Method, device, system and computer storage medium for obtaining brain CT perfusion parameter map

Abstract

The present application relates to a method, device, system and computer storage medium for obtaining a brain CT perfusion parameter map, the method comprising obtaining an original CT perfusion image, correcting the CT perfusion image, and post-processing the CT perfusion image to obtain a brain CT perfusion parameter map, the correction process comprising: screening the original CT perfusion image to obtain the best candidate layer sequence; performing centroid calculation on the best candidate layer sequence to obtain the centroid coordinates, and determining the central axis of the best candidate layer sequence at each moment through the centroid coordinates; calculating a correction angle based on the best candidate layer sequence; performing image correction on the best candidate layer sequence according to the correction angle, and correcting the CT perfusion image based on the central axis of the best candidate layer at each moment. The present application improves the accuracy of the correction process results, avoids the influence on the deviation of the post-processing calculation results, and improves the efficiency of obtaining the brain CT perfusion parameter map by optimizing the correction process of the CT perfusion image.

Description

Technical Field

[0001] The present application relates to the cross-technical field of image processing and medical engineering, and in particular to a method, device, system and computer storage medium for acquiring a brain CT perfusion parameter map. Background Art

[0002] Brain diseases are the main diseases that threaten human life and health today, especially stroke and brain tumors. Taking stroke as an example, about 5.7 million people lose their lives due to stroke every year worldwide. Stroke has become the second leading cause of death after ischemic heart disease, and its disability rate is also quite high. In my country, according to the latest statistics, the number of people over 40 years old suffering from stroke is about 12.42 million, and about 1.96 million people die from stroke every year. The direct and indirect medical expenses and losses caused by brain diseases have also brought heavy economic burdens to countries around the world. Therefore, how to prevent, treat and rehabilitate brain diseases has become a difficult problem facing the world.

[0003] There is an optimal treatment time for stroke. The prognosis of patients treated within this time period can be significantly improved. However, there are limited medical centers in my country that are capable of conducting round-the-clock brain neurological examinations and treatments, and patients die due to lack of timely diagnosis and treatment. Therefore, how to quickly and accurately locate the lesions and obtain valuable pathological conditions has become the key to treating brain diseases. With the rapid development of medical imaging technology, imaging methods such as CT, MRI, and DSA are applied to the diagnosis of brain diseases. After multi-modal imaging (CT plain scan, enhanced CT, CTA, PWI, DWI, MRA) examinations, exchanging tissue windows for time windows and as much rescue time as possible are of great clinical significance for the treatment of brain diseases. At present, the main methods for tissue blood flow detection in clinical practice are SPECT, PET, etc. Since these devices are not only expensive, but also require some auxiliary equipment, their popularization and promotion are greatly limited. CT imaging equipment has the advantages of low cost and simple operation, and is relatively popular in hospitals at all levels in my country.

[0004] Based on this, it is of great practical significance to study how to use CT imaging methods to quickly and accurately diagnose brain diseases. However, clinical medical staff often make their own judgments based on their own experience in CT images. On the one hand, the medical staff's experience is required to be high. On the other hand, because the judgment method is based on the image morphological information, it lacks objective data support, which can easily lead to misdiagnosis and missed diagnosis. CT cerebral perfusion imaging is a method of brain functional imaging. The basic theoretical basis of this method is the tracer dilution theory, that is, the contrast agent and the HU value of the image have a certain linear relationship, which can well solve the problem of quantitative detection of brain ischemic areas. Based on deconvolution, Bayesian, Fourier and other algorithms, parameters such as cerebral blood flow (CBF, Cerebral Blood Flow), blood volume (CBV, Cerebral Blood Volume), mean transit time (MTT, Mean Transit Time), and peak time (TTP, Time To Peak) can be calculated. Doctors can analyze the above parameter diagrams and then judge the patient's brain ischemic and infarct areas, thereby providing guidance for the next step of thrombolytic surgery.

