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

Abstract

The present application discloses a method, device, system and computer storage medium for obtaining a brain CT perfusion imaging parameter map. The method comprises: obtaining a CT perfusion image, pre-processing the CT perfusion image, and obtaining a discrete tissue density change curve C(n); reading the acquisition time information of the CT perfusion image, and obtaining an acquisition time array T(n); intercepting the acquisition time array T(n) to obtain a relative acquisition time array t(n); combining the tissue density change curve C(n) with the corresponding relative acquisition time array t(n) to obtain a tissue density time curve C(t(n)) of each pixel point. n ); for the discrete tissue density time curve C(t n ) is fitted or interpolated, and then discretized again at the same time interval to obtain the discretized tissue density time curve C(n)'. The arterial input change curve AIF(n) and the venous output change curve VOF(n) are processed in the same way. The present application improves the usability of the tissue density time curve, helps to reduce the difficulty of solving and improve the accuracy of solving.

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 obtaining a brain CT perfusion imaging parameter map. Background Art

[0002] Stroke, also known as stroke, is an acute cerebrovascular disease. It is a group of diseases that cause brain tissue damage due to sudden rupture of brain blood vessels or blood vessel blockage that prevents blood from flowing into the brain, including ischemic and hemorrhagic strokes, with extremely high disability rates and high mortality rates. Changes in human life structure have led to an increasing incidence of acute ischemic stroke. In recent years, stroke has become one of the main causes of death among Chinese residents. In 2018, the stroke mortality rate in China was 149.49 / 100,000, accounting for 22.3% of the total mortality rate among Chinese residents. Among them, acute ischemic stroke (cerebral infarction) is the most common type of stroke, accounting for 60% to 80% of all strokes.

[0003] Perfusion imaging is most widely used in acute stroke and oncology. During stroke diagnosis, the goal of perfusion imaging is to determine the extent of diseased tissue and to delineate ischemic tissue that can be reperfused. Perfusion measurements are increasingly used for assessment, staging, and post-treatment monitoring.

[0004] Cerebral CT perfusion imaging (CTP) is an imaging technique for evaluating the blood perfusion status of the brain parenchyma and is also an important imaging method for examining acute ischemic stroke. This technique involves rapid continuous CT scans of the head after intravenous injection of contrast agents (such as iodinated contrast agents). Based on the tracer kinetic model and tracer dilution theory, the acquired contrast-enhanced and time-resolved CT images are processed by deconvolution-based or non-deconvolution-based algorithms to generate perfusion parameter maps (such as cerebral blood volume (CBV), cerebral blood flow (CBF), mean transient time (MTT), etc.).

[0005] In the process of CT perfusion image acquisition in the prior art, due to different image acquisition devices or acquisition methods, different frames of the same image sequence may have different acquisition times. The deconvolution method is currently the most commonly used CT perfusion image processing method, see formula (1): ; Where C(t) is the tissue time density curve; AIF(t) is the arterial input function; k(t) is the residual function, or the flow scale residual function. Since different frames of images have different acquisition times, directly using the acquired original images to calculate the residual function k(t) will lead to problems such as decreased accuracy of subsequent perfusion parameter maps, difficulty in solving, and low temporal resolution of the residual function k(t). Summary of the invention

[0006] In order to solve the problems of low accuracy of perfusion parameter maps, great difficulty in solving perfusion parameter maps, and low time resolution of residual function k(t) in the prior art, the present application provides a method for obtaining brain CT perfusion imaging parameter maps.

[0007] The method for obtaining a brain CT perfusion imaging parameter map in the present application includes:

[0008] Acquire a CT perfusion image, perform pre-processing on the CT perfusion image, and obtain a discrete tissue density change curve C(n), where n is the nth acquisition moment, and C(n) is the tissue density corresponding to the nth moment;

[0009] Read the acquisition time information of the CT perfusion image to obtain the acquisition time array T(n), where n is the nth moment, T(n) is the physical moment of the image acquisition device when acquiring the nth image, and T(n) includes Tc(n) corresponding to the discrete tissue density change curve C(n);

[0010] Intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), t(n) including tc(n) corresponding to the discrete tissue density change curve C(n);

[0011] The tissue density change curve C(n) is combined with the corresponding relative acquisition time array t(n) to obtain the tissue density time curve C(t n );

[0012] For the discrete tissue density time curve C(t n ) is fitted or interpolated, and then discretized again at the same time interval to obtain the discretized tissue density time curve C(n)', and the brain CT perfusion imaging parameter map is obtained according to the tissue density time curve C(n)'.

