a predetermined period or a predetermined time point; and a display unit for displaying the change information.

The parameter processing method of the present invention is a parameter processing method in a parameter processing apparatus, including a step of acquiring volume data obtained by a CT apparatus, including a heart region in a plurality of phases arranged in time series, In the volume data, a step of deriving a cross-sectional image cut at a predetermined cross-section, a step of setting a contour of a ventricular myocardium in the cross-sectional image of each phase, and a cardiac cycle based on the contour of each phase A step of deriving change information indicating a change in length of the contour during a predetermined period or a predetermined time, and a step of displaying the change information on a display unit.

Parameter processing method of the present invention, when viewed including the region of the heart at a plurality of phases arranged in series, a step of acquiring volume data obtained by the CT apparatus, in the volume data for each phase, cut at a predetermined cross section A step of deriving a cross-sectional image obtained, a step of setting a contour of a ventricular myocardium in the cross-sectional image of each phase, and a length of the contour in a predetermined period or a predetermined time point in a cardiac cycle based on the contour of each phase It is a parameter processing program for causing a computer to execute a step of deriving change information indicating a change in height and a step of displaying the change information on a display unit .

  According to the present invention, in testing for valvular heart disease, the burden on the person in charge of the examination and the patient can be reduced, and the accuracy of parameters obtained by the examination can be improved.

1 is a block diagram illustrating a configuration example of an image processing apparatus according to an embodiment. (A)-(C) The schematic diagram which shows the triaxial cross-sectional image of the volume data containing the area | region of the heart in embodiment. The schematic diagram which shows an example of the outline of the left ventricular myocardium in the left ventricular short-axis cross-sectional image in embodiment The schematic diagram which shows an example of the outline of the left ventricular myocardium in the left ventricle vertical long-axis cross-sectional image in embodiment The schematic diagram which shows an example of the outline of the left ventricular myocardium in the left ventricular horizontal long-axis cross-sectional image in embodiment The schematic diagram which shows the image of the relationship between the time position and outline length in 1 period of the cardiac cycle in embodiment The schematic diagram which shows the example of a change of the outline length in the left ventricular short-axis cross-sectional image in embodiment (A), (B) The schematic diagram which shows the example of distribution of the change speed and change acceleration of the length of the outline in the left ventricular short-axis cross-sectional image in embodiment (A), (B) The schematic diagram which shows the example of distribution of the change speed and change acceleration of the length of the length in the left ventricular vertical-long-axis cross-sectional image in embodiment in embodiment (A), (B) The schematic diagram which shows the example of distribution of the change speed and change acceleration of the length of the outline in the left ventricle horizontal long-axis cross-sectional image in embodiment 6 is a flowchart illustrating an operation example of the image processing apparatus according to the embodiment. (A)-(C) The schematic diagram for demonstrating the case where the image processing apparatus in embodiment does not perform phase interpolation (A)-(C) The schematic diagram for demonstrating the case where the image processing apparatus in embodiment performs phase interpolation (A) Left ventricular short-axis cross section of heart diagnosed with aortic regurgitation, (B) Left ventricular short axis cross-section of heart diagnosed with aortic regurgitation Schematic diagram showing the position of the aortic valve in the heart

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

(Background to obtaining one embodiment of the present invention)

  In conventional echocardiography, the burden on the patient at the time of the examination is small, but the image acquired by the ultrasound examination apparatus varies depending on the angle of the ultrasound contactor that contacts the heart part from outside the body. Therefore, the examination result obtained from the image may change depending on the person in charge of the echocardiography examination.

  In a conventional cardiac catheter test, various measurements cannot be performed unless the catheter is inserted into the patient's body and the probe is placed near the heart valve. Therefore, the burden on the inspector and the patient is large, and there is a possibility of invasion to the patient.

  In a conventional cardiac CT examination, it is difficult for the person in charge of the examination to check the backflow of blood ejected from the left ventricle and the pressure in the left ventricle from the CT image. In addition, it takes time and effort to evaluate the inspection result using the CT image.

  The medical image processing apparatus described in Patent Document 1 does not acquire information on left ventricular pressure and left ventricular ejection fraction, which are important indicators for diagnosis of cardiac valvular disease, so that the diagnostic accuracy of cardiac valvular disease may be reduced There is sex.

  Hereinafter, a parameter processing device, a parameter processing method, and a parameter processing program that can reduce the burden on the person in charge of the examination and the patient and can improve the accuracy of the parameters obtained by the examination in the examination of valvular heart disease will be described.

(Embodiment)
FIG. 1 is a block diagram illustrating a configuration example of an image processing apparatus 100 according to the embodiment. The image processing apparatus 100 includes an image acquisition unit 110, an operation unit 120, a display unit 130, a control unit 140, and a storage unit 150. Note that the registration processing unit 142 and the phase interpolation processing unit 145 may be omitted. The image processing apparatus 100 acquires a CT image from a CT (Computed Tomography) apparatus 200 and performs processing on the acquired CT image.

  The control unit 140 includes a setting unit 141, a registration processing unit 142, a contour information processing unit 143, an image derivation unit 144, and a phase interpolation processing unit 145. The control unit 140 includes, for example, a CPU (Central Processing Unit) or a DSP (Digital Signal Processing). The control unit 140 includes, for example, a ROM (Read Only Memory) or a RAM (Random Access Memory). The CPU or DSP realizes each function of the control unit 140 by executing an image processing program stored in the ROM or RAM.

  The CT apparatus 200 irradiates a living body with X-rays and captures an image (CT image) using the difference in X-ray absorption by tissues in the body. A plurality of CT images are taken in time series. In the present embodiment, each time point arranged in time series is also referred to as a phase. The CT apparatus 200 captures a CT image in each phase. The CT image forms volume data including information on an arbitrary location inside the living body. By capturing a CT image, a pixel value (CT value) of each pixel (voxel) in the CT image is obtained.

