Method and medical imaging system for generating CT collateral blood flow images through post-processing of dynamic contrast-enhanced CT image information and for simultaneously implementing 3D subtraction CT arteriography, 3D subtraction CT venography, and 4D color CT angiography

Abstract

The present invention relates to a method for generating patient-customized CT collateral images using blood flow time segmentation of dynamic contrast-enhanced CT images performed by an electronic device, and for simultaneously implementing 3D subtraction CT arteriography, 3D subtraction CT venography, and color-coded 4D CT angiography.

Description

[Technical Field]

[0001] The present invention relates to post-processing of dynamic contrast-enhanced CT images, and more particularly to a method and medical imaging system for simultaneously generating CT collateral images and performing 3D subtraction CT arteriography, 3D subtraction CT venography, and color-coded 4D CT angiography. [Background technology]

[0002] CT (Computed Tomography), one of the medical imaging devices widely used for diagnosing and evaluating cerebrovascular diseases, is widely used in hospitals and is one of the most widely used along with MRI (Magnetic Resonance Imaging) due to its relatively easy and quick testing.

[0003] When evaluating patients with acute ischemic stroke through imaging, the key elements are the size of the initial cerebral infarction, detection of blocked vessels, assessment of the ischemic penumbra, and assessment of collateral blood flow and perfusion.

[0004] However, CT images have not been able to accurately determine the size of early cerebral infarction, and existing CT collateral blood flow images obtained through repeated brain CT angiography have low temporal resolution and are only obtained at a set time after the contrast agent is administered, making it impossible to reflect differences in blood flow that vary depending on each patient's cardiovascular condition. Therefore, the assessment of collateral blood flow through CT collateral blood flow images has been limited.

[0005] Currently, technology that generates collateral blood flow images from 4D MR angiography (4D MRA) and perfusion MRI can be considered, but for acute stroke patients who require urgent treatment, CT is preferred over MRI because of its relatively short examination time and good accessibility.

[0006] In conclusion, in cerebrovascular disease, especially acute cerebral infarction, there is a need for the development of CT technology that can evaluate the size of the initial cerebral infarction and ischemic penumbra, blocked blood vessels, and collateral blood flow more quickly and accurately than existing methods. Technology that generates more accurate patient-specific collateral blood flow images and vascular images that separate arteries and veins through a single dynamic contrast-enhanced CT scan obtained using CT perfusion imaging or similar methods will be one good solution for diagnosing cerebrovascular disease. Summary of the Invention [Problem to be solved by the invention]

[0007] SUMMARY OF THE INVENTION An object of the present invention is to provide a method for generating CT collateral blood flow images and CT angiography images more easily and conveniently, and an electronic device for performing the method.

[0008] An object of the present invention is to provide a method for generating customized CT collateral blood flow images, 3D subtraction CT arteriography, 3D subtraction CT venography, and 4D color CT angiography images according to a patient's cardiovascular condition based on accurate blood flow timing, and an electronic device for performing the method. [Means for solving the problem]

[0009] In a method for generating CT Collateral Images using blood flow timing analysis of dynamic contrast-enhanced CT images and simultaneously implementing 3D Subtraction CT Arteriography, 3D Subtraction CT Venography, and Color-coded 4D CT Angiography, the method comprises the steps of: loading dynamic contrast-enhanced CT images containing dynamic blood flow information; arranging the dynamic contrast-enhanced CT images along a time axis; and generating a time-intensity curve (TIC) based on changes in signal intensity of cerebral arteries and cerebral veins in each dynamic contrast-enhanced CT image. generating a CT angiography (3D subtraction CT arteriography, 3D subtraction CT venography, and 4D color CT angiography) image from the dynamic contrast-enhanced CT image based on the divided blood flow periods; and dividing the dynamic contrast-enhanced CT image corresponding to each period based on the divided blood flow periods and averaging the CT signal intensity to generate a CT collateral blood flow image for each period.

[0010] The step of loading the dynamic contrast-enhanced CT image may include the step of loading the contrast-enhanced CT image having time-dependent dynamic blood flow information and anatomical information and converting the image into a four-dimensional array.

[0011] The step of loading the dynamic contrast-enhanced CT image may include a step of arranging the dynamic contrast-enhanced CT image along time so that the dynamic contrast-enhanced CT image includes dynamic blood flow information, and converting and displaying the image using a maximum intensity projection (MIP) technique.

[0012] The generating of the TIC may include generating a first TIC indicating a change in signal intensity of a cerebral artery and a second TIC indicating a change in signal intensity of a cerebral vein from the dynamic contrast-enhanced CT image.

[0013] The step of classifying by blood flow period may include a step of acquiring blood flow period information for classifying the blood flow period into an arterial period, a capillary period, a venous period (early venous period and late venous period), a delayed period, etc., based on the magnitude and range of the signal intensity of the cerebral artery and the cerebral vein, which change over time.

[0014] The step of generating the CT angiography image may include generating a CT angiography image including at least one of a 3D subtraction CT arteriography image, a 3D subtraction CT venography image, and a 4D color CT angiography image from a dynamic contrast-enhanced CT image in which blood flow periods are separated.