[0013] Optionally, the method further comprises: acquiring a CT perfusion image, pre-processing the CT perfusion image, and obtaining an arterial input function AIF(n), wherein n is the nth acquisition moment;

[0014] Read the acquisition time information of the CT perfusion image to obtain the acquisition time array T(n), where n is the nth time, T(n) is the physical time of the image acquisition device when acquiring the nth image, and T(n) also includes Ta(n) corresponding to the arterial input function AIF(n);

[0015] Intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), t(n) also includes ta(n) corresponding to the arterial input function AIF(n);

[0016] The arterial input function AIF(n) is combined with the corresponding relative acquisition time array t(n) to obtain the arterial input function AIF(t n );

[0017] For the arterial input function AIF(t n ) is fitted or interpolated, and then discretized again at the same time interval to obtain the discretized arterial input function AIF(n)', and the brain CT perfusion imaging parameter map is obtained according to the arterial input function AIF(n)'.

[0018] Optionally, the method includes: acquiring a CT perfusion image, pre-processing the CT perfusion image, and obtaining a discrete tissue density change curve C(n) and an arterial input function AIF(n), wherein n is the nth acquisition moment;

[0019] Read the acquisition time information of the CT perfusion image to obtain the acquisition time array T(n), where n is the nth moment, T(n) is the physical moment of the image acquisition device when acquiring the nth image, and T(n) includes Tc(n) and Ta(n) corresponding to the tissue density change curve C(n) and the arterial input function AIF(n) respectively;

[0020] Intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), t(n) includes tc(n) and ta(n) corresponding to the tissue density change curve C(n) and the arterial input function AIF(n) respectively;

[0021] The tissue density change curve C(n) and the arterial input function AIF(n) are combined with the corresponding relative acquisition time array t(n) to obtain the arterial input function AIF(t n ) and the tissue density time curve C(t n );

[0022] The tissue density time curve C(t n ) and arterial input function AIF(t n) are fitted or interpolated, and the two are discretized again at the same time interval to obtain the discretized tissue density time curve C(n)' and arterial input function AIF(n)'.

[0023] Optionally, the method further comprises: according to the formula The tissue density time curve C(n)' and the arterial input function AIF(n)' are processed to obtain the discretized residual function k(t)';

[0024] Where t is the time variable and k(t) is the residual function.

[0025] Optionally, intercepting the collection time array T(n) to obtain the relative collection time array t(n) specifically includes:

[0026] According to the collection time array T(n), the minimum value Tmin of the collection time array T(n) is found, and the collection time array T(n) is processed according to Tmin to obtain the relative collection time array t(n) starting from the time Tmin.

[0027] Optionally, intercepting the collection time array T(n) to obtain the relative collection time array t(n) specifically includes:

[0028] According to the format of the acquisition time in the CT perfusion image, the acquisition time array T(n) is converted into a time value measured in seconds;

[0029] Traverse the minimum value t(0) of the acquisition time array of each layer of the CT perfusion image, and find the minimum value Tmin in all acquisition time arrays T(n);

[0030] Subtract Tmin from each element in the acquisition time array T(n) of each pixel point to obtain the relative acquisition time array t(n) of each pixel point starting from time Tmin.

[0031] Optionally, the discrete time intervals of the tissue density time curve C(n)' and the arterial input function AIF(n)' are the same, and the starting moments of the tissue density time curve C(n)' and the arterial input function AIF(n)' are the same.