  Further, the CT apparatus 200 may generate a three-dimensional image from volume data using volume rendering as a CT image. The CT apparatus 200 may generate a three-dimensional moving image (four-dimensional image) by arranging a three-dimensional image in time series as a CT image. Note that the generation of the three-dimensional image and the four-dimensional image may be performed by the image processing apparatus 100.

  The image acquisition unit 110 acquires a CT image from the CT apparatus 200. For example, the image acquisition unit 110 may acquire a CT image from the CT apparatus 200 by communication via a wired line or a wireless line, or may acquire via an arbitrary storage medium (not shown).

  The image deriving unit 144 derives a cross-sectional image (two-dimensional image) showing a state cut by a plurality of cross sections from the volume data of the CT image acquired by the image acquisition unit 110. For example, the image deriving unit 144 derives cross-sectional images of three cross sections (a left ventricular short-axis cross section, a left ventricular vertical long-axis cross section, and a left ventricular horizontal long-axis cross section). The image deriving unit 144 is an example of a cross-sectional image deriving unit.

  FIGS. 2A to 2C are schematic diagrams showing three-axis cross-sectional images (cross-sectional images) of volume data including a heart region. FIG. 2A illustrates that the volume data BD is cut by the left ventricular short-axis cross section D1, and a left ventricular short-axis cross-sectional image is formed. FIG. 2B illustrates that the volume data BD is cut by the left ventricular vertical long-axis cross section D2, and a left ventricular vertical long-axis cross-sectional image is formed. FIG. 2C illustrates that the volume data BD is cut by the left ventricle horizontal long-axis cross section D3 to form a left ventricle horizontal long-axis cross-sectional image.

  The operation unit 120 includes, for example, a touch panel, a pointing device (for example, a mouse), and a keyboard. The operation unit 120 receives an arbitrary input operation from a user (for example, a doctor or a radiologist) of the image processing apparatus 100.

  For example, in the left ventricular short-axis cross-sectional image 10 of the volume data, the operation unit 120 receives an input operation for designating the contour 13 (white dotted line portion in FIG. 3) of the myocardium 12 (left ventricular myocardium) surrounding the left ventricle 11. (See FIG. 3).

  FIG. 3 is a schematic diagram showing an example of the contour 13 of the left ventricular myocardium in the left ventricular short-axis cross-sectional image 10. In FIG. 3, the right ventricle 14 is located on the left side of the myocardium 12, and the lung 15 (black in FIG. 3) is located on the upper right side of the myocardium 12. The left ventricle 11 contains a lot of blood containing a contrast agent and has a high CT value.

  For example, in the left ventricular vertical long-axis cross-sectional image 20 of the volume data, the operation unit 120 includes the outer wall of the left ventricle 11 from the base 21 to the apex 22 in the lower wall 11a of the left ventricle 11 (the outer peripheral end of the myocardium 12). An input operation for designating the contour 23 (white dotted line arrow portion in FIG. 4) along the line is accepted (see FIG. 4). FIG. 4 is a schematic diagram showing an example of the contour 23 of the left ventricular myocardium in the left ventricular vertical long-axis cross-sectional image 20. The contour 23 in FIG. 4 corresponds to the contour of the left ventricular myocardium in FIG.

  For example, in the left ventricle horizontal long-axis cross-sectional image 30 of the volume data, the operation unit 120 is provided on the outer wall of the left ventricle 11 from the apex 31 to the base 32 on the side wall 11c of the left ventricle 11 (the outer peripheral end of the myocardium 12). An input operation for designating the contour 33 along the white dotted line arrow portion in FIG. 5 is accepted (see FIG. 5). FIG. 5 is a schematic diagram showing an example of the contour 33 of the left ventricular myocardium in the left ventricular horizontal long-axis cross-sectional image 30. The contour 33 in FIG. 5 corresponds to the contour of the left ventricular myocardium in FIG.

  For example, when an input operation is received by the operation unit 120, the setting unit 141 performs various settings based on the input information. For example, the setting unit 141 sets the contours 13, 23, and 33 received by the operation unit 120. The set contours 13, 23, and 33 are displayed at designated positions in the left ventricular short-axis cross-sectional image, left ventricular vertical long-axis cross-sectional image, and left ventricular horizontal long-axis cross-sectional image.

  Note that the setting unit 141 may perform contour extraction by software instead of the input operation by the operation unit 120. In this case, for example, the control unit 140 may extract the positions of the left ventricle 11, the myocardium 12, the heart base 21, and the apex 22 in each cross-sectional image, and derive and set the contour based on the positions of the respective units. Good. The setting unit 141 is an example of a contour setting unit.

  The registration processing unit 142 performs registration on a plurality of images (two-dimensional images or three-dimensional images). In registration, alignment (position adjustment) of related areas (for example, the heart area) is performed. In this case, the registration processing unit 142 may perform a three-dimensional alignment in consideration of a three-dimensional image, or may perform a two-dimensional alignment in consideration of a two-dimensional image.

  For example, in the volume data including a plurality of phases, the registration processing unit 142 performs registration between the phases.

As a specific method of registration, for example, a known method (see, for example, Patent Document 1) is used.
(Reference Patent Document 1: US Pat. No. 8311300)

  The contour information processing unit 143 derives (for example, calculates) the lengths (contour lengths) of the contours 13, 23, and 33 in each cross-sectional image set by the setting unit 141. The contour information processing unit 143, for example, based on the lengths of the contours 13, 23, and 33 on the screen of the display unit 130 and the physical size information of the voxels included in the volume data, The length 33 (actual length) is derived. In addition, the contour information processing unit 143 derives change information indicating changes in the lengths of the contours 13, 23, and 33 in the cardiac cycle. The contour information processing unit 143 generates a screen related to change information. Thereby, the user can analyze the information on the contour length obtained by the contour information processing unit 143 and diagnose various medical conditions. The contour information processing unit 143 is an example of a change information deriving unit.