[0005] Generally speaking, the generation of cerebral perfusion parameter maps can be divided into the following processes: original CT perfusion images, motion correction, CT perfusion image filtering, brain tissue extraction, automatic search for arteriovenous points, residual function solution, ischemic and infarct core calculation, parameter map smoothing, etc. During the CT scanning process, since all CT perfusion images may not be completely aligned, the judgment result of the infarct core is affected. In addition, in the process of obtaining the original CT perfusion images, the unconscious shaking of the patient's head also causes the orientation of the CT perfusion images at different times to shift, resulting in errors in the subsequent solution of the residual function.

[0006] The correction results of the correction algorithm used in the prior art are inaccurate, which can easily cause the time-concentration attenuation curves of arterial and venous tissues to be misaligned, thereby leading to a series of subsequent calculation errors. In addition, the correction algorithm used in the prior art is not very generalizable, has limited applicability, is time-consuming, and is only applicable to the correction of certain specific images and requires a specific template, which greatly reduces efficiency. Summary of the invention

[0007] In order to solve the problems in the prior art of obtaining brain CT perfusion parameter maps, such as inaccurate results of CT image correction, low generalization, limited applicability, long time consumption and low efficiency, as well as problems affecting the accuracy of post-correction processing (such as misalignment of time-concentration attenuation curves of arterial and venous tissues, deviation or error in judgment of the core area of ​​infarction, and error in solving the residual function), the present application provides a method for obtaining brain CT perfusion parameter maps.

[0008] The method for obtaining a brain CT perfusion parameter map in the present application includes obtaining an original CT perfusion image, correcting the CT perfusion image, and post-processing the corrected CT perfusion image to obtain a brain CT perfusion parameter map, wherein the correction process specifically includes:

[0009] Screen the original CT perfusion images to obtain the best candidate layer sequence;

[0010] Calculating the centroid of the best candidate layer sequence to obtain the centroid coordinates of the best candidate layer sequence, and determining the central axis of the best candidate layer sequence at each moment through the centroid coordinates;

[0011] The correction angle is calculated based on the best candidate layer sequence;

[0012] The best candidate layer sequence is image corrected according to the correction angle, and the CT perfusion image is corrected based on the central axis of the best candidate layer at each time.

[0013] Optionally, the post-processing includes sequentially performing brain tissue extraction, residual function solution and infarction core area determination on the corrected CT perfusion image.

[0014] Optionally, the screening of the original CT perfusion images to obtain the best candidate layer sequence specifically includes:

[0015] Extracting the intracranial region of the CT perfusion image, wherein the intracranial region includes a skull region;

[0016] Extracting the first n connected domains of the intracranial region, where n is a natural number;

[0017] Performing repair processing and / or elimination processing on the CT perfusion image to obtain a selected sample of the CT perfusion image;

[0018] In the selected sample, the CT perfusion image of the intracranial layer with the largest area is used as the best candidate layer sequence.

[0019] Optionally, the repair process includes:

[0020] Constructing a binary image with all pixel values ​​being one, wherein the binary image has the same resolution as the CT perfusion image;

[0021] Subtracting the binary image from the intracranial region;

[0022] Extracting the first n connected domains of the intracranial region from the binary image after difference calculation to obtain a binary image based on the connected domains;

[0023] Subtract the binary image whose pixel values ​​are all ones from the binary image based on the connected domain.

[0024] Optionally, the elimination process includes:

[0025] In CT perfusion images, the number of connected domains between the intracranial region and the extracranial region was calculated;

[0026] CT perfusion images with the number of connected domains greater than two or less than one were eliminated.

[0027] Optionally, performing centroid calculation on the best candidate layer sequence specifically includes:

[0028] Filling all pixel values ​​of the intracranial region of the best candidate layer sequence with one;

[0029] The centroid of the best candidate layer sequence after filling is calculated to obtain the centroid coordinates of the best candidate layer sequence.

[0030] Optionally, the correction angle is calculated based on the best candidate layer sequence, specifically including:

[0031] Calculate the distance between the left and right edges and the centroid of the best candidate layer sequence at each moment at each different angle according to a fixed angle step and calculate the difference;

[0032] Taking the absolute values ​​of the differences and summing them up, and obtaining the minimum cumulative sum of the differences by comparing the sizes;

[0033] A correction angle is determined according to the minimum accumulated sum.