[0032] Optionally, the method further comprises: acquiring a CT perfusion image, pre-processing the CT perfusion image, and obtaining a venous output function VOF(n), where n is the nth acquisition moment;

[0033] Read the acquisition time information of the CT perfusion image to obtain the acquisition time array T(n), where n is the nth time, T(n) is the physical time of the image acquisition device when acquiring the nth image, and T(n) also includes Tv(n) corresponding to the venous output function VOF(n);

[0034] Intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), t(n) also includes tv(n) corresponding to the venous output function VOF(n);

[0035] The venous output function VOF(n) is combined with the corresponding relative acquisition time array t(n) to obtain the venous output function VOF(t n );

[0036] The venous output function VOF(t n ) is fitted or interpolated, and then discretized again at the same time interval to obtain a discretized venous output function VOF(n)', and a brain CT perfusion imaging parameter map is obtained according to the venous output function VOF(n)';

[0037] The discrete time intervals of the tissue density time curve C(n)' and the venous output function VOF(n)' are the same, and the starting moments of the tissue density time curve C(n)' and the venous output function VOF(n)' are the same.

[0038] Optionally, the pre-processing of the CT perfusion image includes brain tissue extraction.

[0039] Optionally, the pre-processing of the CT perfusion image includes motion correction, filtering, skull segmentation to extract brain tissue, and automatic search for arteriovenous points.

[0040] The present application also provides a device for obtaining a brain CT perfusion imaging parameter map, comprising:

[0041] A pre-processing module, which acquires CT perfusion images, pre-processes the CT perfusion images, and obtains the pre-processed brain CT perfusion images, including a tissue density change curve C(n) and a global arterial input function AIF(n) and a venous output function VOF(n);

[0042] The time processing module reads the acquisition time information of the CT perfusion image and obtains the acquisition time array T(n), where n is the nth moment, T(n) is the physical moment of the image acquisition device when the nth image is acquired, and T(n) corresponds to the tissue density change curve C(n); intercepts the acquisition time array T(n) to obtain the relative acquisition time array t(n);

[0043] The discrete processing module combines the tissue density change curve C(n) with the relative acquisition time array t(n) to obtain a tissue density time function diagram of each pixel point; after fitting or interpolating the function diagram, it is discretized again at the same time interval to obtain a discretized function diagram, and the discretized function diagram is a tissue density time curve after time correction.

[0044] Optionally, the discretized function graph includes the tissue density time curve C(n)' and the arterial input function AIF(n)' described in the present application.

[0045] The present application also provides a system for acquiring a brain CT perfusion imaging 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, wherein the server acquires a terminal CT perfusion image from the terminal;

[0046] When the computer processor executes the computer program, the method for obtaining a brain CT perfusion imaging parameter map described in the present application is implemented.

[0047] 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 imaging parameter map described in the present application is implemented.

[0048] The method for obtaining a brain CT perfusion imaging parameter map in this application has at least the following effects:

[0049] After fitting or interpolating the tissue density time curve, it is discretized again at the same time interval, so that different frames of the same image sequence have the same acquisition time, which improves the availability of the tissue density time curve, helps to reduce the difficulty of solution and improve the accuracy of solution;

[0050] By fitting or interpolating the tissue density time curve and the arterial input function at the same time, and then re-discretizing them at the same time interval, the tissue density time curve and the arterial input function have the same starting time and the same time interval, which further reduces the difficulty of solving and improves the accuracy of solving the perfusion parameters;

[0051] After fitting or interpolating the venous output function, the venous output function is discretized again at the same time interval so that different frames of the same image sequence have the same acquisition time, thereby improving the availability of the venous output function. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a schematic diagram of a method for obtaining a brain CT perfusion imaging parameter map in one embodiment of the present application;

[0053] Figure 2 Schematic diagram of a method flow for obtaining a tissue density time curve C(n)' in a method for obtaining a brain CT perfusion imaging parameter map in an embodiment of the present application;

[0054] FIG3( a ) is a before-and-after comparison diagram of a brain CT perfusion imaging parameter map obtained by using the method described in the present application in different embodiments of the present application;

[0055] FIG3( b ) is a before-and-after comparison diagram of brain CT perfusion imaging parameter diagrams obtained by the method described in the present application in different embodiments of the present application;

[0056] FIG4( a ) is a before-and-after comparison diagram of a brain CT perfusion imaging parameter diagram obtained by using the method described in the present application in one embodiment of the present application;

[0057] FIG4( b ) is a before-and-after comparison diagram of a brain CT perfusion imaging parameter diagram obtained by using the method described in the present application in one embodiment of the present application;

[0058] Figure 5 It is a structural block diagram of a device for obtaining a brain CT perfusion imaging parameter map in one embodiment of the present application. DETAILED DESCRIPTION

[0059] In the prior art, the deconvolution method is currently the most commonly used CT perfusion image processing method. See formula (1): ; Wherein, C(t) is the tissue time density curve; AIF(t) is the arterial input function; k(t) is the residual function. The specific description of the three is a common parameter in the prior art and is not repeated here. The deconvolution method assumes that the arterial input function AIF(t), the tissue time density curve C(t) and the residual function k(t) are interrelated through a convolution model. Therefore, the residual function k(t) and the perfusion parameters related thereto can be estimated through the deconvolution operation.