  For example, the contour information processing unit 143 uses the contours 13, 23, and 33 set in the cross-sectional images of the respective phases, based on the contours 13, 23, and 33, the time change rate (the contour length of the contours 13, 23, and 33 when the heart contracts). (Change rate) is derived (for example, calculated).

  For example, the contour information processing unit 143 sets the time change rate of the time change rate of the length of the contours 13, 23, and 33 in the systole of the heart based on the contours 13, 23, and 33 set in the cross-sectional images of each phase. The (contour length change acceleration) is derived (for example, calculated).

  In addition, the contour information processing unit 143 generates a graph indicating the lengths of the contours 13, 23, and 33 in each cross-sectional image, for example. (See FIGS. 6 and 7).

  Note that the contour information processing unit 143 uses the information on the change related to the area of the region surrounded by the contour 13 of the left ventricular myocardium instead of the information on the change related to the length of the contours 13, 23, and 33 of the left ventricular myocardium. It may be derived. That is, the contour information processing unit 143 calculates the change rate of the area of the region when the heart contracts and the change acceleration of the area of the region during the systole based on the contour 13 set in the cross-sectional image of each phase. It may be derived.

The phase interpolation processing unit 145 interpolates phases between a plurality of phases in which volume data is generated, and derives volume data of the interpolated phases (phase interpolation). That is, in phase interpolation, volume data at a time point when no volume data exists is virtually generated. As a specific method of phase interpolation, for example, a known method (see, for example, Reference Patent Document 2) is used. Details of the phase interpolation will be described later.
(Reference Patent Document 2: Japanese Patent No. 5408493)

  The display unit 130 includes, for example, an LCD (Liquid Crystal Display) and displays various types of information. The display unit 130 displays various images (for example, a two-dimensional image (cross-sectional image) of a CT image, a three-dimensional image (still image), a four-dimensional image (moving image), and a two-dimensional image with an outline). In addition, the display unit 130 displays, for example, change information, and graphs and distribution diagrams showing change information (for example, change acceleration, change speed) based on changes in the contours 13, 23, and 33 of the left ventricular myocardium over time. indicate. The display unit 130 displays various operation screens.

  The storage unit 150 stores various images (for example, two-dimensional images (cross-sectional images) of CT images, three-dimensional images (still images), four-dimensional images (moving images)), various data, various programs, and various information (for example, manual or automatic). (Setting information set automatically).

  Next, an example of changes in the contour length in each cross-sectional image will be described.

  FIG. 6 is an image diagram showing the relationship between time (time position) and contour length in one cardiac cycle. One cardiac cycle includes a diastole and a systole. When the cardiac cycle is repeated, the diastole and the systole are repeated in turn. In the diastole, the capacity of the left ventricle 11 is maximized, that is, the length of each contour 13, 23, 33 is the longest. During the systole, the volume of the left ventricle 11 is minimized, that is, the length of each contour 13, 23, 33 is the shortest. In addition, the time position in 1 period of a cardiac cycle is shown by 0-100 (%), and shows the phase in a cardiac cycle.

  The contour information processing unit 143 derives the changing speed of the lengths of the contours 13, 23, and 33 in each cross section when the heart contracts. The time of contraction of the heart is a predetermined period or a predetermined time point from the diastole to the systole. The rate of change of the lengths of the contours 13, 23, and 33 when the heart contracts is such that blood introduced into the left ventricle 11 during the early diastole is ejected from the left ventricle 11 during the end-systole. Since it corresponds to the ratio, it can be said that it corresponds to the left ventricular ejection fraction.

  The change rate of the length of the contours 13, 23, 33 is, for example, the average value or the minimum value (negative value) of the instantaneous change rate at a predetermined moment (time point) from the diastole (time t1) to the systole (time t2). The absolute value of the value may be maximum). In FIG. 6, at the time t3, the instantaneous change rate of the AS becomes the minimum. The rate of change is determined by, for example, the slopes of the contours 13, 23, and 33 in the diastole (time t1) and the lengths of the contours 13, 23, and 33 in the systole (time t2) (slope of the dotted line L1). May be shown.

  The contour information processing unit 143 derives the change acceleration of the lengths of the contours 13, 23, and 33 during the systole of the heart. From this change acceleration, the rate of change in ejection rate at which the left ventricle 11 ejects blood in the systole can be inferred, and it can be said that this corresponds to the pressure in the left ventricle in the systole.

  The change acceleration of the lengths of the contours 13, 23, and 33 may be, for example, instantaneous acceleration in the systole (time t2). The change acceleration may be an average value or a maximum value of the instantaneous acceleration at a predetermined moment (time point) included in the vicinity of the systole (time t2). The instant of the maximum value may deviate from the time t2. This average value indicates the change acceleration in a predetermined period near the systole. In this embodiment, the change acceleration mainly in the systole is exemplified.

  In FIG. 6, there is one contour length change graph, but since there are three contours 13, 23, and 33 in each cross-sectional image, there are three contour length change graphs. In the three graphs, the change speed and the change acceleration exist as parameters one by one, so there are six parameters in total. The contour information processing unit 143 derives these six parameters.

  In FIG. 6, the change in the length of the contours 13, 23, and 33 in the cardiac cycle is shown in a graph. However, the change in the area of the region surrounded by the contour 13 in the cardiac cycle may be shown in a graph.