[0015] The step of generating the 3D subtraction CT arteriography image may include extracting only the arterial phase signal by subtracting and removing the venous phase signal from the arterial phase signal.

[0016] The step of generating the 3D subtraction CT venography image may include extracting only the venous phase signal by subtracting and removing the arterial phase signal from the venous phase signal.

[0017] The step of generating the 4D color CT angiography image may include a step of generating the 4D color CT angiography image by assigning a color to each blood flow period and sequentially dividing and displaying the blood vessels according to the order of contrast enhancement.

[0018] The 4D color CT angiography may include a step of color coding the blood flow periods by assigning red for the arterial phase, green for the capillary phase, and blue for the venous phase, and adjusting the color weighting according to the contrast enhancement level of the blood vessels shown in each blood flow period, thereby generating a dynamic color CT angiography image in which blood vessels from arteries to capillaries and veins are sequentially separated in accordance with the order in which the blood vessels are contrast enhanced in the dynamic contrast-enhanced CT.

[0019] In an electronic device for generating CT Collateral Images using blood flow time segmentation of dynamic contrast-enhanced CT images and simultaneously implementing 3D Subtraction CT Arteriography, 3D Subtraction CT Venography, and Color-coded 4D CT Angiography, the device loads dynamic contrast-enhanced CT images containing dynamic blood flow information, arranges the dynamic contrast-enhanced CT images along time, and generates a time-intensity curve (TIC) based on the change in signal intensity of the cerebral arteries and venous veins in each dynamic contrast-enhanced CT image. The present invention also includes a processor for generating a CT angiography image from the dynamic contrast-enhanced CT image based on the blood flow period information, dividing the dynamic contrast-enhanced CT image into blood flow periods according to the blood flow period information determined based on the TIC, and generating a CT collateral blood flow image for each period by dividing the dynamic contrast-enhanced CT image into blood flow periods and averaging the CT signal intensities.

[0020] The processor can arrange the dynamic contrast-enhanced CT images along time so that they contain dynamic blood flow information, and convert and display them using a maximum intensity projection (MIP) technique.

[0021] The processor can generate a first TIC showing changes in signal intensity of cerebral arteries and a second TIC showing changes in signal intensity of cerebral veins from the dynamic contrast-enhanced CT image.

[0022] The processor can acquire blood flow period information that classifies the blood flow period into an arterial period, a capillary period, a venous period (early venous period and late venous period), a delayed period, etc., based on the magnitude and range of the signal intensity of the cerebral artery and the cerebral vein, which change over time.

[0023] The processor may generate the CT angiography image, including at least one of a 3D subtraction CT arteriography image, a 3D subtraction CT venography image, and a 4D color CT angiography image, from the dynamic contrast-enhanced CT image in which the blood flow period is divided.

[0024] The processor may assign colors to the blood flow periods and generate the 4D color CT angiography image so that blood vessels are sequentially separated and displayed in accordance with the order of contrast enhancement.

[0025] The processor may generate the CT collateral blood flow image by averaging signal intensities of the dynamic contrast-enhanced CT images included in each blood flow period.

[0026] The processor can generate a color image using contrast enhancement according to the degree of blood flow and color hues (e.g., red-yellow-green-sky blue-blue, etc., from areas with high blood flow to areas with low blood flow) in the CT collateral blood flow image for each blood flow period. [Effects of the Invention]

[0027] According to one embodiment of the present invention, information from a single dynamic contrast-enhanced CT image, which can be captured in a short time, can be converted into images necessary for diagnosing various cerebrovascular diseases through a relatively simple interface operation. Patient-customized CT collateral blood flow images, which could not be accurately realized with existing CT technology, 3D subtraction CT arteriography and 3D subtraction CT venography with arteries and veins separated, and 4D color CT angiography using color coding can be obtained all at once, thereby improving the accuracy and speed of diagnosis of various cerebrovascular diseases, including acute stroke. [Brief explanation of the drawings]

[0028] [Figure 1] 1 is a schematic diagram illustrating a process of generating a CT angiogram and a collateral blood flow image according to an embodiment of the present invention; [Figure 2] 1 is a block diagram illustrating a configuration of an electronic device according to an embodiment of the present invention. [Figure 3] 1 is a diagram illustrating an operation flowchart of an electronic device according to an embodiment of the present invention. [Figure 4] 1 is a diagram illustrating an interface of a program implementing a method for generating a CT angiogram and a collateral blood flow image according to an embodiment of the present invention. [Figure 5] 1 is a diagram illustrating transformed images arranged along a time axis according to an embodiment of the present invention; [Figure 6] 1 is a diagram illustrating a time-signal intensity curve according to an embodiment of the present invention. [Figure 7] 1 is a diagram illustrating a blood flow timing division according to an embodiment of the present invention; [Figure 8] 1 is a diagram illustrating a CT angiography image generated according to an embodiment of the present invention. [Figure 9] 1 is a diagram illustrating a program interface for adjusting the output form and color of a CT angiography image generated according to an embodiment of the present invention. [Figure 10] 1 is a diagram illustrating a cross-sectional view of a CT collateral blood flow image according to an embodiment of the present invention; [Figure 11] 1 is a diagram illustrating a CT collateral blood flow image generated according to an embodiment of the present invention. [Figure 12] 1 is a diagram illustrating a CT collateral blood flow image generated according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0029] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The detailed description disclosed below together with the accompanying drawings is intended to describe exemplary embodiments of the present invention and is not intended to show the only embodiments in which the present invention can be practiced. In the process of clearly describing the present invention in the drawings, parts that are not related to the invention may be omitted, and the same reference numerals may be used throughout the specification for the same or similar components.

