WO2016009957A1 - コンピュータプログラム、画像処理装置及び方法 - Google Patents
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Definitions
- the present invention relates to an image processing technique, and more particularly to a technique for analyzing a time-series dynamic image.
- a method is known in which blood flow in a heart is analyzed using an image obtained by imaging the subject's heart after administering a contrast medium to the subject.
- SPECT Single Photo Emission Computed Tomography
- SPECT inspection has few contraindications, an inspection method has been established, and it is “whole heart imagining”.
- Another advantage is that the best outcome can be obtained by estimating the incidence of cardiac accidents according to the severity of myocardial ischemia detected by the SPECT test, and selecting a treatment policy according to the severity. In this regard, sufficient evidence has already been shown.
- the shortcomings of the SPECT examination include the lack of spatial resolution and the inability to simultaneously evaluate coronary artery stenosis.
- MRI examination In recent years, many usefulnesses have been reported for myocardial ischemia evaluation by MRI examination. Advantages of MRI examination include no radiation exposure, few side effects of contrast agents, and high spatial resolution. On the other hand, the disadvantages of the MRI examination include the length of the examination time, the shift of the cardiac phase due to the difference in the time of data collection for each cross section, and contraindicated cases such as non-MRI compatible pacemaker cases.
- the stress myocardial perfusion CT (Computed Tomography) test is a non-invasive stress myocardial blood flow test method that has recently been reported to be useful.
- a great advantage of this test compared to other modalities is that simultaneous coronary CT imaging can be used to evaluate the presence or absence of myocardial ischemia in conjunction with highly accurate coronary artery morphology assessment.
- perfusionCT has mainly been qualitatively evaluated by static image using the single shot method in which only one time phase is taken during the load. Therefore, it is difficult to take a picture while detecting the photographing timing.
- TDC Time density curve
- Non-Patent Document 1 discloses a time series dynamic MRI image obtained by imaging the heart, and an arrival time (Arrival Time) that is a time when a contrast agent arrives in a predetermined myocardial region. The calculation method is described.
- the coronary CT examination has come to be widely used in the field of cardiovascular medicine as a non-invasive examination having a diagnostic accuracy equivalent to that of the coronary angiography (CAG).
- new imaging technologies such as subtraction imaging and dual energy imaging have been clinically applied with the advent of CT devices such as surface detector CT, high resolution CT and two-tube CT. Accordingly, there is a possibility that the coronary artery CT examination can be effectively used for the evaluation of coronary artery stenosis in high heart rate cases, arrhythmia cases, coronary artery calcification cases and the like that have been considered difficult so far.
- myocardial ischemia corresponding to a coronary artery stenosis lesion is an important factor for determining whether or not to perform treatment by revascularization (for example, catheter treatment or surgical treatment).
- revascularization for example, catheter treatment or surgical treatment.
- the judgment of whether or not to intervene for a coronary artery stenosis lesion observed in a coronary CT scan may be based on uncertain symptoms.
- an analyst such as a doctor sets an ROI (Region Of Interest, a region of interest) in the myocardial region, and the arrival time of the myocardial region is determined based on the pixel value in the ROI.
- ROI Region Of Interest
- the conventional method of artificially surrounding and analyzing the ROI since the subjectivity of the observer intervenes, there is a problem that a bias is applied.
- the ROI-based analysis only the pixels within the ROI range are analyzed, and there is a possibility that the normal region and the abnormal region are mixed and averaged. Therefore, the conventional method has a problem that individual differences among analysts are likely to occur and lack of objectivity.
- an object of the present invention is to provide an analysis method that guarantees objectivity and quantitativeness in the analysis of time-series images.
- Another object of the present invention is to provide a more detailed analysis method in the analysis of time series images of the heart.
- Another object of the present invention is to provide an inspection technique capable of performing coronary stenosis evaluation and myocardial ischemia evaluation with high accuracy in a short time.
- An image processing apparatus including a storage unit that stores image data of time-series CT (Computed Tomography) images of a plurality of frames obtained by imaging an organ of a subject after administration of a contrast medium according to an embodiment of the present invention.
- the computer program for identifying to the image processing apparatus the step of identifying an intra-organ pixel position, which is a position of a pixel within an organ region, and based on image data of the time-series CT images of the plurality of frames.
- the computer program sets a predetermined frame after the CT value suddenly rises in the specified time-dependent change as an upper limit frame, and two or more time-lapse changes before the upper limit frame.