  Next, changes in the lengths of the contours 13, 23 and 33 of the myocardium 12 of the actual subject in each cross-sectional image will be described. Here, the CT apparatus 200 actually captured a CT image near the subject's heart, and the image processing apparatus 100 analyzed changes in the lengths of the contours 13, 23, and 33 in each cross-sectional image of the volume data.

  FIG. 7 is a schematic diagram illustrating a change example of the length of the contour 13 in the left ventricular short-axis cross-sectional image 10. In FIG. 7, on the horizontal axis, one cardiac cycle is divided into 0 to 100 (%) time points, and on the vertical axis, the length of the contour 13 of the myocardium 12 is shown. The consideration for the experimental data in FIG. 7 is shown in FIGS. 8 (A) and 8 (B).

  8 (A), (B), FIG. 9 (A), (B), FIG. 10 (A), and (B) show the change rate of the contour length when the heart contracts in each cross-sectional image, It is a schematic diagram which shows the example of distribution of the change acceleration of the contour length in a systole.

  In FIG. 8 (A), (B), FIG. 9 (A), (B), FIG. 10 (A), (B), “AS” indicates the experimental result for a subject with aortic stenosis, and “AR” Indicates experimental results for subjects with aortic regurgitation, and “Control” indicates experimental results for healthy subjects.

  FIG. 8A is a schematic diagram showing a distribution example of the change speed of the length of the contour 13 in the left ventricular short-axis cross-sectional image 10 when the heart contracts. In the distribution of the change rate of the experimental sample in FIG. 8A, “AS” is 5.7 ± 2.1 (mm / msec), and “AR” is 9.0 ± 1.7 (mm / msec). ), And “control” is 8.4 ± 3.5 (mm / msec). The numerical value before the symbol “±” indicates an average value, and the numerical value after the symbol “±” indicates a standard deviation.

  FIG. 8B is a schematic diagram showing a distribution example of the change acceleration of the length of the contour 13 in the left ventricular short-axis cross-sectional image 10 during the systole of the heart. In the distribution of change acceleration of the experimental sample in FIG. 9B, “AS” is 0.22 ± 0.14 (mm / msec), and “AR” is 0.40 ± 0.13 (mm / msec). ) And “control” is 0.29 ± 0.17 (mm / msec).

  FIG. 9A is a schematic diagram showing a distribution example of the change speed of the length of the contour 23 in the left ventricular vertical long-axis cross-sectional image 20 when the heart contracts. In the distribution of the change rate of the experimental sample in FIG. 9A, “AS” is 2.4 ± 1.0 (mm / msec), and “AR” is 4.2 ± 0.9 (mm / msec). ), And “control” is 3.7 ± 1.0 (mm / msec).

  FIG. 9B is a schematic diagram showing a distribution example of the change acceleration of the length of the contour 23 in the left ventricular vertical long-axis cross-sectional image 20 in the systole of the heart. In the distribution of change acceleration of the experimental sample in FIG. 9B, “AS” is 0.11 ± 0.06 (mm / msec), and “AR” is 0.16 ± 0.07 (mm / msec). And “control” is 0.15 ± 0.06 (mm / msec).

  FIG. 10A is a schematic diagram showing a distribution example of the change speed of the length of the contour 33 in the left ventricular horizontal long-axis cross-sectional image 30 when the heart contracts. In the distribution of the change rate of the experimental sample in FIG. 10A, “AS” is 3.0 ± 1.1 (mm / msec), and “AR” is 3.7 ± 0.8 (mm / msec). ), And “control” is 3.4 ± 0.7 (mm / msec).

  FIG. 10B is a schematic diagram showing an example of the distribution of change in the length of the contour 33 in the left ventricular horizontal long-axis cross-sectional image 30 during the systole of the heart. In the distribution of change acceleration of the experimental sample in FIG. 10B, “AS” is 0.13 ± 0.08 (mm / msec), and “AR” is 0.16 ± 0.06 (mm / msec). ), And “control” is 0.13 ± 0.04 (mm / msec).

  Thus, in FIG. 8A, FIG. 9A, and FIG. 10A, the sample of “AR” (indicated by □) has a change rate value higher than that of the sample of “AS” (◯). It can be understood that the distribution is larger. This is because AS is difficult to open the aortic valve, blood is not easily ejected from the left ventricle 11, the degree of contraction of the left ventricle 11 is relatively gentle, and the left ventricular ejection fraction is small. . In addition, since the aortic valve is easily opened in the AR, it is considered that blood is easily ejected from the left ventricle 11, the contraction degree of the left ventricle 11 is relatively steep, and the left ventricular ejection rate is high. In FIG. 10A, the change rate distribution did not show a significant tendency among AS, AR, and Control.

  8B, FIG. 9B, and FIG. 10B, the “AR” sample is distributed in the direction in which the value of the change acceleration is larger than the “AS” sample. This is because AS is difficult to open the aortic valve, blood hardly flows back to the left ventricle 11, the degree of expansion of the left ventricle 11 is relatively gentle, and the pressure in the left ventricle 11 is maintained to some extent. Conceivable. In addition, since the aortic valve is easy to open in the AR, blood is likely to flow back to the left ventricle 11, the degree of expansion of the left ventricle 11 is relatively steep, and it is considered that the pressure in the left ventricle 11 decreases. In FIG. 10B, the change rate distribution did not show a significant tendency among AS, AR, and Control.

  The display unit 130 displays the contour 13 of the left ventricular short-axis cross-sectional image 10 and the contour 23 of the left ventricular vertical long-axis cross-sectional image 20 based on the experimental results for the subject, thereby effectively diagnosing aortic stenosis by the user. I can understand that you can help. Further, by further examining regression analysis of each parameter, it can be expected that the above experimental results can be used more effectively for diagnosis of aortic stenosis and aortic regurgitation.