[0030] FIG. 1 is a schematic diagram illustrating a process of generating a CT angiography image and a collateral blood flow image according to an embodiment of the present invention.

[0031] Referring to FIG. 1, an image generating method according to an embodiment of the present invention inputs and processes dynamic contrast-enhanced CT images 10 into an electronic device 100 to obtain a CT angiography image 20 and a collateral blood flow image 30.

[0032] The electronic device 100 according to an embodiment of the present invention is an apparatus for generating a CT angiography image 20 and a collateral blood flow image 30 from a dynamic contrast-enhanced CT image 10 using blood flow time segmentation of the dynamic contrast-enhanced CT image 10, and may be embodied as a computer, a server, etc. The electronic device 100 includes a program that embodies a method for generating the CT angiography image 20 and the collateral blood flow image 30.

[0033] In one embodiment of the present invention, the dynamic contrast-enhanced CT image 10 refers to an image converted for use in a program from raw CT images taken while injecting a contrast agent into the blood vessels of a subject (e.g., a patient) using CT equipment. The CT angiography image 20 and collateral blood flow image 30 refer to images obtained by post-processing the dynamic contrast-enhanced CT image 10 to confirm the anatomical structure of cerebral blood vessels and the blood circulation process.

[0034] Specifically, the CT angiography images 20 include 3D subtracted grayscale and color CT arteriography 21, 3D subtracted grayscale and color CT venography 22, 3D color CT angiography 23, and 4D color CT angiography 24, and the collateral flow images 30 include grayscale collateral flow images 31 and color collateral flow images 32.

[0035] As mentioned above, currently, CT angiography is used to obtain images to predict collateral blood flow, but because the CT equipment repeatedly obtains images at a set time, it is not possible to reflect differences in blood flow velocity due to the cardiovascular condition of each patient, making it difficult to accurately evaluate collateral blood flow. CT equipment has superior accessibility to cerebrovascular disease compared to other imaging equipment, and as the performance of CT equipment continues to improve, there is an increasing need to develop cerebrovascular disease evaluation technology that utilizes this technology.

[0036] This invention proposes a technology that utilizes blood flow timing analysis and segmentation of dynamic contrast-enhanced CT images to simultaneously obtain venous subtraction 3D arteriography, arterial subtraction 3D venography, 4D color angiography, and patient-customized collateral blood flow images from CT images.

[0037] Hereinafter, the configuration and operation of a diagnostic apparatus for cerebrovascular disease according to one embodiment of the present invention will be specifically described with reference to the drawings.

[0038] FIG. 2 is a block diagram illustrating the configuration of an electronic device according to an embodiment of the present invention.

[0039] The electronic device 100 according to one embodiment of the present invention includes an input unit 110 , a communication unit 120 , a display unit 130 , a memory 140 , and a processor 150 .

[0040] The input unit 110 generates input data in response to a user input of the electronic device 100. For example, the user input may be a user input for starting the operation of the electronic device 100, a user input for operating a program interface (hereinafter referred to as an interface) that embodies a method for generating a CT angiography image and a collateral blood flow image, or any other user input required for generating a CT angiography image and a collateral blood flow image using a CT image of a patient, without limitation.

[0041] The input unit 110 includes at least one input means, and may include a keyboard, a keypad, a dome switch, a touch panel, a touch key, a mouse, a menu button, etc.

[0042] The communication unit 120 communicates with an external device such as a server to receive CT images, blood flow timing information based on signal strength, etc. To this end, the communication unit 120 can perform wireless communication such as 5G (5th generation communication), LTE-A (long term evolution-advanced), LTE (long term evolution), Wi-Fi (wireless fidelity), etc.

[0043] In addition, the communication unit 120 can perform wired communication such as LAN (local area network), WAN (Wide Area Network), or power line communication, and the communication unit 120 can support the establishment of a wired communication channel between the electronic device 100 and an external device and the performance of communication through the established communication channel.

[0044] The display unit 130 displays display data according to the operation of the electronic device 100. The display unit 130 may display a screen for loading a converted image, a screen for displaying a time-signal intensity curve, a screen for displaying generated CT angiography images and collateral blood flow images, a screen for receiving user input, etc. In addition, any screen that can be displayed while implementing a program, such as an interface, may be applied without limitation.

[0045] The display unit 130 may include a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a micro electro mechanical systems (MEMS) display, and an electronic paper display. The display unit 130 may be combined with the input unit 110 and implemented as a touch screen.

[0046] The memory 140 stores an operating program for the electronic device 100. The memory 140 includes non-volatile storage that can store data (information) regardless of whether power is supplied, and volatile memory that loads data to be processed by the processor 150 and cannot store data unless power is supplied. Examples of storage include flash memory, hard disk drives (HDDs), solid-state drives (SSDs), and read-only memory (ROMs), while examples of memory include buffers and random access memory (RAMs).

