Method and medical imaging system for generating CT collateral blood flow images through post-processing of dynamic contrast-enhanced CT images and simultaneously realizing 3D subtractive CT angiography, 3D subtractive 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 particularly to the generation of CT collateral images, 3D subtraction CT arteriography, 3D subtraction CT venography, and a method and medical imaging system for simultaneously implementing color-coded 4D CT angiography.

Background Art

[0002] CT (Computed Tomography), one of the medical imaging devices widely used for the diagnosis and evaluation of cerebrovascular diseases, has a high hospital penetration rate and can be examined relatively easily and quickly. Therefore, it is most frequently used together with MRI (Magnetic Resonance Imaging).

[0003] When evaluating acute ischemic stroke patients through imaging examinations, the size of the initial cerebral infarction, detection of occluded blood vessels, evaluation of the ischemic penumbra, evaluation of collateral blood flow and perfusion are important factors.

[0004] However, in CT images so far, the size of the initial cerebral infarction has not been accurately known, and the existing CT collateral images obtained through repeatedly taken brain CT angiography have low temporal resolution and can only obtain images at a fixed time after the contrast agent is injected. Therefore, the difference in blood flow that varies depending on the cardiovascular state of each patient cannot be reflected. Thus, the evaluation of collateral blood flow through the conventional CT collateral images has been limited.

[0005] While technologies that generate collateral blood flow images from 4D MR Angiography (4D MRA) and Perfusion MRI are currently being considered, CT is preferred over MRI in cases of acute stroke patients requiring urgent treatment because the examination time is relatively shorter and accessibility is better.

[0006] In conclusion, for cerebrovascular diseases, particularly acute ischemic stroke, there is a need for CT technology that can evaluate the size of the initial stroke and ischemic penumbra, the blocked vessels, and collateral blood flow more rapidly and accurately than existing methods. Technology that generates more accurate, patient-specific collateral blood flow images and vascular images separating arteries and veins through a single dynamic contrast-enhanced CT scan obtained using CT perfusion imaging or similar methods would be one of the good solutions for diagnosing cerebrovascular diseases. [Overview of the project] [Problems that the invention aims to solve]

[0007] The 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 carrying out this method.

[0008] The object of the present invention is to provide a method and electronic apparatus for generating customized CT collateral blood flow images, 3D subtractive CT angiography, 3D subtractive CT venography, and 4D color CT angiography images based on the patient's cardiovascular condition and precise blood flow timing. [Means for solving the problem]

[0009] A method for generating CT collateral images and simultaneously performing 3D subtraction CT arteriography, 3D subtraction CT venography, and 4D color CT angiography using blood flow timing analysis of dynamic contrast-enhanced CT images performed by an electronic device according to one embodiment of the present invention, comprising the steps of: loading dynamic contrast-enhanced CT images containing dynamic blood flow information; arranging the dynamic contrast-enhanced CT images in time and creating a time-intensity curve (TIC) based on the change in signal intensity of cerebral arteries and cerebral veins in each dynamic contrast-enhanced CT image. The process includes: generating a curve; analyzing blood flow timing information determined based on the TIC and classifying the dynamic contrast-enhanced CT image by blood flow timing; generating CT angiography (3D subtractive CT angiography, 3D subtractive CT venography, and 4D color CT angiography) images from the dynamic contrast-enhanced CT images based on the classified blood flow timing; and dividing the dynamic contrast-enhanced CT images corresponding to each time period based on the classified blood flow timing and averaging the CT signal intensity to generate CT collateral blood flow images for each time period.

[0010] The step of loading the dynamic contrast-enhanced CT image may include reading the contrast-enhanced CT image, which contains dynamic blood flow information over time and anatomical information, and converting it into a four-dimensional array.

[0011] The step of loading the dynamic contrast-enhanced CT images may include a step of arranging the dynamic contrast-enhanced CT images chronologically so that they contain dynamic blood flow information, and then converting and displaying them using the Maximum Intensity Projection (MIP) technique.

[0012] The step of generating the TIC may include generating a first TIC showing changes in signal intensity of cerebral arteries and a second TIC showing changes in signal intensity of cerebral veins from dynamic contrast-enhanced CT images.