[0034] The present application also provides a device for obtaining a brain CT perfusion parameter map, the device comprising:

[0035] An acquisition module, used to obtain original CT perfusion images;

[0036] A screening module is used to screen the original CT perfusion images to obtain the best candidate layer sequence;

[0037] A centroid calculation module, used to calculate the centroid of the best candidate layer sequence to obtain the centroid coordinates of the best candidate layer sequence, and determine the central axis of the best candidate layer sequence at each moment through the centroid coordinates;

[0038] A correction angle calculation module calculates the correction angle according to the best candidate layer sequence;

[0039] An image correction module performs image correction on the best candidate layer sequence according to the correction angle, and corrects the CT perfusion image based on the central axis of the best candidate layer at each time;

[0040] The post-processing module performs post-processing on the corrected CT perfusion image to obtain a brain CT perfusion parameter map.

[0041] The present application also provides a system for obtaining a brain CT perfusion parameter map, comprising a terminal and a server, wherein the server comprises a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, and the server obtains an original CT perfusion image from the terminal;

[0042] When the computer processor executes the computer program, the method for obtaining a brain CT perfusion parameter map described in the present application is implemented.

[0043] The present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a computer processor, the method for obtaining a brain CT perfusion parameter map described in the present application is implemented.

[0044] The method for obtaining brain CT perfusion parameter map in this application has at least the following effects:

[0045] This application improves the accuracy of the correction processing results, avoids the influence of the deviation of the post-processing calculation results, and improves the efficiency of obtaining brain CT perfusion parameter maps by optimizing the correction processing of CT perfusion images, specifically including:

[0046] By screening CT perfusion images, the best candidate layer of CT perfusion images is obtained, thereby ensuring the reliability of the sample; by centroid calculation, the centroid coordinates and central axis of the best candidate layer are obtained, and the CT perfusion images are corrected by correction angles, thereby improving the efficiency of obtaining brain CT perfusion parameter maps; ultimately, the impact on the deviation of post-processing calculation results is avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] FIG1 is an original CT perfusion image obtained in an embodiment of the present application;

[0048] FIG2 is an original CT perfusion image obtained in an embodiment of the present application;

[0049] Figure 3 This is a schematic diagram of a method for obtaining a brain CT perfusion parameter map in an embodiment of the present application;

[0050] Figure 4 This is a schematic diagram of a process for CT perfusion image correction processing in one embodiment of the present application;

[0051] FIG5 is a schematic diagram illustrating the CT perfusion image screening process in one embodiment of the present application;

[0052] FIG6 is a schematic diagram illustrating the CT perfusion image screening process in one embodiment of the present application;

[0053] Figure 7 for Figure 4Schematic diagram illustrating the calculation method in step S23 shown;

[0054] Figure 8 Figure 3 The original CT perfusion images obtained in the process shown;

[0055] Figure 9 Figure 4 The corrected CT perfusion images were obtained in the process shown.

[0056] Fig.10 1 is a diagram of the internal structure of a computer device in one embodiment of the present application. DETAILED DESCRIPTION

[0057] In the prior art method of obtaining brain CT perfusion parameter maps, during the CT scanning process, there is a phenomenon that all CT perfusion images are not completely aligned, such as the original CT perfusion images of the 16th to 19th layers at the 5th moment shown in Figure 1. The main basis for judging the infarct core is rCBF (blood flow is less than 30% of the contralateral side, which is determined to be the infarct core area). Therefore, if the image is not completely aligned, a large error will be introduced when calculating the infarct core. In addition, due to the unconscious shaking of the patient's head, the orientation of the CT perfusion image in a certain period of time is offset from the image in other time periods, such as the original CT perfusion image of the 15th layer at the 3rd to 6th moment shown in Figure 2, which causes errors in the solution of the subsequent residual function. Therefore, it is of great significance to accurately calculate various parameters of the patient's brain tissue by completely aligning all CT perfusion images through a certain method.