  Note that conventional methods such as measurement of the left ventricular ejection fraction measure the volume of the intraventricular cavity, but in this embodiment, the myocardium, particularly the outer wall of the myocardium, is measured. This is because the image is unclear due to the presence of tissues such as papillary muscles in the intraventricular cavity, and in particular, the measurement of the intraventricular cavity is not stable at the apex. On the other hand, the myocardium, particularly the outer wall of the myocardium, is a clear tissue, and stable measurement results can be expected. The myocardium can be measured without using a contrast medium.

Next, an operation example of the image processing apparatus 100 will be described.
FIG. 11 is a flowchart illustrating an operation example of the image processing apparatus 100.

  The CT apparatus 200 captures a CT image including the vicinity of the human heart. The image processing apparatus 100 acquires volume data of a plurality of phases from the CT apparatus 200 (S101).

  The operation unit 120 is an input operation for designating three sections (for example, a left ventricular minor axis section, a left ventricular vertical major axis section, and a left ventricular horizontal major axis section) from volume data in one of a plurality of phases. Is received (S102). The setting unit 141 specifies the three cross sections according to the input operation of the operation unit 120. The image deriving unit 144 derives cross-sectional images of three cross sections (for example, the left ventricular short-axis cross-sectional image 10, the left ventricular vertical long-axis cross-sectional image 20, and the left ventricular horizontal long-axis cross-sectional image 30).

  The operation unit 120 receives an input operation for designating the contour 13 in the derived left ventricular short-axis cross-sectional image 10 (S103). The setting unit 141 sets the contour 13. The display unit 130 may display the set contour 13 so as to overlap the left ventricular short-axis cross-sectional image 10 in order to make the contour 13 visible.

  The operation unit 120 receives an input operation for designating the contour 23 in the derived left ventricular vertical long-axis cross-sectional image 20 (S104). The setting unit 141 sets the contour 23. The display unit 130 may display the set contour 23 so as to overlap the left ventricular vertical long-axis cross-sectional image 20 in order to make the contour 23 visible.

  The operation unit 120 receives an input operation for designating the contour 33 in the derived left ventricular horizontal long-axis cross-sectional image 30 (S105). The setting unit 141 sets the contour 33. The display unit 130 may display the set contour 33 so as to overlap the left ventricular horizontal long-axis cross-sectional image 30 in order to make the contour 33 visible.

  The registration processing unit 142 performs registration between a plurality of phases of the volume data acquired in S101 (S106). That is, the registration processing unit 142 adjusts the position of a predetermined region (eg, heart) of the volume data between a plurality of phases.

  In accordance with the registration result by the registration processing unit 142, that is, the alignment result, the setting unit 141 sets the contours 13, 23, and 33 in the cross-sectional images in phases other than the one phase among the plurality of phases. Set (S107).

  The contour information processing unit 143 derives the length of each contour 13, 23, 33 in each phase (S108).

  The contour information processing unit 143 derives the changing speed of the lengths of the contours 13, 23, and 33 when the heart contracts in each phase (S109).

  The contour information processing unit 143 derives a change acceleration of the length of each contour 13, 23, 33 in the systole of the heart in each phase (S110).

  The contour information processing unit 143 generates a graph (see, for example, FIGS. 6 and 7) indicating the lengths of the contours 13, 23, and 33 in each phase. The display unit 130 displays the generated graph (S111).

  The contour information processing unit 143 is a distribution diagram (for example, FIG. 8 (A), FIG. 9 (A), FIG. 10 () showing the change rate of the length of each contour 13, 23, 33 when the heart contracts in each phase. A)) is generated. The display unit 130 displays the generated distribution map of the changing speed of the contour length (S112). In addition, the display part 130 may represent the change speed of outline length by a figure other than a distribution map.

  The contour information processing unit 143 is a distribution diagram (for example, FIG. 8B, FIG. 9B, FIG. 10) showing the change acceleration of the length of each contour 13, 23, 33 during the systole of the heart in each phase. B)) is generated. The display unit 130 displays a distribution map of the generated change acceleration of the contour length (S113). In addition, the display part 130 may represent the change acceleration of a contour length by other than a distribution map.

  According to the operation example of the image processing apparatus 100, parameters corresponding to the left ventricular pressure and the left ventricular ejection rate, which are important indicators for the diagnosis of aortic stenosis and aortic regurgitation, are obtained from an image of the inside of the living body. The change acceleration and the change speed can be acquired. Further, the image processing apparatus 100 derives and displays the distribution state of the change acceleration and the change speed, so that the user can easily distinguish aortic valve stenosis and aortic regurgitation from the tendency of the distribution state. Can be diagnosed.

  Next, details of phase interpolation will be described.

  In the phase interpolation processing unit 145, for example, the position of each region in the cross-sectional image, the pixel value of each pixel in the cross-sectional image, the pitch (time interval) of the three-dimensional image in the cardiac cycle, and the number of three-dimensional images are used. Based on this, the volume data in the interpolated phase is derived.

  For example, when the CT device 200 captures a CT image, in one cardiac cycle, for example, the number of phases is about 10 to 20, that is, the number of captured three-dimensional images is 10 to 20. The phase interpolation processing unit 145 performs phase interpolation, so that the change of the image between phases can be smoothed.

  FIG. 12A is a schematic diagram showing the relationship between the phase of the cardiac cycle and the left ventricular area (left ventricular cross-sectional area) in the left ventricular short-axis cross-sectional image 10 when phase interpolation is not performed. FIG. 12B is a schematic diagram illustrating an example in which the left ventricular short-axis cross-sectional images 10 of the respective phases when phase interpolation is not performed are arranged in time series. FIG. 12C is a schematic diagram illustrating an example of the left ventricular short-axis cross-sectional image 10 when phase interpolation is not performed. The left ventricular area can be treated as a parameter similar to the contour of the left ventricular myocardium.