[0013] The step of classifying by blood flow timing may include a step of acquiring blood flow timing information that classifies the blood flow timing into arterial phase, capillary phase, venous phase (early venous phase and late venous phase), delayed phase, etc., based on the magnitude and range of the signal intensity of cerebral arteries and cerebral veins that change over time.

[0014] The step of generating the CT angiography image may include generating a CT angiography image from dynamic contrast-enhanced CT images with segmented blood flow timings, which includes at least one of the following: a 3D subtractive CT angiography image, a 3D subtractive CT venography image, or a 4D color CT angiography image.

[0015] The step of generating the three-dimensional subtractive CT angiography image may include a step of 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 three-dimensional subtractive CT venography image may include a step of 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 in which the hue is specified according to the blood flow timing and the image is sequentially divided and shown in accordance with the order in which the blood vessels are enhanced by contrast.

[0018] The aforementioned 4D color CT angiography may include a step of generating a dynamic color CT angiography image in which blood vessels are sequentially separated and shown in the order in which blood vessels are enhanced by contrast in dynamic contrast-enhanced CT, with the arterial phase designated as red, the capillary phase as green, and the venous phase as blue, and the intensity of the hue (color weighting) adjusted according to the degree of contrast enhancement of the blood vessels shown in each blood flow phase.

[0019] In an electronic device that generates CT collateral images using time segmentation of blood flow in dynamic contrast-enhanced CT images and simultaneously performs 3D subtraction CT arteriography, 3D subtraction CT venography, and 4D color CT angiography, the device loads dynamic contrast-enhanced CT images containing dynamic blood flow information, arranges the dynamic contrast-enhanced CT images chronologically, and generates a time-intensity curve (TIC) based on the change in signal intensity of cerebral arteries and veins in each dynamic contrast-enhanced CT image. The processor includes a function that generates a curve, divides dynamic contrast-enhanced CT images according to blood flow timing information determined based on TIC, generates CT angiography images from the dynamic contrast-enhanced CT images based on the divided blood flow timings, and after dividing the dynamic contrast-enhanced CT images corresponding to each time period based on the divided blood flow timings, averages the CT signal intensity to generate CT collateral blood flow images for each time period.

[0020] The aforementioned processor can arrange dynamic contrast-enhanced CT images over time to include dynamic blood flow information and convert them into a Maximum Intensity Projection (MIP) technique for display.

[0021] The processor can generate a first TIC indicating the change in the signal intensity of the cerebral artery and a second TIC indicating the change in the signal intensity of the cerebral vein from the dynamic contrast-enhanced CT image.

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

[0023] The processor can generate the CT angiography image including at least one or more of a three-dimensional subtracted CT arteriography image, a three-dimensional subtracted CT venography image, and a four-dimensional color CT angiography image from the dynamic contrast-enhanced CT image in which the blood flow phase is classified.

[0024] The processor can generate the four-dimensional color CT angiography image so that hues are specified for each blood flow phase and are sequentially classified and shown in the order in which the blood vessels are contrast-enhanced.

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

[0026] The processor can generate a color image using hues (for example, red - yellow - green - sky blue - blue, etc. from the part with more blood flow to the part with less blood flow) classified according to the contrast enhancement due to the degree of blood flow and the magnitude of the signal intensity in the CT collateral blood flow image of each blood flow phase.

Advantages of the Invention

[0027] According to an embodiment of the present invention, information on a single dynamic contrast-enhanced CT image that can be taken in a short time is obtained by converting it into an image necessary for diagnosing various cerebrovascular diseases through a relatively simple interface operation. Since it is possible to obtain a patient-ordered CT collateral blood flow image, three-dimensional subtracted CT angiography and three-dimensional subtracted CT venography in which arteries and veins are separated, and four-dimensional color CT angiography using color coding, which could not be accurately realized with existing CT technologies, the diagnostic accuracy and speed of various cerebrovascular diseases including acute stroke can be improved.