[0047] The memory 140 can store CT images, time-signal intensity curves, blood flow timing information based on vascular signal intensity, etc. received from external devices, as well as calculation programs required for the processes of loading CT images, generating time-signal intensity curves, dividing blood flow timing, and generating CT angiography images and collateral blood flow images.

[0048] Processor 150 can execute software such as a program to control at least one other component (e.g., hardware or software component) of electronic device 100 and perform various data processing or calculations. According to one embodiment, processor 150 can include a main processor that functions as a central processing unit (CPU) or application processor, and a graphics processing unit (GPU) that can operate independently or together with the main processor.

[0049] The processor 150 loads dynamic contrast-enhanced CT images containing dynamic blood flow information, arranges the dynamic contrast-enhanced CT images along a time axis, and generates a time-intensity curve (TIC) based on changes in signal intensity of the cerebral arteries and venous veins in each dynamic contrast-enhanced CT image. The processor 150 analyzes blood flow period information determined based on the TIC to classify the dynamic contrast-enhanced CT images into each blood flow period, generates CT angiography images from the dynamic contrast-enhanced CT images based on the classified blood flow periods, and generates CT collateral blood flow images for each period by dividing the dynamic contrast-enhanced CT images corresponding to each period based on the classified blood flow periods and averaging the CT signal intensities.

[0050] FIG. 3 is a diagram illustrating an operation flowchart of an electronic device according to an embodiment of the present invention.

[0051] According to one embodiment of the present invention, the processor 150 of FIG. 2 loads dynamic contrast-enhanced CT images containing dynamic blood flow information, aligns them in time and space, and then outputs subtracted MIP images (S10).

[0052] According to one embodiment of the present invention, the original CT images include high temporal resolution dynamic (multi-temporal) contrast-enhanced images obtained in a manner identical or similar to CT perfusion imaging.

[0053] At this time, the processor 150 aligns the original CT images in time and space to prepare them so that dynamic blood flow information of blood vessels and tissues can be accurately displayed.

[0054] The processor 150 can arrange the dynamic contrast-enhanced CT images along time so that they contain dynamic blood flow information, and convert and display them using a maximum intensity projection (MIP) technique.

[0055] More specifically, the processor 150 reads a CT image in Digital Imaging and Communications in Medicine (DICOM) format, and generates subtracted Maximum Intensity Projection (MIP) vascular images by removing the brain parenchyma and skull through parameter setting, and arranges the images in chronological order. A subtracted MIP image (hereinafter also referred to as a converted image) according to one embodiment is shown in FIG. 5.

[0056] According to one embodiment of the present invention, the processor 150 generates time-intensity curves (TICs) for the cerebral arteries and cerebral veins based on the signal intensity changes of the cerebral arteries and venous veins in the dynamic contrast-enhanced CT images (S20).

[0057] The processor 150 aligns the dynamic contrast-enhanced CT images in the order of acquisition time based on the DICOM header information, calculates the signal intensities of the cerebral arteries and cerebral veins, and generates a TIC.

[0058] The processor 150 can generate a first TIC that shows the change in signal intensity of the cerebral arteries over time in the dynamic contrast-enhanced CT images, and a second TIC that shows the change in signal intensity of the cerebral veins over time.

[0059] At this time, in order to more accurately distinguish the blood flow period, the processor 150 may designate regions of interest (ROI) in the cerebral arteries (the M1 segment of the normal middle cerebral artery, the A1 segment of the anterior cerebral artery, or the distal portion of the internal carotid artery) and the cerebral veins (the superior sagittal sinus, the straight sinus, or the distal portion of a large cortical vein), and generate a first TIC and a second TIC based on the signal intensity of the designated ROI. A TIC according to one embodiment is shown in FIG.

[0060] According to one embodiment of the present invention, the processor 150 analyzes the blood flow period information determined based on the TIC and classifies the dynamic contrast-enhanced CT image into each blood flow period (S30).

[0061] According to one embodiment of the present invention, blood flow phase information (hereinafter referred to as blood flow phase information) based on an analysis of the signal intensities of arteries and veins that change depending on blood flow means information that classifies the blood flow phase into an arterial phase, a capillary phase, a venous phase, and a delayed phase based on the numerical change and range caused by the increase and decrease in the signal intensity of cerebral arteries and cerebral venous signals.

[0062] For example, the section from the start of the arterial phase (arterial start phase) when the signal intensity of the cerebral arteries begins to increase to the highest arterial phase (arterial peak phase) when it reaches its highest value is classified as the arterial phase, the section between the highest arterial phase and the highest venous phase (venous peak phase) is classified as the capillary phase, the section from the highest venous phase to the point when the venous signal intensity begins to decrease and become horizontal (venous end phase) is classified as the venous phase, and the period after the venous phase is classified as the delayed phase. Furthermore, the venous phase can be classified into an early venous phase and a late venous phase based on its center point.

[0063] According to one embodiment of the present invention, the processor 150 can receive a user input specifying the blood flow period based on the magnitude and range of the signal strength of the cerebral artery and cerebral vein through the input unit 110 to obtain blood flow period information, or can receive predefined blood flow period information from the outside through the communication unit 120.