[0060] Since the acquisition time of each frame of CT perfusion image is discrete, the above formula needs to be discretized to obtain formula (2): ;

[0061] in,

[0062] t j is the jth acquisition moment of CT perfusion image;

[0063] c(t j ) is t j The density of the organization at the moment;

[0064] AIF is the arterial input function;

[0065] k is the residual function;

[0066] N is the number of acquisition frames of each layer of perfusion image;

[0067] Δt is the time interval between two consecutive frames of the same layer of perfusion image.

[0068] It can be seen from formula (2) that when performing the deconvolution operation to solve the residual function k(t), the corresponding moments of the arterial input function AIF(t) and the tissue time density curve c(t) must be consistent.

[0069] During CT perfusion image acquisition, due to different image acquisition devices or acquisition methods, images at different spatial positions (i.e., different layers) of the same image sequence may have different acquisition times. In this case, when the same global arterial input function is used for deconvolution, the acquisition time of the time density curve of some layers will inevitably be inconsistent with the acquisition time of the global arterial input function, which will cause the calculation results to underestimate or overestimate the delay, thereby reducing the accuracy of the final perfusion parameter map.

[0070] At the same time, in the same image sequence, the acquisition time intervals of images at the same spatial position (i.e., the same layer) may also be different. When performing discretized deconvolution calculations, different time intervals will increase the difficulty of solving the perfusion parameter map (using Formula 2 to solve the perfusion parameter map requires the same time interval). In addition, during the acquisition of CT perfusion images, if the acquisition time interval of the same layer of images is too large, the residual function k(t) obtained after direct deconvolution calculation will have a low temporal resolution. The time Tmax corresponding to the maximum value of the residual function k(t) is an important reference indicator for determining whether the tissue is ischemic. It can be understood that the residual function k(t) is a "time-value" function, and Tmax is the corresponding time coordinate of the maximum value of the residual function k(t). Therefore, directly using this acquisition time interval will reduce the accuracy of the final perfusion parameter map.

[0071] 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.

[0072] To solve the above technical problems, see Figure 1 In one embodiment of the present application, a method for obtaining a brain CT perfusion imaging parameter map is provided, comprising:

[0073] Step S10, acquiring CT perfusion images, pre-processing the CT perfusion images, and obtaining the pre-processed brain CT perfusion images, including the tissue density change curve C(n) and the global arterial input function AIF(n) and the venous output function VOF(n); the pre-processing of the CT perfusion images includes brain tissue extraction. Specifically, the pre-processing includes motion correction, filtering, skull segmentation to extract brain tissue, and automatic arteriovenous point search for the CT perfusion images.

[0074] Step S20, reading the acquisition time information of the CT perfusion image, obtaining an acquisition time array T(n), wherein n is the nth time, T(n) is the physical time of the image acquisition device when acquiring the nth image, and T(n) corresponds to the tissue density change curve C(n); intercepting the acquisition time array T(n) to obtain a relative acquisition time array t(n);

[0075] Step S30, combining the tissue density variation curve C(n) with the relative acquisition time array t(n) to obtain a tissue density time function diagram of each pixel point;

[0076] Step S40, after fitting or interpolating the function graph, re-discretize it at the same time interval to obtain a discretized function graph, the discretized function graph is a time curve of tissue density after time correction, and the brain CT perfusion imaging parameter graph is obtained according to the discretized function graph.

[0077] In this embodiment, the brain CT perfusion imaging parameter map is obtained by processing the tissue density time curve, the arterial input function and the venous output function. The obtained brain CT perfusion imaging parameter map is obtained by formula 1 according to each function map (tissue density time curve, arterial input function and venous output function), including cerebral blood flow CBF, cerebral blood volume CBV, mean transmission time MTT, residual function peak time Tmax, etc. The above parameters are commonly used parameters by those skilled in the art and will not be explained in detail.