Brief Description of Drawings

[0028] [Figure 1] It is a schematic diagram illustrating the process of generating CT angiography images and collateral blood flow images according to an embodiment of the present invention. [Figure 2] It is a block diagram illustrating the configuration of an electronic device according to an embodiment of the present invention. [Figure 3] It is a drawing illustrating the operation flowchart of an electronic device according to an embodiment of the present invention. [Figure 4] It is a drawing illustrating the interface of a program embodying a method for generating CT angiography images and collateral blood flow images according to an embodiment of the present invention. [Figure 5] It is a drawing illustrating converted images arranged along time according to an embodiment of the present invention. [Figure 6] It is a drawing illustrating a time-signal intensity curve according to an embodiment of the present invention. [Figure 7] It is a drawing illustrating a posture for classifying blood flow phases according to an embodiment of the present invention. [Figure 8] It is a drawing illustrating a CT angiography image generated according to an embodiment of the present invention. [Figure 9] It is a drawing illustrating the interface of a program for adjusting the output form and hue of a CT angiography image generated according to an embodiment of the present invention. [Figure 10] It is a drawing illustrating a posture for dividing a CT collateral blood flow image according to an embodiment of the present invention into cross-sections. [Figure 11] This is a diagram illustrating a CT collateral blood flow image generated according to one embodiment of the present invention. [Figure 12] This is a diagram illustrating a CT collateral blood flow image generated according to one embodiment of the present invention. [Modes for carrying out the invention]

[0029] Preferred embodiments of the present invention will be described in detail below 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 only the possible embodiments of the present invention. In the process of clearly illustrating the present invention with the drawings, parts that are not relevant to the invention may be omitted, and the same reference numerals may be used throughout the specification for identical or similar components.

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

[0031] Referring to Figure 1, the image generation method according to one embodiment of the present invention involves inputting dynamic contrast-enhanced CT images 10 into an electronic device 100 and processing them to obtain CT angiography images 20 and collateral blood flow images 30.

[0032] An electronic device 100 according to one embodiment of the present invention is a device that generates CT angiography images 20 and collateral blood flow images 30 from dynamic contrast-enhanced CT images 10 by utilizing the blood flow time segmentation of the dynamic contrast-enhanced CT images 10, and can be embodied in a computer, server, etc. The electronic device 100 includes a program that embodies a method for generating CT angiography images 20 and collateral blood flow images 30.

[0033] The dynamic contrast-enhanced CT image 10 according to one embodiment of the invention refers to an image converted for program use from the original CT image (raw data) acquired while injecting contrast agent into the blood vessels of a target body (such as 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 in order to confirm the anatomical structure of the cerebral blood vessels and the circulatory process of blood flow.

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

[0035] As mentioned above, the current method of obtaining images to predict collateral blood flow using CT angiography is employed. However, because images are repeatedly obtained at predetermined intervals using CT equipment, it is not possible to reflect the differences in blood flow velocity due to the cardiovascular condition of each individual patient, making accurate evaluation of collateral blood flow difficult. The accessibility of CT equipment to cerebrovascular diseases is relatively superior compared to other imaging equipment, and as the performance of CT equipment continues to improve, the need for the development of cerebrovascular disease evaluation techniques utilizing this is increasing.

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

[0037] The configuration and operation of a diagnostic device for cerebrovascular diseases according to one embodiment of the present invention will be described in detail below with reference to the drawings.

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

[0039] An 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 user input to the electronic device 100. For example, user input may be user input to start the operation of the electronic device 100, user input to activate the interface of a program that embodies a method for generating CT angiography images and collateral blood flow images (hereinafter referred to as the interface), and any other user input necessary for generating CT angiography images and collateral blood flow images using the patient's CT images can be applied without limitation.

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

[0042] The communication unit 120 communicates with external devices such as servers to receive CT images, blood flow timing information based on signal intensity, and other data. For this purpose, the communication unit 120 can perform wireless communication such as 5G (5th generation communication), LTE-A (long term evolution-advanced), LTE (long term evolution), and Wi-Fi (wireless fidelity).

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

[0044] The display unit 130 displays display data generated by the operation of the electronic device 100. The display unit 130 can display a screen for loading converted video, a screen for displaying time-signal intensity curves, a screen for displaying generated CT angiography video and collateral blood flow video, a screen for receiving user input, and so on. In addition, any screen that can be displayed during the implementation of a program, such as an interface, can be applied without limitation.