[0058] However, the correction results of the correction algorithms in the prior art are inaccurate, which can easily cause the time-concentration attenuation curves of arterial and venous tissues to be misaligned, leading to a series of subsequent calculation errors. In addition, the correction algorithms in the prior art are not very generalizable and have limited applicability. They are only applicable to the correction of certain specific images and require specific templates, which greatly reduces efficiency. In addition, since brain perfusion images have high resolution, many layers, and dozens of time items, the correction algorithms in the prior art take tens of minutes to complete the correction of all sequences, which greatly reduces the chances of stroke patients being cured.

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] To solve the above technical problems, see Figure 3 In one embodiment of the present application, a method for generating a brain CT perfusion parameter map is provided, comprising:

[0061] Step S10: obtaining an original CT perfusion image. It can be understood that the original CT perfusion image is obtained by the medical imaging technology in the prior art.

[0062] Step S20, CT perfusion image correction processing. Figure 4 The correction process specifically includes steps S21 to S24.

[0063] Step S21, screening the original CT perfusion images to obtain the best candidate layer sequence.

[0064] It can be understood that the main purpose of CT perfusion image screening is to select the best candidate layer sequence from all CT perfusion images, obtain the global central axis and rotation angle based on the candidate layer sequence, and on this basis completely align all CT perfusion images. Screening the original CT perfusion images mainly consists of the following steps:

[0065] The intracranial region of the CT perfusion image is extracted, and the intracranial region includes the skull region. In this embodiment, the method for extracting the intracranial region of the CT perfusion image can be selected as a threshold method or other extraction methods in the prior art, and the intracranial region is shown in FIG5(a).

[0066] The first n connected domains of the intracranial region are extracted, where n is a natural number. In this embodiment, the algorithm for extracting the first n connected domains of the intracranial region is preferably a maximum connected domain algorithm, as shown in FIG5(b).

[0067] To ensure the reliability of the selected samples, the CT perfusion images are repaired and / or eliminated to obtain the selected samples of the CT perfusion images; the CT perfusion images of the layer with the largest intracranial area are selected as the best candidate layer sequence in the selected samples. It can be understood that selecting the layer with the largest intracranial area as the final candidate sequence after the CT perfusion images have been repaired and / or eliminated is helpful to improve the accuracy of subsequent processing.

[0068] In order to reduce the error of centroid calculation in subsequent steps and ensure that the candidate sequence has good symmetry, in one embodiment, the small holes in the skull are repaired.

[0069] The repair process includes: constructing a binary image with all pixel values ​​being one, the binary image having the same resolution as the CT perfusion image; subtracting the binary image from the intracranial region; extracting the first n connected domains of the intracranial region from the binary image after subtraction to obtain a binary image based on the connected domain; subtracting the binary image with all pixel values ​​being one from the binary image based on the connected domain.

[0070] Specifically, first, a binary image with the same resolution as Figure 5(a) and all pixel values ​​of 1 is constructed and subtracted from Figure 5(a) to obtain a transition image as shown in Figure 5(c). Then, the maximum connected domain algorithm is used to extract the first n connected domains of Figure 5(c) to obtain a transition image as shown in Figure 5(d). Finally, the binary image of all 1 is used to subtract Figure 5(d) to obtain the image result of the repair processing as shown in Figure 5(e).

[0071] The elimination process includes: calculating the number of connected domains between the intracranial region and the extracranial region in the CT perfusion image; and eliminating the CT perfusion images with the number of connected domains being greater than two or less than one.

[0072] Specifically, since some sequences in CT perfusion images contain tissues such as eyes, nose, and mouth, these sequences are not the most common sites of ischemia, and on the other hand, these sequences have poor symmetry and cannot be used as candidate sequences and need to be eliminated. The method used in this embodiment is to calculate the number of connected domains inside and outside the skull. If the number of connected domains is greater than 2 or less than 1, all are eliminated, and only the sequence with a connected domain number of 2 is retained, such as Figure 6(a) to Figure 6(f) shown.

[0073] Step S22, calculate the centroid of the best candidate layer sequence to determine the central axis of the best candidate layer sequence at each moment. Specifically, it includes: filling all the pixel values ​​of the intracranial region of the best candidate layer sequence with one; calculating the centroid of the filled best candidate layer sequence to obtain the centroid coordinates of the best candidate layer sequence. The central axis of the best candidate layer sequence at each moment is determined by the centroid coordinates.