[0064] According to one embodiment of the present invention, the processor 150 generates a CT angiography image from the dynamic contrast-enhanced CT image based on the segmented blood flow period (S41).

[0065] The processor 150 can generate a CT angiography image using the maximum signal intensity of an image included in each blood flow phase. At this time, the blood flow phase can include at least one of an arterial phase, a capillary phase, an early venous phase, a late venous phase, and a delayed phase, and can include multiple blood flow phases (multi-phases).

[0066] The processor 150 can generate CT angiography images including at least one of 3D subtraction CT arteriography, 3D subtraction CT venography, and 4D color CT angiography images from the dynamic contrast-enhanced CT images with the blood flow periods separated. In particular, for 3D subtraction CT arteriography, an angiography image showing only arteries is generated by selectively subtracting venous signals from arterial phase signals, and for 3D subtraction CT venography, an angiography image showing only veins is generated by selectively subtracting arterial signals from venous phase signals.

[0067] The technical details of the subtraction will be explained in detail in the process of generating each image.

[0068] According to one embodiment of the present invention, processor 150 divides the dynamic contrast-enhanced CT image corresponding to each divided blood flow period and averages the signal intensities to generate a CT collateral blood flow image for each period (S42). There is no set order for the operations of S41 and S42, and they can be performed sequentially or in parallel depending on the situation.

[0069] Before generating a cross-sectional collateral blood flow image from the dynamic contrast-enhanced CT image, the processor 150 outputs a central sagittal image and can rotate the image to be parallel to the anterior commissure-posterior commissure line (AC-PC line) or a user-defined plane to obtain an image that is parallel to other brain images, such as MRI.

[0070] Then, processor 150 can generate a CT collateral blood flow image of a cross section or a slice parallel to a desired plane based on the set image reconstruction information. At this time, the image reconstruction information can include the thickness of the image slice, the distance between slices, the total number of images, etc. Processor 150 can receive a user input for setting the image reconstruction information through input unit 110 or can generate a collateral blood flow image based on preset image reconstruction information.

[0071] The processor 150 can generate a collateral blood flow image by averaging the signal intensities of the images included in each blood flow period, where the blood flow period is as previously described in step S41.

[0072] According to one embodiment of the present invention, by generating various images at once, the diagnostic and evaluation functions of cerebrovascular diseases using CT can be improved, thereby increasing the usability of CT equipment and convenience for patients.

[0073] FIG. 4 is a diagram illustrating an interface of a program that embodies a method for generating a CT angiogram and a collateral blood flow image according to an embodiment of the present invention.

[0074] A series of operations for generating CT angiography images and CT collateral blood flow images described in relation to Fig. 3 can be performed through the interface 400 of Fig. 4. At this time, the design and detailed configuration of the interface 400 are not limited to those shown in this drawing and can be adjusted as necessary.

[0075] The interface 400 according to one embodiment of the present invention is an interface that uses dynamic contrast-enhanced CT images. It can be divided into a first interface 410 that generates a CT angiography image and a CT collateral blood flow image using original CT images of 1 mm or less, and a second interface 430 that generates only a CT collateral blood flow image using original CT images reconstructed from thicker slices (e.g., images reconstructed from 1-5 mm slices). However, since the detailed configurations and operations of each interface are similar, the first interface 410 will be used as a reference for description.

[0076] According to one embodiment of the present invention, the detailed configurations included in the interface 400 may be selected automatically or sequentially by receiving a user input to select the detailed configurations, or by program operation, and the method of operation does not limit the present invention.

[0077] First, in relation to S10 of FIG. 3, the processor 150 can input (load) an original CT image and generate a converted image through icons such as Open Dicom Images 411 and Open Mat File 412.

[0078] Then, through an icon such as Display SUB MIP 413, processor 150 subtracts the brain region image from the brain region image before the contrast agent was injected from the brain region image after the contrast agent was injected, processes the images using the MIP technique, and outputs the images in chronological order with appropriate brightness and darkness. The converted images according to one embodiment are shown in FIG.

[0079] Furthermore, when the brightness icon 414 is selected, a new window 440 is displayed, and the processor 150 can go through a process of optimizing the window width and window level after displaying the brightness of the image as a histogram.

[0080] Other configurations and functions will be described below with reference to FIGS. 6, 7 and 9.

[0081] According to one embodiment of the present invention, image processing can be performed through a simple interface operation using a captured dynamic contrast-enhanced CT image.

[0082] FIG. 5 is a diagram illustrating transformed images arranged along a time axis according to an embodiment of the present invention.

[0083] The images shown in FIG. 5 are converted images containing dynamic blood flow information, as described in S10 of FIG. 3, and are arranged in chronological order.

[0084] At this time, the processor 150 can number each of the converted images arranged in chronological order for future classification of blood flow periods.

[0085] FIG. 6 is a diagram illustrating a time-signal intensity curve according to an embodiment of the present invention.

[0086] As described in relation to S20 of FIG. 3, the processor 150 outputs a maximum intensity CT image with enhanced blood vessels using the ROI (Artery) icon 415 and the ROI (Vein) icon 416 (see interface 400 of FIG. 4), sets optimal regions of interest (ROIs) for the cerebral arteries and cerebral veins in the output image, and then generates time-intensity curves (TICs) for the cerebral arteries and cerebral veins based on the changes in signal intensity extracted from the ROIs for the cerebral arteries and cerebral veins, respectively.