[0045] The display unit 130 includes liquid crystal displays (LCDs), light-emitting diode (LED) displays, organic light-emitting diode (OLED) displays, micro-electro-mechanical systems (MEMS) displays, and electronic paper displays. The display unit 130 can be combined with the input unit 110 to be implemented as a touchscreen.

[0046] Memory 140 stores the operating program for the electronic device 100. Memory 140 includes non-volatile storage that can store data (information) regardless of whether power is supplied, and volatile memory that cannot store data unless power is supplied and data is loaded for processing by the processor 150. Storage includes flash memory, HDD (hard-disc drive), SSD (solid-state drive), ROM (Read Only Memory), etc., and memory includes buffer, RAM (Random Access Memory), etc.

[0047] Memory 140 can store CT images, time-signal intensity curves, and blood flow timing information based on blood vessel signal intensity received from external devices, as well as calculation programs necessary for processes such as CT image loading, time-signal intensity curve generation, blood flow timing classification, and generation of CT angiography images and collateral blood flow images.

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

[0049] The processor 150 loads dynamic contrast-enhanced CT images containing dynamic blood flow information, arranges the dynamic contrast-enhanced CT images in time, generates a time-intensity curve (TIC) based on the changes in signal intensity of cerebral arteries and cerebral veins in each dynamic contrast-enhanced CT image, analyzes the blood flow timing information determined based on the TIC to divide the dynamic contrast-enhanced CT images according to each blood flow timing, generates CT angiography images from the dynamic contrast-enhanced CT images based on the divided blood flow timings, divides the dynamic contrast-enhanced CT images corresponding to each time period based on the divided blood flow timings, averages the CT signal intensity to generate CT collateral blood flow images for each time period.

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

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

[0052] According to one embodiment of the present invention, the CT source image includes a high-temporal-resolution dynamic (multi-time) contrast-enhanced image obtained in the same or similar manner as CT perfusion imaging.

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

[0054] Processor 150 can arrange dynamic contrast-enhanced CT images over time to include dynamic blood flow information and convert them to display using the Maximum Intensity Projection (MIP) technique.

[0055] More specifically, the processor 150 can read CT images in DICOM (Digital Imaging and Communications in Medicine) format, and through variable settings, generate subtractive maximum intensity projection (MIP) vascular images by removing brain parenchyma and skull, and arrange them in chronological order. A subtractive MIP image (hereinafter also referred to as a converted image) according to one embodiment is illustrated in Figure 5.

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

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

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

[0059] At this time, in order to more accurately distinguish the timing of blood flow, the processor 150 can specify regions of interest (ROIs) for the cerebral arteries (M1 segment of the normal middle cerebral artery, A1 segment of the anterior cerebral artery, or terminal portion of the internal carotid artery) and cerebral veins (superior sagittal sinus, straight sinus, or distal portion of the large cortical veins), and generate a first TIC and a second TIC, respectively, based on the signal intensity from the specified regions of interest. A TIC according to one embodiment is illustrated in Figure 6.

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

[0061] According to one embodiment of the present invention, blood flow timing information obtained by analyzing the signal intensity of arteries and veins that changes with blood flow (hereinafter referred to as "blood flow timing information") means information that classifies the blood flow timing into arterial phase, capillary phase, venous phase, and delayed phase based on the numerical changes and ranges resulting from increases and decreases in the signal intensity of cerebral arteries and cerebral veins.

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

[0063] According to one embodiment of the present invention, the processor 150 can receive user input through the input unit 110 that specifies the blood flow timing based on the magnitude and range of signal intensity of cerebral arteries and cerebral veins, thereby acquiring blood flow timing information, or it can receive predefined blood flow timing information from an external source through the communication unit 120.

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

[0065] The processor 150 can generate CT angiography images by utilizing the maximum signal intensity of the images contained in each blood flow phase. In this case, the blood flow phase can include at least one of the following blood flow phases: arterial phase, capillary phase, early venous phase, late venous phase, and delayed phase, and can include multiple blood flow phases (multi-phases).

[0066] The processor 150 can generate CT angiography images from dynamic contrast-enhanced CT images with separated blood flow phases, including at least one of the following: 3D subtractive CT angiography, 3D subtractive CT venography, and 4D color CT angiography. Specifically for 3D subtractive CT angiography, it selectively subtracts venous signals from arterial signals to generate angiography images showing only arteries, and for 3D subtractive CT venography, it selectively subtracts arterial signals from venous signals to generate angiography images showing only veins.