[0074] Specifically, the centroid is calculated according to formula (1) and formula (2). In order to prevent the presence of some small local skull bones in the candidate layer, which may cause inaccurate centroid calculation, the present application fills the intracranial area with 1.

[0075]

[0076]

[0077] Where: i, j represent the row and column of the image respectively;

[0078] T represents the voxel value of a row;

[0079] p=0,1;q=0,1;

[0080] x0 and y0 represent the abscissa and ordinate of the center of mass, respectively;

[0081] m pq , is the p+q order moment of the binary image;

[0082] m10 , is the first-order moment m of the binary image 10 ;

[0083] m 00 , is the cumulative sum of all pixel values ​​in the binary image;

[0084] m 01 , is the first-order moment m of the binary image 01 .

[0085] Step S23, calculates the correction angle based on the best candidate layer sequence, specifically including: calculating the distance between the left and right edges and the centroid of the best candidate layer sequence at each time at each different angle according to the fixed angle step and finding the difference, taking the absolute value of the difference and accumulating it, obtaining the minimum cumulative sum by comparing the size; determining the correction angle based on the minimum cumulative sum. It can be understood that the correction angle is obtained based on the central axis of the best candidate layer sequence, and the correction processing result is obtained by correcting the original CT perfusion image through the correction angle.

[0086] Specifically, after determining the rotation angle and finding the centroid of the candidate sequence, the candidate sequence is rotated in a certain angle range (e.g., from -60 degrees to 60 degrees) in a fixed step length (e.g., a step length of 1 degree); according to formula (3), the following is calculated: Figure 7 The absolute value of the distance difference between the left edge and the right edge and the centroid of each CT perfusion image at each angle is accumulated, and the accumulated sum at different angles is calculated based on the exhaustive search algorithm, and the rotation angle corresponding to the minimum accumulated sum at a certain rotation angle is selected as the rotation angle of all layers at that moment. It can be understood that in order to adapt to the angle deviation of the original CT perfusion image and adjust the overall detection efficiency, the angle area and step size can be adjusted respectively.

[0087]

[0088] Among them, S θ represents the cumulative sum at a rotation angle of θ;

[0089] m, l, and r represent all rows, the left edge, and the right edge, respectively.

[0090] Step S24: image correction is performed on the best candidate layer sequence according to the correction angle, and the CT perfusion image is corrected based on the central axis of the best candidate layer at each time.

[0091] Specifically, based on the centroid coordinates of the candidate layer obtained in step S22 (the centroid coordinates of the best candidate layer sequence) and the best rotation angle (correction angle) obtained in step S23, all CT perfusion images are rotated at the centroid coordinates to obtain corrected CT perfusion images.

[0092] Step S30, post-processing the corrected CT perfusion image to obtain a brain CT perfusion parameter map. The post-processing includes sequentially performing brain tissue extraction, residual function solution and infarction core area determination on the corrected CT perfusion image.

[0093] Specifically, post-processing includes CT perfusion image filtering, brain tissue extraction, automatic search for arteriovenous points, residual function solution, ischemic and infarct core calculation, CT perfusion parameter map smoothing and other processes.

[0094] In summary, this application optimizes the method of obtaining brain CT perfusion parameter maps through a fast automatic correction algorithm (based on the central axis of the brain). Its implementation process mainly includes four parts: CT perfusion image screening, centroid solution, exhaustive search algorithm, and image correction. Specifically, it includes: first, all spatial CT perfusion images are processed to screen out a layer of perfusion images with the best symmetry, and then the central axis of the layer of images at all times is found by calculating the centroid, and the rotation angle of the layer of images at all times is calculated based on the exhaustive symmetry search algorithm when the layer is completely straightened, and the central axis and rotation angle of the layer of images at all times are used as the central axis and rotation angle of different layers of images at the same time, and all CT perfusion images are completely straightened based on this central axis and rotation angle.