[0087] The processor 150 may generate a first TIC 610 showing the change in signal intensity of the cerebral arteries over time in the dynamic contrast-enhanced CT images, and a second TIC 620 showing the change in signal intensity of the cerebral veins over time.

[0088] The first TIC 610 and the second TIC 620 in Figure 6 are generated based on the converted images from No. 1 (the first image) to No. 25 (the last image) in Figure 5. In this case, the x-axis Phase of the TIC is generated to correspond to each image number and can also be changed to the time axis. Therefore, it can be seen that the image with the highest signal intensity of the cerebral artery in the first TIC 610 is No. 8 in Figure 5, and the image with the highest signal intensity of the cerebral vein in the second TIC 620 is No. 12 in Figure 5.

[0089] Hereinafter, the operation of dividing the blood flow period using the generated TIC will be described with reference to FIG.

[0090] 7 is a diagram illustrating a blood flow period classification according to an embodiment of the present invention, in which an interface 400 in FIG. 7 is the same as the interface 400 in FIG.

[0091] As described in relation to S30 of FIG. 3, the processor 150 distinguishes blood flow periods for constructing CT angiography images and CT collateral blood flow images using TICs based on changes in signal intensity of cerebral arteries and cerebral veins.

[0092] For example, processor 150 can identify image number 417 corresponding to first TIC 610 and second TIC 620 in Figure 6. Referring to Figure 7, image numbers corresponding to the start of the arterial phase (Start Arterial phase), the end of the arterial phase (End Arterial phase), the start of the venous phase (Start Venous phase), and the end of the venous phase (End Venous phase) are 2, 8, 12, and 19, respectively, and processor 150 can generate 418 CT collateral blood flow images based on the classified blood flow phases.

[0093] Additionally, processor 150 can select a Gaussian filter magnitude 419 for softening the CT collateral blood flow image before outputting the image.

[0094] Below, CT angiography images will be explained.

[0095] FIG. 8 is a diagram illustrating a CT angiography image generated according to an embodiment of the present invention.

[0096] Figure 8 illustrates CT angiography images generated through the operation process of Figure 3. More specifically, Figure 8 illustrates 3D subtraction CT arteriography 810, 820, 3D subtraction CT venography 830, 840, and 3D CT angiography 850, 860 images in grayscale and color, and 4D color CT angiography image 870 in chronological order.

[0097] Also, the interface for generating each CT angiography image will be described with reference to FIG.

[0098] Below, we will take a closer look at the process of generating each image.

[0099] The processor 150 separates and calculates image information of blood vessels from the time when the arterial signal begins to increase to the arterial peak phase, from the time when the venous signal intensity decreases and becomes horizontal from the venous peak phase, and between the arterial peak phase and the venous peak phase, to generate venous subtraction CT arteriography images 810 and 820 and arterial subtraction CT venography images 830 and 840.

[0100] Specifically, processor 150 subtracts venous signals from arterial signals to generate an arterial mask with a value greater than or equal to 0, updates the arterial maximum intensity projection (MIP) with the value obtained by multiplying the arterial mask by the arterial mask, and generates 3D subtraction CT arteriograms 810 and 820. Processor 150 also subtracts arterial signals from venous signals to generate a venous mask with only those with a value greater than or equal to 0, updates the venous MIP with the value obtained by multiplying the venous mask by the venous mask, and generates 3D subtraction CT venograms 830 and 840. In this case, to optimally subtract cerebral veins and cerebral arteries in 3D subtraction CT arteriograms and 3D subtraction CT venograms, respectively, an appropriate reference time point for subtraction can be determined around the time point at which the signal intensity difference between the cerebral artery and cerebral vein is greatest (the time point indicated by the gray dotted line in FIG. 6 , e.g., the sixth phase).

[0101] The processor 150 can classify blood vessels in venous subtraction CT arteriography, arterial subtraction CT venography, and 3D and 4D CT angiography in different colors depending on the blood flow period.

[0102] For example, the processor 150 displays arterial phase blood vessels in red (RED) from the point at which the arterial signal begins to rise until the arterial peak phase, displays venous phase blood vessels in blue (BLUE) from the venous peak phase until the venous signal intensity decreases and becomes horizontal, and displays capillary phase blood vessels between the arterial peak phase and the venous peak phase in green (GREEN). At this time, to make blood vessels in the desired blood flow phase more visible, the color intensities (weighting) of red, blue, and green are adjusted to distinguish arteries, veins, and capillaries, etc., and dynamic changes in blood flow can be seen in 4D color CT angiography.

[0103] FIG. 9 illustrates a program interface for adjusting the output form and color of a CT angiography image generated according to an embodiment of the present invention.

[0104] At this time, the interface 400 in FIG. 9 is the same as the interface 400 in FIGS.

[0105] According to one embodiment of the present invention, the processor 150 can rotate the image or add color to the blood vessels to further enhance the usability of the image.

[0106] To this end, the interface 400 includes sections 420 and 421 for adjusting the rotation of the image, and a section 422 for adjusting the hue of the image.