[0067] The technical aspects of the subtraction process will be explained in detail during the process of generating each video.

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

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

[0070] Subsequently, the processor 150 can generate CT collateral blood flow images of sections parallel to a cross-section or a desired plane based on the set image reconstruction information. At this time, the image reconstruction information may include the thickness of the image sections, the distance between sections, the total number of images, etc. The processor 150 can receive user input through the input unit 110 to set the image reconstruction information, or generate collateral blood flow images based on pre-set image reconstruction information.

[0071] The processor 150 can generate collateral blood flow images by averaging the signal intensity of the images included in each blood flow period. At this time, the blood flow periods are as explained earlier in S41.

[0072] According to one embodiment of the present invention, by generating a variety of images simultaneously, the diagnostic and evaluation functions of cerebrovascular diseases using CT can be enhanced, thereby improving the usability of CT equipment and the convenience for patients.

[0073] Figure 4 is a diagram illustrating the interface of a program that embodies a method for generating CT vascular images and collateral blood flow images according to one embodiment of the present invention.

[0074] Through the interface 400 shown in Figure 4, a series of operations can be performed to generate CT angiography images and CT collateral blood flow images, as described in relation to Figure 3. The design and detailed configuration of the interface 400 are not limited to those shown in this drawing and can be adjusted as needed.

[0075] Interface 400 according to one embodiment of the present invention is an interface that utilizes dynamic contrast-enhanced CT images. This can be divided into a first interface 410 that generates CT angiography images and CT collateral blood flow images using CT original images of 1 mm or less, and a second interface 430 that generates only CT collateral blood flow images using CT original images reconstructed from slightly thicker sections (for example, images reconstructed from 1-5 mm sections). However, since the detailed configuration and operation of each interface are similar, the first interface 410 will be used as the basis for explanation.

[0076] According to one embodiment of the present invention, the interface 400 may receive user input to select detailed configurations, or the detailed configurations may be automatically or sequentially selected by program operation, and the operation method is not limited to the present invention.

[0077] First, relating to S10 in Figure 3, the processor 150 can input (load) the original CT images and generate converted images through icons such as Open Dicom Images 411 and Open Mat File 412.

[0078] Then, through an icon such as Display SUB MIP413, the processor 150 subtracts the brain region image from the brain region image after contrast agent injection from the brain region image after contrast agent injection, processes the image using the MIP technique, and outputs it in chronological order with appropriate brightness and contrast. A converted image according to one embodiment is illustrated in Figure 5.

[0079] Furthermore, when the brightness / contrast 414 icon is selected, a new window 440 is displayed, and the processor 150 can then perform a process to optimize the window width and window level after displaying the brightness / contrast of the image as a histogram.

[0080] Further details regarding other configurations and functions will be explained below with reference to Figures 6, 7, and 9.

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

[0082] Figure 5 is a diagram illustrating a converted video sequence arranged in time according to one embodiment of the present invention.

[0083] The video shown in Figure 5 is a converted video containing dynamic blood flow information, as explained in S10 of Figure 3, and is arranged in chronological order.

[0084] At this time, the processor 150 can assign numbers to each converted image, which is arranged in chronological order, for future classification of blood flow timing.

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

[0086] As described in relation to S20 in Figure 3, the processor 150 outputs a maximum intensity CT image with blood vessels emphasized using ROI (Artery) icon 415 and ROI (Vein) icon 416, etc. (see interface 400 in Figure 4). After setting the optimal Region of Interest (ROI) for the cerebral arteries and cerebral veins in the output image, it generates the TIC (Time-Intensity Curve) of the cerebral arteries and cerebral veins based on the changes in signal intensity extracted from the ROIs of the cerebral arteries and cerebral veins, respectively.

[0087] The processor 150 can generate a first TIC610 showing the change in signal intensity of cerebral arteries over time in dynamic contrast-enhanced CT images, and a second TIC620 showing the change in signal intensity of cerebral veins.