- the step of approximating with the function may be executed by the image processing apparatus.
- the arrival time and the base value may be specified by the two or more approximated functions.
- the computer program sets a predetermined frame after the CT value suddenly rises in the specified temporal change as an upper limit frame, and the temporal change before the upper limit frame is a normal cumulative distribution.
- the step of approximating with a function or a cumulative distribution function may be further executed by the image processing apparatus. At this time, the arrival time and the base value may be specified by the approximated normal cumulative distribution function or cumulative distribution function.
- the computer program may cause the image processing apparatus to further execute a step of approximating the specified temporal change with an m-order function (m is 3 or more).
- m is 3 or more.
- the upper limit frame is determined based on an m-order function obtained by the approximation, and the two or more functions include a straight line and a quadratic approximated to the m-order function before the upper limit frame. It may contain functions.
- the computer program may cause the image processing apparatus to further execute a step of setting an ROI (Region of Interest) including a plurality of pixels in a blood vessel region through which blood flows into the organ.
- the temporal change of the CT value may be a temporal change of the ROI value specified based on the CT value of the pixel in the set ROI.
- the arrival time may be an eye arrival time when the contrast medium reaches the blood vessel region specified based on a change with time of the specified ROI value.
- the base value may be determined based on a CT value of a pixel in the blood vessel region.
- a pixel having a CT value within a predetermined range may be selected in all the time-series CT images of the plurality of frames.
- the plurality of time-series CT images may be selected based on a difference value between a maximum value and a minimum value of CT values.
- the organ is a heart
- the time-series CT images of the plurality of frames may be CT images captured in synchronization with an electrocardiogram.
- FIG. 1 is an overall configuration diagram of an image processing apparatus 1 according to an embodiment of the present invention. It is explanatory drawing of a CT image. It is a figure which shows an example of TDC. An example of the data structure of the input function table 130 and the output function table 150 is shown. An example of TDC (Time density curve) of the CT value at the target pixel position is shown. It is a flowchart which shows the whole process sequence of an input function derivation process. It is a flowchart which shows the detailed procedure of the process which specifies arrival time and a base value. It is a flowchart which shows the whole process sequence of an output function derivation process.
- FIG. 1 is an overall configuration diagram of an image processing apparatus 1 according to the present embodiment.
- the image processing apparatus 1 performs quantitative analysis of the blood flow volume of the organ using the CT image of the organ of the subject imaged after the contrast medium is administered to the subject.
- the image processing apparatus 1 uses, for example, iopromide (N, N'-bis (2,3-dihydroxypropyl) -2,4,6-triiodo-5- (2-methoxyacetamido) -N- as a contrast agent.
- a subject may be a plurality of CT frame images of the same phase obtained by imaging the subject's heart in synchronism with an electrocardiogram using methylisophthalamide).
- the image processing apparatus 1 preferably analyzes the above-described CT image of the heart in units of pixels, thereby calculating an arrival time and a baseline value (hereinafter referred to as a baseline) used for calculating a quantitative value of the myocardial blood flow. Base value).
- the myocardial blood flow may be estimated based on the change in the pixel value of the CT image due to the contrast agent administered to the vein. For example, when the pixel value of the CT image suddenly changes due to the contrast medium reaching the target site, the pixel value before the pixel value starts to change is used as the base value, and the time when the pixel value starts to change is the arrival time. It may be time. In this case, for example, an increase from the base value of the pixel value after the arrival time is considered to be due to the influence of the contrast agent. Therefore, the blood flow rate may be calculated based on the increase from the base value.
- the image processing apparatus 1 is configured by, for example, a general-purpose information processing apparatus (computer system), and individual components or functions in the image processing apparatus 1 described below are realized by, for example, executing a computer program.
- the This computer program can be stored in a computer-readable recording medium.
- the image processing apparatus 1 includes an image data storage unit 11, an input function data storage unit 13, an output function data storage unit 15, an input function derivation unit 20, an output function derivation unit 30, a blood flow analysis processing unit 50, Is provided.
- An input device 3 and a display device 5 may be connected to the image processing device 1.
- the image data storage unit 11 stores image data of CT images.
- the image data of the CT image may be three-dimensional voxel data.
- the CT image may be a three-dimensional image composed of a plurality of slice images (short axis tomographic images).