[0107] The processor 150 can rotate the CT angiography image by setting angles for each of the x-axis, y-axis, and z-axis, and can generate a rotational angiography image that shows cerebral blood vessels by continuously rotating a specific angle (e.g., 12 degrees) based on a specific axis (e.g., the z-axis).

[0108] In addition, the processor 150 can specify a color for each blood flow period to check the movement of blood flow for each period. At this time, the processor 150 can specify a color by setting a weight value for each of R (Red), G (Green), and B (Blue), and can receive a user input specifying the color through the input unit 110.

[0109] Alternatively, the color may be designated by selecting one of predetermined colors (e.g., red for the arterial phase and blue for the venous phase), and the method of designating the color does not limit the present invention.

[0110] FIG. 10 is a diagram illustrating a CT collateral blood flow image reconstructed as a cross-sectional image according to an embodiment of the present invention.

[0111] In this case, the interface 400 in Figure 10 is the same as the interface 400 in Figures 4, 7, and 9. The interface 400 temporarily creates and outputs a central sagittal plane CT image using a portion of the original CT image, and to obtain an image parallel to other images, the interface 400 can first rotate the image so that it is as parallel as possible to the anterior commissure-posterior commissure line (AC-PC line) or so that it is parallel to a plane defined by the user.

[0112] It includes a section 423 for converting the image into a cross-sectional image of collateral blood flow based on this.

[0113] As described in connection with S42 of FIG. 3, processor 150 reconstructs the dynamic contrast-enhanced CT image into a transverse plane or a specific plane selected by the user based on the set information, and generates a CT collateral blood flow image based on the blood flow period.

[0114] The processor 150 can reconstruct a CT collateral blood flow image as a cross-sectional image based on the set image reconstruction information. At this time, the reconstruction information can include image slice thickness, slice distance, total number of images, etc. However, reconstruction of a CT collateral blood flow image is not limited to a cross-sectional image, and various cross-sectional images are possible.

[0115] The processor 150 can average the signal intensities of the images included in each blood flow period to generate a CT collateral blood flow image, which is illustrated in FIGS.

[0116] 11 and 12 are diagrams illustrating collateral blood flow images generated according to an embodiment of the present invention.

[0117] Figure 11 is a CT collateral blood flow image created using an original CT image of less than 1 mm, and Figure 12 is a CT collateral blood flow image created using an original CT image reconstructed from a slightly thicker slice (5 mm).

[0118] In addition, Figures 11 and 12 show cross-sectional CT collateral blood flow images in the order of blood flow phase: arterial phase, capillary phase, early venous phase, late venous phase, and delayed phase.

[0119] According to one embodiment of the present invention, the processor 150 can generate a color image containing hues classified according to the magnitude of signal intensity to indicate the degree of blood flow for each collateral blood flow image at each time point.

[0120] [National research and development project that supported this invention] 1. [Project unique number]1711164322 [Project Number] 2020R1F1A1071619 [Department name] Ministry of Science, ICT and Communication [Name of issue management (specialized) organization] Korea Research Foundation [Research project name] Individual basic research (Ministry of Science, Technology, Information and Communication) [Research title] Development of imaging techniques and imaging indices to predict histological prognosis, malignant course, and bleeding risk in patients with acute ischemic stroke [Contribution rate] 10 / 100 [Name of project executing organization] Catholic University Industry-Academia Collaboration Group [Research Period] 2020.06.01~2023.02.28 2. [Project unique number]1345371257 [Project Number] 00248375 (RS-2023-00248375) [Department name] Education Department [Name of issue management (specialized) organization] Korea Research Foundation [Research Project Name] Construction of Academic Research Infrastructure for Science and Engineering (Creative Challenge Research Infrastructure Support Project) [Research title] Development and clinical validation of artificial intelligence technology for generating and analyzing CT collateral blood flow images [Contribution rate] 20 / 100 [Name of project executing organization] Catholic University Industry-Academia Collaboration Group [Research Period] 2023.06.01~2026.05.31 3. [Project unique number]1711197626 [Project Number] 00252980 (RS-2023-00252980) [Department name] Ministry of Science, ICT and Communication [Name of issue management (specialized) organization] Korea Research Foundation [Research project name] Individual basic research (Ministry of Science, Technology, Information and Communication) [Research title] Research for realizing precision medicine for acute ischemic stroke and developing patient-tailored evaluation technology [Contribution rate] 20 / 100 [Name of project executing organization] Catholic University Industry-Academia Collaboration Group [Research Period] 2023.06.01~2026.02.28 4. [Project unique number]1465040847 [Project Number] 00266130 (RS-2023-00266130) [Department name] Health and Welfare Department [Name of issue management (specialized) organization] Korea Health Industry Development Agency [Research Project Name] Development of technology to solve clinical problems related to neurological disorders (R&D) [Research title] Collateral blood flow image generation and analysis software development and commercialization infrastructure construction [Contribution rate] 50 / 100 [Name of project executing organization] Catholic University Industry-Academia Collaboration Group [Research Period] 2023.07.01~2026.12.31