[0088] The first TIC610 and second TIC620 in Figure 6 were generated based on the converted images from number 1 (first image) to number 25 (last image) in Figure 5. At this time, the x-axis phase of the TIC was generated to correspond to the number of each image, and it is also possible to change it to the time axis. Therefore, it can be seen that the image with the highest signal intensity of the cerebral arteries in the first TIC610 is image number 8 in Figure 5, and the image with the highest signal intensity of the cerebral veins in the second TIC620 is image number 12 in Figure 5.

[0089] The following describes the process of classifying blood flow timing using the generated TIC, referring to Figure 7.

[0090] Figure 7 is a diagram illustrating how blood flow timing is divided according to one embodiment of the present invention. In this case, the interface 400 in Figure 7 is the same as the interface 400 in Figure 4.

[0091] As described in relation to S30 in Figure 3, the processor 150 uses TIC, which is caused by changes in the signal intensity of cerebral arteries and cerebral veins, to distinguish between blood flow timings for constructing CT angiography images and CT collateral blood flow images.

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

[0093] Additionally, processor 150 can select a Gaussian filter size of 419 to soften the CT collateral blood flow images before outputting the image.

[0094] The following describes CT angiography images.

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

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

[0097] Furthermore, the interfaces for generating each CT angiography image will be explained with reference to Figure 9.

[0098] The following details the process of generating each video.

[0099] The processor 150 divides and calculates the vascular image information from the point when the arterial signal begins to rise up to the arterial peak phase, the vascular information from the venous peak phase up to the point when the venous signal intensity decreases and becomes parallel, and the vascular image information between the arterial peak phase and the venous peak phase to generate venous subtraction CT angiography images 810, 820 and arterial subtraction CT venography images 830, 840.

[0100] Specifically, processor 150 subtracts the venous phase signal from the arterial phase signal to generate an arterial mask using values ​​greater than or equal to 0, and updates the arterial phase MIP (Maximum Intensity Projection) by multiplying the arterial mask by the arterial phase MIP to constitute 3D subtractive CT angiography 810 and 820. Furthermore, processor 150 subtracts the arterial phase signal from the venous phase signal to generate a venous mask using only values ​​greater than or equal to 0, and updates the venous phase MIP by multiplying the venous mask by the venous phase MIP to constitute 3D subtractive CT venography 830 and 840. At this time, in order to optimally subtract cerebral veins and arteries in 3D subtractive CT angiography and 3D subtractive CT venography, respectively, an appropriate subtraction reference point can be determined centered on the point in time when the signal intensity difference between cerebral arteries and cerebral veins is greatest (the point in time shown by the gray dotted line in Figure 6, for example, the 6th phase).

[0101] The processor 150 can distinguish between blood vessels in venous subtraction CT angiography, arterial subtraction CT venography, and 3D and 4D CT angiography using different colors depending on the timing of blood flow.

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

[0103] Figure 9 is a diagram illustrating the output format of CT angiography images generated by one embodiment of the present invention and the interface of a program for adjusting the hue.

[0104] At this time, interface 400 in Figure 9 is the same as interface 400 in Figures 4 and 7.

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

[0106] For this purpose, interface 400 includes parts 420 and 421 for adjusting the rotation of the image, and part 422 for adjusting the hue of the image.

[0107] The processor 150 can rotate CT angiography images by setting angles for the x, y, and z axes separately, and can generate rotating angiography images that show the cerebral blood vessels while continuously rotating by a specific angle (e.g., 12 degrees) based on a specific axis (e.g., the z axis).

[0108] Furthermore, the processor 150 can specify the hue for each blood flow stage to observe how blood flow moves at each stage. At this time, the processor 150 can specify the hue by setting weighted values ​​for R (Red), G (Green), and B (Blue), and can receive user input specifying the hue through the input unit 110.

[0109] In addition, the hue can be specified by selecting one of a predetermined set of hues (for example, red for the arterial phase and blue for the venous phase), and the method of specifying the hue is not limited to the present invention.

[0110] Figure 10 is a diagram illustrating the reconstruction of a CT collateral blood flow image in a cross-sectional view according to one 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 uses a portion of the original CT image to temporarily create and output a CT image of the central sagittal plane, and in order to obtain an image parallel to other images, it can first rotate the image so that it is as parallel as possible to the anterior commissure-posterior commissure line (AC-PC line), or to a plane defined by the user.