- the CT image may be a multiple-frame time-series CT image obtained by imaging an organ of a subject after the contrast medium is administered.
- FIG. 2 is an explanatory diagram of a CT image.
- the CT image may be a frame image 110 in which a heart is imaged in synchronization with an electrocardiogram and a predetermined number (for example, 30) of heartbeats are in phase.
- One frame may be composed of a plurality of slice images. That is, one frame of data may be composed of three-dimensional image data. In the figure, a frame image of one slice is illustrated.
- the CT image may be one in which imaging is started immediately after the contrast medium is administered to the subject.
- the CT image may include the first frame to the 30th frame in the order of imaging.
- the input function deriving unit 20 determines that the contrast agent flowing into the organ (heart) of the subject is a pixel value (hereinafter referred to as a CT image) of the CT image based on the image data stored in the image data storage unit 11.
- An input function related to a change in the pixel value or CT value may be derived.
- the input function deriving unit 20 may further include an ROI setting unit 21, a temporal change specifying unit 23, and a functionalization processing unit 25.
- the ROI setting unit 21 selects an area for specifying an input function.
- the ROI setting unit 21 may set the ROI on the CT image according to the operation of an analyst such as a doctor.
- the ROI setting unit 21 reads image data from the image data storage unit 11 and causes the display device 5 to display a frame image of a slice in which the target region is reflected.
- the ROI may be set in a blood vessel that causes blood to flow into the heart, that is, in the region of the aorta or the lumen region of the heart. Therefore, a slice in which the region of the aorta or the lumen region of the heart is selected is selected and displayed on the display device 5.
- the ROI may be set on the image displayed on the display device 5 in accordance with the input performed by the analyst using the input device 3.
- the position of this ROI may be common to all frames of the same slice.
- the frame image for which the ROI setting is performed may be an image in which the aortic region can be easily identified.
- the set ROI includes a plurality of pixels.
- the temporal change specifying unit 23 may specify the temporal change of the CT value in the ROI based on the image data of time-series CT images of a plurality of frames. For example, the temporal change specifying unit 23 may generate a TDC (Time Density Curve) of an ROI value determined from a CT value in the ROI as a temporal change of the CT value of the aorta region of the CT image.
- TDC Time Density Curve
- FIG. 3A is a diagram illustrating an example of TDC.
- the temporal change specifying unit 23 may apply the ROI set by the ROI setting unit 21 to another frame image 110 of the same slice. Then, the temporal change specifying unit 23 may determine an ROI value representing the ROI for each frame image 110 based on the CT value of the pixel in the ROI. Plotting the ROI values determined for all frames of the same slice generates a TDC as shown in FIG.
- the ROI value is, for example, a value (statistical value) obtained by processing a pixel value in the ROI with a statistical algorithm, and may be any one of an average value, a mode value, a median value, a maximum value, a minimum value, and the like.
- the temporal change specifying unit 23 may perform the smoothing process so that the TDC becomes a smooth curve.
- the functionalization processing unit 25 may specify the eye rival time at which the contrast agent reaches the region where the ROI is set based on the temporal change of the CT value.
- FIG. 3B shows the ROI values plotted to draw the TDC. With reference to the same figure, the process which specifies arrival time is demonstrated.
- the functionalization processing unit 25 may determine a predetermined frame after the CT value rapidly increases in the temporal change of the CT value specified by the temporal change specifying unit 23 as the upper limit frame Fa.
- the upper limit frame Fa may be, for example, one frame before the frame where the CT value reaches a peak. Then, the temporal change specifying unit 23 may approximate the TDC before the upper limit frame Fa to a function.
- the upper limit frame Fa may be determined as follows, for example. That is, the temporal change specifying unit 23 may detect the maximum value Max of the CT value in the TDC, or the first peak value after the curve has risen significantly (the CT value suddenly increases).
- the maximum value Max or the peak value which takes a certain ratio (for example, 70%, 80%, 90%, etc.) from the minimum value Min of the CT value in the TDC to the maximum value Max or the peak value is detected.
- a frame before the set frame may be set as the upper limit frame Fa.
- the upper limit frame may be determined by other methods. For example, the TDC change rate may be obtained, and any frame between the frame having the maximum change rate and the frame having the change rate of 0 may be set as the upper limit frame.