[0095] The present application proposes a method for obtaining a brain CT perfusion parameter map, which includes an automatic correction algorithm for CT perfusion images. Compared with existing correction algorithms, the correction algorithm proposed in the present application does not require any template, which greatly simplifies the correction process; the correction accuracy is also improved compared with existing algorithms; the correction algorithm proposed in the present application can complete all image corrections within a few seconds, which is significantly more time-consuming than existing algorithms, saving valuable time for timely surgery for stroke patients.

[0096] At present, the main basis for evaluating the infarct core using CT perfusion images is rCBF. It is generally believed that when the ischemic area is 30% lower than the blood flow velocity of the contralateral side, it is considered to be the infarct core. Therefore, the calculation of the infarct core requires the CT perfusion image to be completely aligned on the one hand, and the brain centerline to be found on the other hand. This application completes the image correction on the basis of finding the brain centerline, which is of great significance for the subsequent calculation of CT perfusion parameters and then the evaluation of cerebral ischemia and infarction areas.

[0097] It should be understood that although Figure 3-4 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 3-4At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0098] In another embodiment, the present application also provides a device for acquiring a brain CT perfusion parameter map, comprising: an acquisition module, for acquiring an original CT perfusion image;

[0099] A screening module is used to screen the original CT perfusion images to obtain the best candidate layer sequence;

[0100] A centroid calculation module, used to calculate the centroid of the best candidate layer sequence to obtain the centroid coordinates of the best candidate layer sequence, and determine the central axis of the best candidate layer sequence at each moment through the centroid coordinates;

[0101] A correction angle calculation module calculates the correction angle according to the best candidate layer sequence;

[0102] An image correction module performs image correction on the best candidate layer sequence according to the correction angle, and corrects the CT perfusion image based on the central axis of the best candidate layer at each time;

[0103] The post-processing module performs post-processing on the corrected CT perfusion image to obtain a brain CT perfusion parameter map.

[0104] For the specific definition of the device for obtaining the brain CT perfusion parameter map, please refer to the definition of the method for obtaining the brain CT perfusion parameter map above, which will not be repeated here. Each module in the above-mentioned device for obtaining the brain CT perfusion parameter map can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0105] In another embodiment, a computer device is also provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.10As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for obtaining a brain CT perfusion parameter map is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0106] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0107] In one embodiment, a system for obtaining a brain CT perfusion parameter map is provided, comprising a terminal and a server, wherein the server comprises a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, and the server obtains an original CT perfusion image from the terminal; when the computer processor executes the computer program, the following steps are implemented:

[0108] Step S10, obtaining the original CT perfusion image;

[0109] Step S21, screening the original CT perfusion images to obtain the best candidate layer sequence;

[0110] Step S22, calculating the centroid of the best candidate layer sequence to obtain the centroid coordinates of the best candidate layer sequence, and determining the central axis of the best candidate layer sequence at each moment through the centroid coordinates;

[0111] Step S23, calculating a correction angle according to the best candidate layer sequence;

[0112] Step S24, performing image correction on the best candidate layer sequence according to the correction angle, and correcting the CT perfusion image based on the central axis of the best candidate layer at each time;

[0113] Step S30, post-processing the corrected CT perfusion image to obtain a brain CT perfusion parameter map.

[0114] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0115] Step S10, obtaining the original CT perfusion image;

[0116] Step S21, screening the original CT perfusion images to obtain the best candidate layer sequence;

[0117] Step S22, calculating the centroid of the best candidate layer sequence to obtain the centroid coordinates of the best candidate layer sequence, and determining the central axis of the best candidate layer sequence at each moment through the centroid coordinates;

[0118] Step S23, calculating a correction angle according to the best candidate layer sequence;

[0119] Step S24, performing image correction on the best candidate layer sequence according to the correction angle, and correcting the CT perfusion image based on the central axis of the best candidate layer at each time;

[0120] Step S30, post-processing the corrected CT perfusion image to obtain a brain CT perfusion parameter map.

[0121] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0122] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. When the technical features in different embodiments are embodied in the same figure, it can be regarded that the figure also discloses the combination examples of the various embodiments involved.