Claims

1. A method for generating CT collateral images using blood flow time segmentation of dynamic contrast-enhanced CT images performed by an electronic device and simultaneously implementing 3D subtraction CT arteriography, 3D subtraction CT venography, and color-coded 4D CT angiography, loading a dynamic contrast-enhanced CT image containing dynamic blood flow information; arranging the dynamic contrast-enhanced CT images along a time axis and generating a time-intensity curve (TIC) based on changes in signal intensity of the cerebral arteries and cerebral veins in each dynamic contrast-enhanced CT image; classifying the dynamic contrast-enhanced CT images into blood flow periods according to the blood flow period information determined based on the TIC; generating a CT angiography image from the dynamic contrast-enhanced CT image based on the segmented blood flow epochs; The method includes the step of dividing dynamic contrast-enhanced CT images corresponding to each blood flow period based on the divided blood flow periods, and averaging the CT signal intensities to generate CT collateral blood flow images for each period.

2. The step of loading the dynamic contrast-enhanced CT image includes:

2. The method of claim 1, further comprising: arranging the dynamic contrast-enhanced CT images along time to include the dynamic blood flow information, and converting and displaying the images using a maximum intensity projection (MIP) technique to enhance blood vessels.

3. The step of generating a TIC comprises: The method of claim 1, further comprising generating a first TIC indicating a change in signal intensity of a cerebral artery over time and a second TIC indicating a change in signal intensity of a cerebral vein over time from the dynamic contrast-enhanced CT image.

4. The step of classifying the blood flow period based on the TIC includes: The method of claim 3, further comprising a step of acquiring blood flow period information that classifies the blood flow period into an arterial period, a capillary period, an early venous period, a late venous period, and a delayed period based on the magnitude and range of the signal intensity of the cerebral artery and the cerebral vein, which change over time.

5. The step of generating the CT angiography image comprises:

5. The method of claim 4, further comprising generating a CT angiography image including at least one of a 3D subtraction CT arteriography image, a 3D subtraction CT venography image, and a 4D color CT angiography image from the dynamic contrast-enhanced CT image in which the blood flow period is divided.

6. The step of generating the 3D subtraction CT arteriography image includes:

6. The method of claim 5, further comprising extracting only the arterial phase signal by subtracting out the venous signal from the arterial phase signal.

7. The step of generating the 3D subtraction CT venography image comprises:

6. The method of claim 5, further comprising the step of extracting only the venous phase signal by subtracting out the arterial signal from the venous phase signal.

8. The step of generating a 4D color CT angiography image includes:

6. The method of claim 5, further comprising the step of generating a 4D color CT angiography image in which blood vessels are sequentially segmented and displayed in accordance with the order of contrast enhancement by assigning colors according to blood flow periods.

9. The step of generating a CT collateral blood flow image comprises:

10. The method of claim 1, further comprising averaging signal intensities of dynamic contrast-enhanced CT images included in each blood flow epoch to generate a CT collateral blood flow image.

10. An electronic device for simultaneously performing CT collateral blood flow images (CT collateral images) using blood flow time division of dynamic contrast-enhanced CT images, and 3D subtraction CT arteriography, 3D subtraction CT venography, and color-coded 4D CT angiography, Loading dynamic contrast-enhanced CT images containing dynamic blood flow information; The dynamic contrast-enhanced CT images are arranged in time sequence, and a time-intensity curve (TIC) is generated based on the change in signal intensity of the cerebral arteries and cerebral veins in each dynamic contrast-enhanced CT image; Dividing the dynamic contrast-enhanced CT images into blood flow periods according to the blood flow period information determined based on the TIC; Analyzing the divided blood flow periods to generate CT angiography images from dynamic contrast-enhanced CT images; An electronic device including a processor for dividing dynamic contrast-enhanced CT images corresponding to each blood flow period based on the divided blood flow periods and averaging CT signal intensities to generate CT collateral blood flow images for each period.

11. The processor:

11. The electronic device of claim 10, wherein the dynamic contrast-enhanced CT images are arranged along time to include dynamic blood flow information and are converted and displayed using a maximum intensity projection (MIP) technique to enhance blood vessels.

12. The processor: The electronic device of claim 10 , which generates a first TIC indicating a change in signal intensity of a cerebral artery over time and a second TIC indicating a change in signal intensity of a cerebral vein over time from dynamic contrast-enhanced CT images.

13. The processor: The electronic device of claim 12, wherein blood flow period information is acquired that classifies blood flow periods into an arterial period, a capillary period, an early venous period, a late venous period, and a delayed period based on the magnitude and range of the signal intensity of the cerebral artery and the cerebral vein, which change over time.

14. The processor: The electronic device of claim 13, wherein the CT angiography image includes at least one of a 3D subtraction CT arteriography image, a 3D subtraction CT venography image, and a 4D color CT angiography image from the dynamic contrast-enhanced CT image in which the blood flow period is divided based on the TIC.

15. The processor: The electronic device of claim 14, wherein the 4D color CT angiography image is generated by assigning a color to each blood flow period and displaying the blood vessels in a sequentially divided order according to the order of contrast enhancement.

16. In paragraph 10: The processor: The electronic device of claim 10 , wherein the CT collateral blood flow image is generated by averaging signal intensities of the dynamic contrast-enhanced CT images included in each blood flow period.

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