- the functionalization processing unit 25 may perform TDC functionalization as follows, for example. That is, the functionalization processing unit 25 performs linear approximation by applying the least square method or the like to the ROI value before the nth (n is 2 to the upper limit frame-1) frame Fn, and derives the equation of the straight line L. May be. Similarly, the functionalization processing unit 25 applies a least square method or the like to the ROI value from the nth frame Fn to the upper limit frame, approximates it to a quadratic function, and derives an expression of the quadratic function F. May be.
- the functionalization processing unit 25 may calculate the sum of squares of errors between the straight line L and the ROI value before the nth frame Fn. Similarly, the functionalization processing unit 25 may calculate a square sum (residual square sum) of errors between the quadratic function F and the ROI values after the nth frame Fn.
- the functionalization processing unit 25 may perform the above-described processing for all n, and specify n with the smallest sum of squares of these errors.
- the functionalization processing unit 25 may specify the straight line L and the quadratic function F in the case of n that minimizes this error as approximate functions.
- the frame that is the intersection (boundary) of the straight line L and the quadratic function F is defined as arrival time (AT), and the ROI value of the frame that is the intersection (boundary) of the straight line L and quadratic function F is the base value (BL). It is good.
- the functionalization processing unit 25 may determine the arrival time and base value of the aorta region from the TDC of the ROI value.
- the TDC is functionalized with two functions of a straight line and a quadratic curve as described above, but the present invention is not limited to this.
- the line before the nth frame Fn may be approximated by a straight line L
- the distribution of ROI values after the nth frame Fn may be approximated by a straight line or a multi-order function of third or higher order.
- all ROI distributions up to the upper limit frame may be approximated to a function expressed by a multidimensional polynomial. In these cases, the arrival time and the base value may be determined in the same manner as described above.
- the functionalization processing unit 25 may approximate the TDC with three or more functions. For example, the functionalization processing unit 25 may divide the second frame to the upper limit frame-1 into three or more sections and approximate each section with a predetermined function. In TDC, the CT value may once decrease immediately before the curve rises significantly (the CT value increases rapidly). When such a phenomenon appears, the functionalization processing unit 25 performs a section (first section) in which the CT value is approximately constant and can be linearly approximated, a section in which the CT value decreases (second section), and the subsequent sections. You may divide into the section (3rd section) where CT value rises rapidly.
- the functionalization processing unit 25 may approximate the first section with a straight line, the second section with a quadratic or higher function, and the third section with another quadratic or higher function, for example.
- either the frame serving as the boundary between the first section and the second section or the frame serving as the boundary between the second section and the third section is set as the arrival time, and the ROI value of the frame serving as the arrival time is the base value. It is good.
- the functionalization processing unit 25 may approximate the TDC with a single function.
- the functionalization processing unit 25 may approximate the TDC with a single function by fitting the normal cumulative distribution function or the cumulative distribution function to the TDC.
- the functionalization processing unit 25 may select the standard deviation (SD) and average value of the normal distribution so as to best fit the rising curve of the TDC. .
- SD standard deviation
- the frame closest to ⁇ 3SD of the normal cumulative distribution function approximated to TDC may be used as the arrival time, and the ROI value of the frame serving as this arrival time may be used as the base value.
- the input function data storage unit 13 stores data related to the input function derived by the input function deriving unit 20.
- the input function data storage unit 13 may store an input function table 130.
- FIG. 4A shows an example of the data structure of the input function table 130.
- the input function table 130 may include arrival time 133, base value 135, functions L and F137, and ROI value 139 as data items.
- the ROI value 139 may be an ROI value for each frame from which the TDC is based.
- the ROI value 139 or the function L and F137 corrected so that the arrival time 133 and the base value 135 are the origin is used. May be.
- the output function deriving unit 30 derives, as an output function, a change in the pixel value (CT value) of the CT image due to the contrast agent flowing into the blood vessel of the organ (heart) of the subject. Also good.
- the output function deriving unit 30 may derive the output function in units of pixels instead of in units of ROI.
- the output function deriving unit 30 includes a target pixel extracting unit 31, a temporal change specifying unit 33, a smoothing processing unit 35, and a functionalization processing unit 37.
- the target pixel extraction unit 31 may extract a pixel from which an output function is derived and specify its position on the frame image.
- the pixel extracted here may be a pixel in the heart region in each slice.