  FIG. 13A is a schematic diagram showing the relationship between the phase of the cardiac cycle of the left ventricular short-axis cross-sectional image 10 and the left ventricular area when performing phase interpolation. FIG. 13B is a schematic diagram illustrating an example in which the left ventricular short-axis cross-sectional images 10 of the respective phases when phase interpolation is performed are arranged in time series. FIG. 13C is a schematic diagram illustrating an example of the left ventricular short-axis cross-sectional image 10 in the interpolated frame in the case of phase interpolation.

  Comparing FIGS. 12A and 12B and FIGS. 13A and 13B, FIG. 13A has a larger number of frames and the left ventricle with respect to the phase of the cardiac cycle derived by each frame. It can be understood that the number of samples in the area increases and the graph is drawn smoothly.

  The acceleration of the image change between phases is expressed by, for example, (Equation 1) below.

  Note that n represents differentiation, Φ (t) represents the contour length at time t, and ± h represents the preceding and following frames. That is, (Expression 1) indicates that three consecutive frames of images are extracted from the volume data, the difference between the images between the frames is taken, and second order differentiation is performed to derive the acceleration at time t.

  In general, with the passage of time, the position of the heart moves somewhat as the living body moves in addition to movement due to contraction or expansion. Therefore, the position of the heart in the CT image moves. Even in this case, when the phase interpolation processing unit 145 performs the phase interpolation, for example, it becomes easy to track the shape change in the three cross sections. Therefore, the change in the contour length can be tracked with high accuracy, and the parameter derivation accuracy can be improved. In particular, since the heart contracts while twisting, it is easy to set the correspondence between frames by performing phase interpolation once, and it can be expected that the analysis accuracy of the registration and thus the contour length will be improved.

  As described above, according to the image processing apparatus 100, the left ventricular pressure and the left ventricle, which are important indicators for diagnosing cardiac valvular disease (for example, aortic stenosis, aortic valve insufficiency) from an image taken inside the living body. An index equivalent to ejection fraction can be acquired. Therefore, a test for valvular heart disease can be performed without using ultrasonic waves, and variation in test results by the person in charge of the test can be reduced. In addition, since valvular heart disease can be examined without using a catheter, invasiveness to the human body can be reduced, and labor for the person in charge of the examination and the patient can be reduced.

  Thus, in the examination of valvular heart disease, the burden on the person in charge of the examination and the patient can be reduced, and the accuracy of the parameters obtained by the examination can be improved. Therefore, the user can perform diagnosis of valvular heart disease with high accuracy using the obtained parameters.

  The present invention is not limited to the configuration of the above-described embodiment, and any configuration can be used as long as the functions shown in the claims or the functions of the configuration of the present embodiment can be achieved. Is also applicable.

  In the above embodiment, the image processing apparatus 100 has exemplified that the parameters corresponding to the left ventricular ejection fraction and the left ventricular pressure are derived based on the change in the length of the contours 13, 23, and 33. Not limited. For example, the image processing apparatus 100 may derive parameters corresponding to the left ventricular ejection fraction and the left ventricular systolic pressure based on the left ventricular area in the cross-sectional image or the left ventricular volume in the volume data. For example, the image processing apparatus 100 may derive the change rate of the left ventricular area during the contraction of the heart and the change acceleration of the left ventricular area during the systole of the heart, similarly to the contour of the left ventricular myocardium.

  In the above-described embodiment, the image processing apparatus 100 may include a filter used when deriving the change speed or change acceleration of the lengths of the contours 13, 23, and 33. Thereby, the contour information processing unit 143 can reduce the influence of noise and improve the accuracy of deriving the change speed or the change acceleration by using a filter when, for example, obtaining the difference and deriving the inclination.

  In the above-described embodiment, it is exemplified that the CT apparatus 200 captures an image and generates volume data including information inside the living body. However, another apparatus (for example, an MRI (Magnetic Resonance Imaging) apparatus) captures an image, Volume data may be generated. In addition, when using an MRI apparatus, the precision of volume data may fall with respect to the patient in which the artificial valve was inserted in the body. Therefore, when the examination, the insertion of the prosthetic valve, and the examination after the insertion of the artificial valve are continuously performed on patients with valvular heart disease, the examination accuracy can be improved by using the CT apparatus 200.

  In the above embodiment, three cross sections, ie, a left ventricular short axis cross section, a left ventricular vertical long axis cross section, and a left ventricular horizontal long axis cross section, are exemplified as the cross section for cutting the volume data. May be used. Moreover, you may use several cross sections (for example, several left ventricular short-axis cross section) about each cross-sectional direction. The cross section may be a curved surface (for example, a curved surface used for so-called Curved Planar Construction along a curve connecting the centroids of the left ventricular short-axis cross sections).

  In the above embodiment, it is mainly exemplified to derive parameters for diagnosing aortic stenosis and aortic regurgitation from an image obtained by imaging the inside of the living body, but other than aortic stenosis and aortic regurgitation Parameters for diagnosing valvular heart disease may be derived. For example, the image processing apparatus 100 derives change information (for example, change acceleration, change speed) of the length of the outline of the right ventricular myocardium or the area of the region surrounded by the outline, so that the right ventricular pressure and ejection Rate (outflow rate) can be derived. Thereby, parameters for diagnosing stenosis or insufficiency related to each heart valve (for example, mitral valve, tricuspid valve, aortic valve, and pulmonary valve) can be obtained from a CT image or the like. At this time, for example, the contour length of the portion not in contact with the left ventricle in the right ventricular short-axis cross section can be an excellent index. Thus, parameters for diagnosing pulmonary valve stenosis and pulmonary valve insufficiency may be derived. It can also be applied to cardiac function analysis.