[0123] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for obtaining a brain CT perfusion parameter map, comprising obtaining an original CT perfusion image, correcting the CT perfusion image, and post-processing the corrected CT perfusion image to obtain a brain CT perfusion parameter map, characterized in that: The correction process specifically includes: Screen the original CT perfusion images to obtain the best candidate layer sequence; Calculating the centroid of the best candidate layer sequence to obtain the centroid coordinates of the best candidate layer sequence, and determining the central axis of the best candidate layer sequence at each moment through the centroid coordinates; Calculating the correction angle according to the best candidate layer sequence includes: calculating the distances between the left and right edges and the centroid of the best candidate layer sequence at different angles at each time according to a fixed angle step and calculating the difference, taking the absolute values ​​of the calculated differences and accumulating them, obtaining the minimum cumulative sum of the differences by comparing the sizes, and determining the correction angle according to the minimum cumulative sum; The best candidate layer sequence is image corrected according to the correction angle, and the CT perfusion image is corrected based on the central axis of the best candidate layer at each time.

2. The method for obtaining a brain CT perfusion parameter map according to claim 1, characterized in that: The post-processing includes sequentially extracting brain tissue, solving residual functions and determining the infarct core area on the rectified CT perfusion images.

3. The method for obtaining a brain CT perfusion parameter map according to claim 1, characterized in that: The screening of the original CT perfusion images to obtain the best candidate layer sequence specifically includes: Extracting the intracranial region of the CT perfusion image, wherein the intracranial region includes a skull region; Extracting the first n connected domains of the intracranial region, where n is a natural number; Performing repair processing and / or elimination processing on the CT perfusion image to obtain a selected sample of the CT perfusion image; In the selected sample, the CT perfusion image of the intracranial layer with the largest area is used as the best candidate layer sequence.

4. The method for obtaining a brain CT perfusion parameter map according to claim 3, characterized in that: The repair process includes: Constructing a binary image with all pixel values ​​being one, wherein the binary image has the same resolution as the CT perfusion image; Subtracting the binary image from the intracranial region; Extracting the first n connected domains of the intracranial region from the binary image after difference calculation to obtain a binary image based on the connected domains; Subtract the binary image whose pixel values ​​are all ones from the binary image based on the connected domain.

5. The method for obtaining a brain CT perfusion parameter map according to claim 3, characterized in that: The elimination process includes: In CT perfusion images, the number of connected domains between the intracranial region and the extracranial region was calculated; CT perfusion images with the number of connected domains greater than two or less than one were eliminated.

6. The method for obtaining a brain CT perfusion parameter map according to claim 1, characterized in that: The centroid of the best candidate layer sequence is calculated, specifically including: Filling all pixel values ​​of the intracranial region of the best candidate layer sequence with one; The centroid of the best candidate layer sequence after filling is calculated to obtain the centroid coordinates of the best candidate layer sequence.

7. A system for acquiring a brain CT perfusion parameter map, comprising a terminal and a server, wherein the server comprises a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, characterized in that: The server obtains the original CT perfusion image from the terminal; when the computer processor executes the computer program, the method for obtaining a brain CT perfusion parameter map according to any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a computer processor, the method for obtaining a brain CT perfusion parameter map according to any one of claims 1 to 6 is implemented.

9. A device for obtaining a brain CT perfusion parameter map, characterized in that: The device comprises: An acquisition module, used to obtain original CT perfusion images; A screening module is used to screen the original CT perfusion images to obtain the best candidate layer sequence; A centroid calculation module, used to calculate the centroid of the best candidate layer sequence to obtain the centroid coordinates of the best candidate layer sequence, and determine the central axis of the best candidate layer sequence at each moment through the centroid coordinates; The correction angle calculation module calculates the correction angle according to the best candidate layer sequence, including: calculating the distance between the left and right edges and the centroid of the best candidate layer sequence at different angles at each time according to a fixed angle step and finding the difference, taking the absolute value of the difference and accumulating it, obtaining the minimum cumulative sum of the difference by comparing the sizes, and determining the correction angle according to the minimum cumulative sum; An image correction module, wherein the correction angle performs image correction on the best candidate layer sequence according to the correction angle, and corrects the CT perfusion image based on the central axis of the best candidate layer at each time; The post-processing module performs post-processing on the corrected CT perfusion image to obtain a brain CT perfusion parameter map.

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