- the target pixel extraction unit 31 may perform the following processing for each slice as the first extraction processing. That is, the target pixel extraction unit 31 may select pixels whose CT values are within a predetermined range in all the time-series CT images of a plurality of frames. For example, the target pixel extraction unit 31 may extract, as a target pixel, a pixel that has a CT value in the range of 30 to 200 in all frame images in one slice.
- a region that is a small target outside the target pixel region may be included.
- the object pixel extraction unit 31 regards this area as a processing defect and performs a process of converting to the object pixel. You can go.
- the target pixel extraction unit according to the size of the isolated target area (for example, the number of pixels is a predetermined number or less). 31 may perform processing for excluding this area.
- the target pixel extraction unit 31 may perform the following process for each pixel as the second extraction process. That is, the target pixel extraction unit 31 may select a pixel in a time-series CT image of a plurality of frames based on the amount of change in the CT value of each pixel, for example, the difference between the maximum value and the minimum value. For example, the target pixel extraction unit 31 obtains a difference between the maximum value and the minimum value of the CT value in all the frame images in one slice, and determines a pixel whose difference is a predetermined value (for example, 50 to 150). It may be extracted.
- a predetermined value for example, 50 to 150
- the target pixel extraction unit 31 may perform both the first extraction process and the second extraction process, or may perform only one of them.
- the target pixel extraction unit 31 may perform the following third extraction process. For example, after the arrival time is specified in a process to be described later based on the pixel that is the target pixel by performing the first and / or the second extraction process, the target pixel extraction unit 31 performs this process as the third extraction process. A pixel whose specified arrival time is earlier than the arrival time 133 of the input function data is specified. Then, the target pixel extraction unit 31 may exclude the specified pixel from the target pixel.
- the target pixel extraction unit 31 may specify the position of the pixel extracted for each slice here as the target pixel position (internal organ pixel position) that is the target for calculating the output function.
- the temporal change specifying unit 33 may specify the temporal change of the CT value of the pixel at the target pixel position (intra-organ pixel position) based on the image data of time-series CT images of a plurality of frames. For example, the temporal change specifying unit 33 may generate a TDC as a temporal change of the CT value of the target pixel position for each slice.
- FIG. 5A is a diagram illustrating an example of a TDC of a CT value at a target pixel position.
- the circle ( ⁇ ) plot in the figure is the CT value. While the above-described temporal change specifying unit 23 generates TDC based on the ROI value, the temporal change specifying unit 33 may generate TDC using the CT value itself.
- the smoothing processing unit 35 may smooth the temporal change specified by the temporal change specifying unit 33.
- the smoothing processing unit 35 may approximate TDC with an m-order function (m is 3 or more).
- the smoothing processing unit 35 may approximate the TDC for each pixel generated by the temporal change specifying unit 33 to a quintic function using, for example, the least square method. This is because, as shown in FIG. 5A, the CT values (circle plots) vary greatly, so that the TDC generated by the temporal change specifying unit 33 is smoother than the TDC generated by the temporal change specifying unit 23. It does not become a curve. Therefore, the smoothing processing unit 35 may approximate the TDC to a multi-order function and perform smoothing.
- the smoothing processing unit 35 may approximate the TDC of the CT value to an m-order function using a least square method or the like.
- a curve indicated by a square ( ⁇ ) plot in FIG. 5A is a quintic function approximated by the smoothing processing unit 35, and each square is a value sampled from the quintic function.
- the TDC of the CT value may be smoothed by a method other than fitting to an m-order function. For example, when generating a TDC, an average value with surrounding pixels may be obtained and used, or the TDC may be smoothed by a moving average.
- the functionalization processing unit 37 may specify the eye rival time at which the contrast agent has reached for each pixel based on the temporal change of the CT value.
- the functionalization processing unit 37 may approximate the TDC smoothed in the frame (time) direction for each pixel by the smoothing processing unit 35 with a function.
- FIG. 5B is a diagram in which values obtained by sampling the smoothed TDC are plotted.
- the algorithm of the processing performed by the functionalization processing unit 37 is the same as the processing performed by the functionalization processing unit 25.
- the difference between the processing performed by the functionalization processing unit 37 and the processing performed by the functionalization processing unit 25 is that the functionalization processing unit 25 approximates a predetermined function for the TDC of the ROI value.
- the conversion processing unit 37 is functionalized with respect to a value sampled from the TDC (approximate m-order function) smoothed by the smoothing processing unit 35.