(Overview of one embodiment of the present invention)
The parameter processing apparatus according to one aspect of the present invention includes an image acquisition unit that acquires volume data including heart regions in a plurality of phases arranged in time series, and a cross section cut at a predetermined cross section in the volume data of each phase. A cross-sectional image deriving unit for deriving an image, a contour setting unit for setting a contour of a ventricular myocardium in the cross-sectional image of each phase, and the contour at a predetermined period or a predetermined time point in a cardiac cycle based on the contour of each phase A change information deriving unit for deriving change information indicating a change in the length or area of the region surrounded by the contour, and a display unit for displaying the change information.

  According to this configuration, a parameter corresponding to an index for diagnosis of valvular heart disease can be acquired from an image obtained by imaging the inside of the living body. Therefore, a test for valvular heart disease can be performed without using ultrasonic waves, and variation in test results by the person in charge of the test can be reduced. In addition, since valvular heart disease can be examined without using a catheter, invasiveness to the human body can be reduced, and labor for the person in charge of the examination and the patient can be reduced. Thus, in the examination of valvular heart disease, the burden on the person in charge of the examination and the patient can be reduced, and the accuracy of the parameters obtained by the examination can be improved. Therefore, the user can perform diagnosis of valvular heart disease with high accuracy using the obtained parameters.

  In the parameter processing device according to one aspect of the present invention, the change information deriving unit is configured to determine the length of the contour at a predetermined period or a predetermined time in the vicinity of the systole including the systole in the cardiac cycle based on the contour of each phase. A change acceleration of the area of the region is derived, and the display unit displays information on the change acceleration.

  According to this configuration, it is possible to acquire a parameter (the change acceleration described above) capable of analogizing the pressure in the ventricle (for example, the left ventricle or the right ventricle), which is an important index for diagnosis of valvular heart disease, from an image obtained by imaging the inside of the living body.

  In the parameter processing device according to one aspect of the present invention, the change information deriving unit is configured to determine the length of the contour in a predetermined period or a predetermined time from the diastole to the systole in the cardiac cycle based on the contour in each phase. Alternatively, the change rate of the area of the region is derived, and the display unit displays the change rate information.

  According to this configuration, a parameter (the above change rate) that can estimate the ejection fraction of the ventricle (for example, the left ventricle or the right ventricle), which is an important index for diagnosis of valvular heart disease, is obtained from an image captured inside the living body. it can.

  In the parameter processing apparatus according to one aspect of the present invention, the predetermined cross section includes a left ventricular short-axis cross section or a left ventricular long-axis cross section.

  According to this configuration, change information can be easily derived using a general left ventricular cross section.

  In the parameter processing device according to an aspect of the present invention, the predetermined cross section exists in a plurality, the contour setting unit sets the contour in a plurality of cross-sectional images of each phase, and the change information deriving unit includes each phase. The plurality of pieces of change information are derived based on the plurality of the contours, and the display unit displays the plurality of pieces of change information.

  According to this configuration, since a plurality of change information related to the ventricular myocardium is derived and displayed in the plurality of cross-sectional images, the user refers to the plurality of change information (for example, change information using different cross-sectional images), and Diagnosis of valvular heart disease can be easily performed.

  A parameter processing apparatus according to an aspect of the present invention includes a registration processing unit that adjusts the position of the contour in a cross-sectional image of each phase.

  According to this configuration, even when the position of the heart moves in the cross-sectional image of each phase, it is possible to easily follow the change in the movement of the heart.

  A parameter processing apparatus according to an aspect of the present invention includes a phase interpolation processing unit that derives the volume data by interpolating phases between successive phases, and the cross-sectional image deriving unit is acquired by the image acquisition unit The cross-sectional image is derived from the phase and phase volume data interpolated by the phase interpolation processing unit.

  According to this configuration, for example, the number of areas of the contour or the region derived in one cardiac cycle can be increased, so that the change information derived based on the length of the contour or the area of the region in each phase Accuracy can be improved. Therefore, even when the position of the heart moves, it is possible to easily follow changes in the movement of the heart.

  In the parameter processing device according to one aspect of the present invention, the display unit displays a graph indicating a change in the length of the contour or the area of the region set by the contour setting unit.

  According to this configuration, the user can easily visually recognize the change in the area of the contour or the region, and can intuitively grasp the change in the area of the contour or the region.

  The parameter processing method of one aspect of the present invention includes a step of acquiring volume data including heart regions in a plurality of phases arranged in time series, and a cross-sectional image cut at a predetermined cross section in the volume data of each phase. A step of deriving, a step of setting a contour of a ventricular myocardium in the cross-sectional image of each phase, and a length or a contour of the contour in a predetermined period or a predetermined time point in a cardiac cycle based on the contour of each phase Deriving change information indicating a change in the area of the enclosed region, and displaying the change information on a display unit.

  According to this method, a parameter corresponding to an index for diagnosis of valvular heart disease can be acquired from an image obtained by imaging the inside of a living body. Therefore, a test for valvular heart disease can be performed without using ultrasonic waves, and variation in test results by the person in charge of the test can be reduced. In addition, since valvular heart disease can be examined without using a catheter, invasiveness to the human body can be reduced, and labor for the person in charge of the examination and the patient can be reduced. Thus, in the examination of valvular heart disease, the burden on the person in charge of the examination and the patient can be reduced, and the accuracy of the parameters obtained by the examination can be improved. Therefore, the user can perform diagnosis of valvular heart disease with high accuracy using the obtained parameters.

  A parameter processing program according to an aspect of the present invention is a program for causing a computer to execute each step of the parameter processing method.