[0112] This includes a section 423 that converts this into a cross-sectional collateral blood flow image.

[0113] As described in relation to S42 in Figure 3, the 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 timing.

[0114] The processor 150 can reconstruct CT collateral blood flow images in cross-sectional views based on the configured image reconstruction information. This reconstruction information may include slice thickness, slice distance, and the total number of slices. However, reconstruction of CT collateral blood flow images is not limited to cross-sections and is possible in various cross-sectional views.

[0115] The processor 150 can generate CT collateral blood flow images by averaging the signal intensity of the images included in each blood flow period. The generated collateral blood flow images are shown in Figures 11 and 12.

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

[0117] Figure 11 shows a CT collateral blood flow image created using original CT images less than 1 mm thick, while Figure 12 shows a CT collateral blood flow image created using original CT images reconstructed from slightly thicker sections (5 mm).

[0118] Figures 11 and 12 illustrate transverse CT collateral blood flow images in the order of blood flow stage: 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 divided by signal intensity to indicate the degree of blood flow in the collateral blood flow images at each time point.

[0120] [National research and development projects that supported this invention] 1. [Project-specific number] 1711164322 [Issue Number] 2020R1F1A1071619 [Department Name] Department of Science, Technology and ICT [Project Management (Specialized) Institution Name] Korea Research Foundation [Research project name] Individual basic research (Ministry of Science, Technology, Information and Communication) [Research Project Title] Development of video techniques and video indicators for predicting histological prognosis, malignant course, and bleeding risk in patients with acute ischemic stroke. [Contribution Rate] 10 / 100 [Name of organization carrying out the project] Catholic University Industry-Academia Cooperation Group [Research Period] 2020.06.01~2023.02.28 2. [Issue-specific number] 1345371257 [Issue Number] 00248375 (RS-2023-00248375) [Department Name] Education Department [Project Management (Specialized) Institution Name] Korea Research Foundation [Research Project Name] Construction of Research Infrastructure for Science and Engineering (Support Project for Creative and Challenging Research Infrastructure) [Research Project Title] Development and Clinical Verification of Artificial Intelligence Technology for CT Collateral Blood Flow Image Generation and Analysis [Contribution Rate] 20 / 100 [Name of organization carrying out the project] Catholic University Industry-Academia Cooperation Group [Research Period] 2023.06.01~2026.05.31 3. [Project-Specific Number] 1711197626 [Issue Number] 00252980 (RS-2023-00252980) [Department Name] Department of Science, Technology and ICT [Project Management (Specialized) Institution Name] Korea Research Foundation [Research project name] Individual basic research (Ministry of Science, Technology, Information and Communication) [Research Project Title] Research for the Realization of Precision Medicine for Acute Ischemic Stroke and the Development of Patient-Personalized Assessment Technologies [Contribution Rate] 20 / 100 [Name of organization carrying out the project] Catholic University Industry-Academia Cooperation Group [Research Period] 2023.06.01~2026.02.28 4. [Issue-specific number] 1465040847 [Issue Number] 00266130 (RS-2023-00266130) [Department Name] Health and Welfare Department [Name of the specialized organization for issue management] Korea Health Industry Development Agency [Research Project Name] Development of Problem-Solving Technologies for Clinical Settings of Neurological Diseases (R&D) [Research Project Title] Development of software for generating and analyzing collateral blood flow images and construction of a commercialization platform [Contribution Rate] 50 / 100 [Name of organization carrying out the project] Catholic University Industry-Academia Cooperation Group [Research Period] 2023.07.01~2026.12.31