- the functionalization processing unit 37 determines an upper limit frame in the same manner as the functionalization processing unit 25, approximates the m-order function before the upper limit frame with two straight lines and / or curves, or a plurality of functions, and performs an arrival for each pixel.
- the time and base value may be specified.
- the functionalization processing unit 37 may determine the arrival time and the base value for each pixel from the smoothed TDC for each pixel. That is, the functionalization processing unit 37 may determine an output function for each pixel.
- the functionalization processing unit 37 may approximate the function to a non-smoothed TDC. That is, the process of the smoothing process part 35 does not need to be performed.
- the output function data storage unit 15 stores data related to the output function derived by the output function deriving unit 30.
- An output function table 150 is stored in the output function data storage unit 15.
- FIG. 4B shows an example of the data structure of the output function table 150.
- the output function table 150 may include, for example, a slice number 151, a pixel position 153, an arrival time 155, a base value 156, functions L and F157, and a sampling value 159 as data items. Good.
- the sampling value 159 corresponds to the ROI value 139 of the input function table 130. That is, the sampling value 159 may be a value obtained by sampling the smoothed TDC that is the target of the functionalization processing unit 37.
- the blood flow analysis processing unit 50 uses this output function for blood flow analysis, as in the case of the input function, the sampling value 159 or the functions L and F157 are corrected so that the arrival time 155 and the base value 156 are the origin. You may be made to use what was done.
- the blood flow analysis processing unit 50 may perform a quantitative analysis of the blood flow of the heart based on the input function and the output function.
- the blood flow analysis processing unit 50 refers to the input function table 130 and the output function table 150 and performs predetermined analysis processing.
- a technique for quantitative analysis of the blood flow rate for example, the Patlake Plot method, the Devolution method, or the like may be used.
- the result of quantitative analysis by the blood flow analysis processing unit 50 may be displayed on the display device 5.
- each segment may be displayed in a display mode corresponding to each blood flow rate.
- the display device 5 displays an image in which the heart region is divided into a plurality of arbitrary segments and displayed, each segment may be displayed in a display mode corresponding to each blood flow rate.
- the display device 5 displays an image in which the heart region is divided into a plurality of arbitrary segments and displayed, each segment may be displayed in a display mode corresponding to each blood flow rate.
- storage part 11 you may make it the display aspect of each pixel in the 3D image become a thing according to the blood flow rate. .
- the display of the result of the quantitative analysis by the blood flow analysis processing unit 50 described above may be displayed side by side or overlaid with the CT image, for example.
- evaluation of coronary artery stenosis and myocardial ischemia can be performed simultaneously in the case of myocardial infarction or angina.
- the CT image may be displayed in a display mode corresponding to the time difference between the arrival time of each pixel in the myocardial region and the arrival time of the input function.
- the CT image may be displayed as a 3D image based on the coordinate information of each pixel.
- time difference between the arrival time of the input function and the arrival time of the myocardium. This is because the input function is based on a change in the pixel value of the aorta region upstream from the myocardium.
- This time difference differs depending on, for example, the distance and state of the blood flow path to each region of the myocardium. For example, the time difference of arrival time becomes large at a location where the distance of the blood flow path from the aorta is long, such as near the tip of the heart. For example, if there is a blood channel having a large resistance due to clogging of blood vessels, the time difference in the arrival time of the myocardial region ahead of the blood channel becomes large.
- FIG. 6 is a flowchart showing the entire processing procedure of the input function derivation processing.
- the input function deriving unit 20 selects a slice image in which the region of the aorta is shown, and reads out the image data of the frame image of the slice from the image data storage unit 11 (S11).
- the selection of the slice may be automatically performed by the input function deriving unit 20, or the automatically selected slice may be confirmed after being confirmed by an analyst and then corrected as necessary.
- the ROI setting unit 21 sets the ROI according to the operation of the analyst (S13).
- the temporal change specifying unit 23 calculates the ROI values for all the frame images and draws the TDC based on the ROI values (S15).
- the functionalization processing unit 25 specifies the upper limit frame Fa that defines the range of frames from which the input function is derived based on the TDC (S17). Then, the functionalization processing unit 25 performs functionalization processing on frames before the upper limit frame Fa and specifies the arrival time and the base value (S19).
- FIG. 7 is a flowchart showing a detailed procedure of processing for specifying the arrival time and the base value performed by the functionalization processing unit 25.