[0078] In the embodiment of the present application, by fitting or interpolating the tissue density time curve and the arterial input function, the tissue density time curve and the arterial input function are made to have the same acquisition time for images of different layers in the same image sequence, which is beneficial to improving the accuracy of the brain CT perfusion image parameter map in subsequent calculations.

[0079] It can be understood that when the image acquisition device acquires CT perfusion images at different locations (different layers) of the brain, the physical meaning of n is the same, that is, the nth acquisition moment of the CT perfusion image of this layer. However, since the image acquisition device may not be able to complete the acquisition of all layers of CT perfusion images at the same time, the physical time corresponding to the actual nth acquisition moment of different layers of images may be different, resulting in different layers of tissue density change curves, global arterial input functions and venous output functions. The start time and time interval are not necessarily the same. After fitting or interpolating each function graph, the embodiment of the present application is re-discretized according to the same time interval, so that each function graph has the same start time and time interval. In this embodiment, taking any one of the tissue density time curve and the arterial input function as an example, this embodiment improves the reliability of the tissue density time curve or the arterial input function, which helps to reduce the difficulty of solving and improve the accuracy of solving.

[0080] In another embodiment, see Figure 2 , the method for obtaining the brain CT perfusion imaging parameter map includes:

[0081] Step S11, obtaining a tissue density change curve, specifically comprising: obtaining a CT perfusion image, pre-processing the CT perfusion image, and obtaining a discrete tissue density change curve C(n), wherein n is the nth acquisition moment, and C(n) is the tissue density corresponding to the nth moment;

[0082] Step S21, reading the image acquisition time information, specifically includes: reading the acquisition time information of the CT perfusion image, obtaining the acquisition time array T(n), wherein n is the nth moment, T(n) is the physical moment of the image acquisition device when acquiring the nth image, and T(n) includes T(c(n) corresponding to the discrete tissue density change curve C(n) n );

[0083] It can be understood that the CT perfusion image contains accurate information about the acquisition time of each frame. When reading the CT perfusion image, the acquisition time of each frame is obtained and saved. Finally, each pixel can obtain an acquisition time array T(n), which corresponds to the tissue density change curve of the pixel. Similarly, n is the nth acquisition time of the pixel. All pixels on the same layer of the image have the same acquisition time array T(n). It can be seen that T(n) must be a monotonically increasing array.

[0084] Step S31, processing the acquisition time array, specifically including: intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), t(n) including t(c) corresponding to the discrete tissue density change curve C(n); n ); Combine the tissue density change curve C(n) with the corresponding relative acquisition time array t(n) to obtain the tissue density time curve C(t n );

[0085] Step S41, fitting or interpolation processing is performed on the tissue density time curve. n ) is fitted or interpolated, and then discretized according to the same time interval dt to obtain a discrete tissue density time curve C(n)', and a brain CT perfusion imaging parameter map is obtained according to the tissue density time curve C(n)'.

[0086] In each embodiment of the present application, since the image acquisition time of the CT perfusion image is discrete, the CT perfusion imaging parameter map obtained by pre-processing can be understood as a discrete curve, for example, the tissue density time curve C (tn) is a discrete curve. The points on each curve are fitted or interpolated. Specifically, the fitting method includes but is not limited to polynomial fitting, curve fitting, etc. The interpolation method includes but is not limited to Lagrange interpolation, Newton interpolation, Hermite interpolation, spline interpolation, etc.

[0087] In another embodiment, the method for obtaining a brain CT perfusion imaging parameter map further includes:

[0088] Step S12, obtaining an arterial input function, specifically including: obtaining a CT perfusion image, pre-processing the CT perfusion image, and obtaining an arterial input function AIF(n), where n is the nth acquisition time;

[0089] Step S22, reading the image acquisition time information, specifically including: reading the acquisition time information of the CT perfusion image, obtaining an acquisition time array T(n), wherein n is the nth time, T(n) is the physical time of the image acquisition device when acquiring the nth image, and T(n) also includes Ta(n) corresponding to the arterial input function AIF(n);

[0090] It can be understood that the CT perfusion image contains accurate information about the acquisition time of each frame. When reading the CT perfusion image, the acquisition time of each frame is obtained and saved. Finally, each pixel can obtain an acquisition time array T(n), which corresponds to the arterial input function AIF(n) of the pixel. Similarly, n is the nth acquisition time of the pixel. All pixels on the same layer of the image have the same acquisition time array T(n).