  According to this program, a parameter corresponding to an index for diagnosis of valvular heart disease can be acquired from an image obtained by imaging the inside of a living body. Therefore, a test for valvular heart disease can be performed without using ultrasonic waves, and variation in test results by the person in charge of the test can be reduced. In addition, since valvular heart disease can be examined without using a catheter, invasiveness to the human body can be reduced, and labor for the person in charge of the examination and the patient can be reduced. Thus, in the examination of valvular heart disease, the burden on the person in charge of the examination and the patient can be reduced, and the accuracy of the parameters obtained by the examination can be improved. Therefore, the user can perform diagnosis of valvular heart disease with high accuracy using the obtained parameters.

  INDUSTRIAL APPLICABILITY The present invention is useful for a parameter processing device, a parameter processing method, a parameter processing program, and the like that can reduce the burden on the person in charge of the examination and the patient in the examination of valvular heart disease and can improve the accuracy of parameters obtained by the examination.

10 Left ventricular short-axis cross-sectional image 11 Left ventricle 12 Myocardium 13, 23, 33 Contour 14 Right ventricle 15 Lung 20 Left ventricular vertical long-axis cross-sectional image 21, 32 Heart base 22, 31 Apex 30 Left ventricle horizontal long-axis cross-sectional image 100 Image processing device 110 Image acquisition unit 120 Operation unit 130 Display unit 140 Control unit 141 Setting unit 142 Registration processing unit 143 Contour information processing unit 144 Image deriving unit 145 Phase interpolation processing unit 150 Storage unit 200 CT apparatus

Claims (13)

Including an area of the heart in a plurality of phases arranged in time series, an image acquisition unit for acquiring volume data obtained by a CT (Computed Tomography) apparatus;
In the volume data of each phase, a cross-sectional image deriving unit for deriving a cross-sectional image cut at a predetermined cross-section,
In the cross-sectional image of each phase, a contour setting unit that sets the contour of the ventricular myocardium,
Based on the contour of each phase, a change information deriving unit for deriving change information indicating a change in the length of the contour at a predetermined period or a predetermined time in a cardiac cycle;
A display unit for displaying the change information;
A parameter processing apparatus comprising: The parameter processing apparatus according to claim 1,
The contour includes the contour of the left ventricular myocardium, the contour along the outer wall of the left ventricle from the base of the left ventricle to the apex, and the outer wall of the left ventricle from the apex to the base of the left ventricle. A parameter processing device which is at least one of contours along. The parameter processing device according to claim 1 or 2 ,
The change information deriving unit derives a change acceleration of the length of the contour in a predetermined period near a systole including a systole in a cardiac cycle or a predetermined time based on the contour of each phase,
The parameter processing device comprising: the display unit displaying information on the change acceleration. The parameter processing device according to claim 1 or 2 ,
The change information deriving unit derives a change rate of the length of the contour in a predetermined period or a predetermined time point from the diastole to the systole in the cardiac cycle based on the contour of each phase,
The display unit is a parameter processing device that displays information on the change speed. The parameter processing device according to any one of claims 1 to 4 ,
The parameter processing apparatus, wherein the predetermined cross section includes a left ventricular short-axis cross section or a left ventricular long-axis cross section. The parameter processing device according to any one of claims 1 to 5 ,
There are a plurality of the predetermined cross sections,
The contour setting unit sets the contour in a plurality of cross-sectional images of each phase,
The change information deriving unit derives a plurality of the change information based on the plurality of the contours of each phase,
The said display part is a parameter processing apparatus which displays the said some change information. The parameter processing device according to any one of claims 1 to 6 , further comprising:
A parameter processing apparatus comprising a registration processing unit that adjusts the position of the contour in a cross-sectional image of each phase. The parameter processing device according to any one of claims 1 to 7 , further comprising:
A phase interpolation processing unit for interpolating phases between successive phases to derive the volume data;
The parameter processing device, wherein the cross-sectional image deriving unit derives the cross-sectional image in the volume data of the phase acquired by the image acquiring unit and the phase interpolated by the phase interpolation processing unit. The parameter processing device according to any one of claims 1 to 8 ,
The display unit displays a graph showing the change in length of the contour set by the contour setting unit, the parameter processing device. A parameter processing method in a parameter processing apparatus,
Including the heart region in a plurality of phases arranged in time series, and obtaining volume data obtained by a CT apparatus;
Deriving a cross-sectional image cut at a predetermined cross-section in the volume data of each phase;
In the cross-sectional image of each phase, setting a ventricular myocardial contour;
Deriving change information indicating a change in length of the contour at a predetermined period or a predetermined time point in a cardiac cycle based on the contour of each phase;
Displaying the change information on a display unit;
A parameter processing method comprising: The parameter processing method according to claim 10, comprising:
The contour includes the contour of the left ventricular myocardium, the contour along the outer wall of the left ventricle from the base of the left ventricle to the apex, and the outer wall of the left ventricle from the apex to the base of the left ventricle. A parameter processing method which is at least one of contours along. A step when viewed including the region of the heart at a plurality of phases arranged in series, to obtain the volume data obtained by the CT apparatus,
Deriving a cross-sectional image cut at a predetermined cross-section in the volume data of each phase;
In the cross-sectional image of each phase, setting a ventricular myocardial contour;
Deriving change information indicating a change in length of the contour at a predetermined period or a predetermined time point in a cardiac cycle based on the contour of each phase;
Displaying the change information on a display unit;
A parameter processing program for causing a computer to execute. A parameter processing program according to claim 12,
The contour includes the contour of the left ventricular myocardium, the contour along the outer wall of the left ventricle from the base of the left ventricle to the apex, and the outer wall of the left ventricle from the apex to the base of the left ventricle. A parameter processing program which is at least one of contours along.