- the functionalization processing unit 25 first sets a variable n indicating a frame No to 1 (S31).
- the functionalization processing unit 25 adds 1 to n (S33).
- the functionalization processing unit 25 approximates the ROI value of the frame before the nth frame to one straight line L (S35).
- the functionalization processing unit 25 approximates the ROI value of the frame after the nth frame and before the upper limit frame to the quadratic function F (S37).
- the functionalization processing unit 25 calculates the least square error of the ROI value of the frame before the straight line L and the nth frame obtained by the above processing. Similarly, the functionalization processing unit 25 calculates a least square error between the quadratic function F and the ROI value of the frame after the nth frame and before the upper limit frame. The functionalization processing unit 25 calculates the sum of these errors (S39).
- the functionalization processing unit 25 repeats the processing from steps S53 to S59 as long as n is smaller than the upper limit frame -1 (S41).
- the functionalization processing unit 25 identifies n having the smallest sum of the least square errors from among them (S43).
- the functionalization processing unit 25 sets the frame that is the intersection of the straight line L and the quadratic function F at n specified in step S63 as arrival time, and sets the ROI value at that time as the base value.
- the base value may be the height (Y intercept) of the straight line L (S45).
- FIG. 8 is a flowchart showing the entire processing procedure of the output function derivation processing.
- the output function deriving unit 30 selects one slice image, and reads the image data of the frame image of the slice from the image data storage unit 11 (S51).
- the target pixel extraction unit 31 regards, as all the pixels in the selected slice, the pixels whose CT values in all the frame images satisfy the above-described conditions as the pixels in the heart region, and extracts these pixels as the target pixels ( S53).
- the temporal change specifying unit 33 selects one pixel from the target pixels extracted in S53 (S55), and draws a TDC for the selected pixel (S57).
- the smoothing processing unit 35 performs smoothing by approximating the TDC drawn in S57 to a quintic function (S59). Further, the smoothing processing unit 35 specifies an upper limit frame Fa that defines a range of frames from which an output function is derived based on the smoothed TDC (S61). The functionalization processing unit 37 performs functionalization processing on frames before the upper limit frame Fa, and specifies the arrival time and the base value (S63).
- the output function deriving unit 30 performs the processing from steps S55 to S63 for all target pixels (S65).
- the output function deriving unit 30 further performs the processing from steps S53 to S65 for all slices (S67).
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Abstract
Description