Claims

1. In a method for generating CT collateral flow images using blood flow time segmentation of dynamic contrast-enhanced CT images performed by an electronic device, and for simultaneously realizing 3D Subtraction CT Arteriography, 3D Subtraction CT Venography, and 4D Color-coded CT Angiography, The stage of loading dynamic contrast-enhanced CT images containing dynamic blood flow information; The steps include generating subtractive maximum intensity projection (MIP) vascular images by removing the brain parenchyma and skull from the dynamic contrast-enhanced CT images, arranging the dynamic contrast-enhanced CT images chronologically, and generating a time-intensity curve (TIC) based on the changes in signal intensity of cerebral arteries and cerebral veins in each dynamic contrast-enhanced CT image; A step in which dynamic contrast-enhanced CT images are divided according to each blood flow period, corresponding to the blood flow timing information determined based on the aforementioned TIC; The step of generating 3D subtractive CT angiography images, 3D subtractive CT venography images, and 4D color CT angiography images from dynamic contrast-enhanced CT images based on segmented blood flow timings; The process includes the step of dividing the dynamic contrast-enhanced CT images corresponding to each period based on the segmented blood flow periods, averaging the CT signal intensity, and generating CT collateral blood flow images for each period; A method characterized in that, in the step of generating the three-dimensional subtractive CT angiography image and the three-dimensional subtractive CT venography image, the point in time when the signal intensity difference between the cerebral arteries and cerebral veins is greatest is selected as the reference point for subtraction.

2. The step of generating the aforementioned TIC is: The method according to claim 1, further comprising the step of generating a first TIC showing the change in signal intensity of cerebral arteries over time and a second TIC showing the change in signal intensity of cerebral veins over time from the dynamic contrast-enhanced CT image.

3. The step of classifying the blood flow timing based on the aforementioned TIC is: The method according to claim 2, further comprising the step of acquiring blood flow timing information that classifies the blood flow timing into arterial phase, capillary phase, early venous phase, late venous phase, and delayed phase based on the magnitude and range of the signal intensity of cerebral arteries and cerebral veins that change over time.

4. The step of generating the three-dimensional subtractive CT angiography image is as follows: The method according to claim 1, further comprising the step of extracting only the arterial phase signal by subtracting and removing the venous signal from the arterial phase signal.

5. The step of generating the three-dimensional subtractive CT venography image is as follows: The method according to claim 1, further comprising the step of extracting only the venous phase signal by subtracting and removing the arterial signal from the venous phase signal.

6. The step of generating the aforementioned 4D color CT angiography image is as follows: The method according to claim 1, further comprising the step of generating a four-dimensional color CT angiography image such that the hue is specified according to the blood flow period and the images are sequentially divided and shown in accordance with the order in which the blood vessels are enhanced by contrast.

7. In an electronic device that generates CT collateral flow images using dynamic contrast-enhanced CT images with blood flow time segmentation, and simultaneously performs 3D subtraction CT arteriography, 3D subtraction CT venography, and 4D color CT angiography, Loading dynamic contrast-enhanced CT images containing dynamic blood flow information, A subtractive maximum intensity projection (MIP) vascular image is generated by removing the brain parenchyma and skull from the dynamic contrast-enhanced CT image, the dynamic contrast-enhanced CT images are arranged in time, and a time-intensity curve (TIC) is generated based on the changes in signal intensity of cerebral arteries and cerebral veins in each dynamic contrast-enhanced CT image. Based on the TIC, dynamic contrast-enhanced CT images are categorized according to each blood flow period, corresponding to the blood flow timing information. By analyzing segmented blood flow timings, three-dimensional subtractive CT angiography images, three-dimensional subtractive CT venography images, and four-dimensional color CT angiography images are generated from dynamic contrast-enhanced CT images. It includes a processor that divides the dynamic contrast-enhanced CT images corresponding to each period based on the segmented blood flow periods, averages the CT signal intensity, and generates CT collateral blood flow images for each period. The processor is an electronic device characterized in that, when generating the three-dimensional subtractive CT angiography image and the three-dimensional subtractive CT venography image, it selects the point in time when the signal intensity difference between the cerebral arteries and cerebral veins is greatest as the reference point for subtraction.

8. The aforementioned processor, The electronic device according to claim 7, which generates a first TIC showing the change in signal intensity of cerebral arteries over time and a second TIC showing the change in signal intensity of cerebral veins from dynamic contrast-enhanced CT images.

9. The aforementioned processor, The electronic device according to claim 8, which acquires blood flow timing information that classifies the blood flow timing into arterial phase, capillary phase, early venous phase, late venous phase, and delayed phase based on the magnitude and range of the signal intensity of cerebral arteries and cerebral veins that change over time.

10. The aforementioned processor, The electronic device according to claim 7, which generates a four-dimensional color CT angiography image in which the hue is specified according to the blood flow period and is sequentially divided and shown in the order in which the blood vessels are enhanced by contrast.

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