methylisophthalamide)を用いて、被験者の心臓を心電図に同期して撮像した、同位相の複数枚のCTのフレーム画像を対象としてもよい。そして、画像処理装置1は、好ましくは、上記の心臓のCT画像を画素単位で解析することにより、心筋の血流の定量値を算出するために用いるアライバルタイム及びベースラインとなる値(以下、ベース値という)を算出する。
11 画像データ記憶部
13 入力関数データ記憶部
15 出力関数データ記憶部
20 入力関数導出部
21 ROI設定部
23 経時変化特定部
25 関数化処理部
30 出力関数導出部
31 対象画素抽出部
33 経時変化特定部
35 平滑化処理部
37 関数化処理部
50 血流解析処理部
Claims (10)
- 造影剤が投与された後の被験者の臓器を撮像した、複数フレームの時系列のCT(Computed Tomography)画像の画像データを記憶する記憶手段を備えた画像処理装置のためのコンピュータプログラムであって、
前記画像処理装置に、
臓器の領域内の画素の位置である臓器内画素位置を特定するステップと、
前記複数フレームの時系列のCT画像の画像データに基づいて、前記特定された臓器内画素位置の画素のCT値の経時変化を特定するステップと、
前記特定された経時変化に基づいて、前記臓器内画素位置にある臓器に前記造影剤が到達したアイライバルタイム、及び前記臓器内画素位置の画素のベースとなるCT値であるベース値を特定するステップと、を実行させるコンピュータプログラム。 - 前記コンピュータプログラムは、
前記特定された経時変化においてCT値が急激に上昇した後の所定のフレームを上限フレームと定めるステップと、
前記上限フレーム以前の前記経時変化を2つ以上の関数で近似するステップと、をさらに前記画像処理装置に実行させ、
前記アライバルタイム及び前記ベース値は、前記近似された2つ以上の関数によって特定される、請求項1記載のコンピュータプログラム。 - 前記コンピュータプログラムは、
前記特定された経時変化においてCT値が急激に上昇した後の所定のフレームを上限フレームと定めるステップと、
前記上限フレーム以前の前記経時変化を正規累積分布関数または累積分布関数で近似するステップと、をさらに前記画像処理装置に実行させ、
前記アライバルタイム及び前記ベース値は、前記近似された正規累積分布関数または累積分布関数によって特定される、請求項1記載のコンピュータプログラム。 - 前記コンピュータプログラムは、
前記特定された経時変化をm次関数(mは3以上)で近似するステップをさらに前記画像処理装置に実行させ、
前記上限フレームは、前記近似によって得られたm次関数に基づいて定められ、
前記2つ以上の関数は、前記上限フレーム以前の前記m次関数に対して近似された直線及び2次関数を含む、請求項2に記載のコンピュータプログラム。 - 前記コンピュータプログラムは、
前記臓器内に血液を流入させる血管領域内に複数の画素を含むROI(Region of Interest)を設定するステップをさらに前記画像処理装置に実行させ、
前記CT値の経時変化は、前記設定されたROI内の画素のCT値に基づいて特定されるROI値の経時変化であり、
前記アライバルタイムは、前記特定されたROI値の経時変化に基づいて特定される前記血管領域に前記造影剤が到達したアイライバルタイムであり、
前記ベース値は、前記血管領域の画素のCT値に基づいて定められる、請求項1~4のいずれかに記載のコンピュータプログラム。 - 前記臓器内画素位置を特定するステップでは、前記複数フレームの時系列のCT画像のすべてにおいて、CT値が所定の範囲内である画素を選択する、請求項1~5のいずれかに記載のコンピュータプログラム。
- 画素位置を特定するステップでは、前記複数枚の時系列のCT画像において、CT値の最大値と最小値との差分の値に基づいて選択する、請求項1~6のいずれかに記載のコンピュータプログラム。
- 前記臓器は心臓であり、
前記複数フレームの時系列のCT画像は、心電図に同期して撮像されたCT画像である、請求項1~7のいずれかに記載のコンピュータプログラム。 - 造影剤が投与された後の被験者の臓器を撮像した、複数枚の時系列のCT(Computed Tomography)画像の画像データを記憶する記憶手段と、
臓器の領域内の画素の位置である臓器内画素位置を特定する手段と、
前記複数フレームの時系列のCT画像の画像データに基づいて、前記特定された臓器内画素位置の画素のCT値の経時変化を特定する手段と、
前記特定された経時変化に基づいて、前記臓器内画素位置にある臓器に前記造影剤が到達したアイライバルタイム、及び前記臓器内画素位置の画素のベースとなるCT値であるベース値を特定する手段と、を備えた画像処理装置。 - 造影剤が投与された後の被験者の臓器を撮像した、複数フレームの時系列のCT(Computed Tomography)画像の画像データを記憶する記憶手段を備えた画像処理装置が行う画像処理方法であって、
前記画像処理装置が、
臓器の領域内の画素の位置である臓器内画素位置を特定するステップと、
前記複数フレームの時系列のCT画像の画像データに基づいて、前記特定された臓器内画素位置の画素のCT値の経時変化を特定するステップと、
前記特定された経時変化に基づいて、前記臓器内画素位置にある臓器に前記造影剤が到達したアイライバルタイム、及び前記臓器内画素位置の画素のベースとなるCT値であるベース値を特定するステップと、を行う方法。
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JP7236747B2 (ja) | 2018-05-29 | 2023-03-10 | 国立大学法人愛媛大学 | コンピュータプログラム、画像処理装置、および画像処理方法 |
US11813106B2 (en) | 2018-05-29 | 2023-11-14 | National University Corporation Ehime University | Image processing device, and image processing method utilizing time-series computed tomography (CT) images |
CN112165901B (zh) * | 2018-05-29 | 2024-04-05 | 国立大学法人爱媛大学 | 计算机可读记录介质、图像处理装置、以及图像处理方法 |
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JPWO2016009957A1 (ja) | 2017-04-27 |
EP3170454A4 (en) | 2018-03-21 |
EP3170454A1 (en) | 2017-05-24 |
JP6343004B2 (ja) | 2018-06-13 |
US10102623B2 (en) | 2018-10-16 |
US20170206652A1 (en) | 2017-07-20 |
EP3170454B1 (en) | 2020-05-20 |
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