[0091] Step S32, processing the acquisition time array, specifically including: intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), t(n) also includes ta(n) corresponding to the arterial input function AIF(n); combining the arterial input function AIF(n) with the corresponding relative acquisition time array t(n) to obtain the arterial input function AIF(t n );

[0092] Step S42, input function AIF(t n ) is fitted or interpolated, and then discretized again at the same time interval to obtain the discretized arterial input function AIF(n)', and the brain CT perfusion imaging parameter map is obtained according to the arterial input function AIF(n)'.

[0093] As shown in the figure, FIG3(a) includes the arterial input function AIF(n) before the method described in the embodiment of the present application is adopted, and FIG3(b) includes the arterial input function AIF(n)' after the method described in the embodiment of the present application is adopted. As shown in FIG3(a) and FIG3(b), the ordinate is the parameter value (value), and the abscissa is different time (time).

[0094] To achieve the application of the acquired parameters, in another embodiment, the method for acquiring a brain CT perfusion imaging parameter map further includes: according to formula (1): The tissue density time curve C(n)' and the arterial input function AIF(n)' are solved to obtain the discretized residual function k(t)'; where t is the time variable and k(t) is the residual function. According to the convolution algorithm of the convolution formula (1), it can be understood that the residual function k(t) can be obtained after deconvolution.

[0095] In another embodiment, the discrete time intervals of the tissue density time curve C(n)' and the arterial input function AIF(n)' are the same, and the starting time of the tissue density time curve C(n)' and the arterial input function AIF(n)' is the same. In the embodiment of the present application, by simultaneously applying time fitting and interpolation processing to the tissue density time curve and the arterial input function AIF, the tissue density change curve and the arterial input function have the same starting time (Tmin) and the same time interval (dt), thereby solving the overestimation or underestimation of the delay caused by different starting times in the traditional method, and also solving the increased difficulty of the deconvolution algorithm caused by different time intervals. At the same time, reducing the time interval dt (such as setting dt=1) during re-discretization can increase the time resolution of the residual function k(t) obtained by the deconvolution calculation, thereby improving the accuracy of the perfusion parameter map finally solved. As shown in the figure, Figure 4(a) shows the Tmax parameter diagram obtained before the method described in each embodiment of the present application is adopted; Figure 4(b) is the Tmax parameter diagram obtained after the method described in each embodiment of the present application is adopted. It can be seen from the figure that the method of obtaining brain CT perfusion imaging parameter diagram based on time fitting and interpolation technology of the present application has a significantly improved effect.

[0096] In another embodiment, the method for obtaining a brain CT perfusion imaging parameter map further includes:

[0097] Step S13, obtaining the venous output function, specifically including: obtaining a CT perfusion image, pre-processing the CT perfusion image, and obtaining a venous output function VOF(n), where n is the nth acquisition time;

[0098] Step S23, reading the image acquisition time information, specifically including: reading the acquisition time information of the CT perfusion image, obtaining an acquisition time array T(n), wherein n is the nth time, T(n) is the physical time of the image acquisition device when acquiring the nth image, and T(n) also includes Tv(n) corresponding to the venous output function VOF(n);

[0099] Step S33, processing the acquisition time array, specifically including: intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), t(n) also includes tv(n) corresponding to the venous output function VOF(n); combining the venous output function VOF(n) with the corresponding relative acquisition time array t(n) to obtain the venous output function VOF(t n );

[0100] It can be understood that the CT perfusion image contains accurate information about the acquisition time of each frame. When reading the CT perfusion image, the acquisition time of each frame is obtained and saved. Finally, each pixel can get an acquisition time array T(n), which corresponds to the venous output function VOF(n) of the pixel. Similarly, n is the nth acquisition time of the pixel. All pixels on the same layer of the image have the same acquisition time array T(n).

[0101] Step S43: Calculate the venous output function VOF(t n ) is fitted or interpolated, and then discretized again at the same time interval to obtain a discretized venous output function VOF(n)', and a brain CT perfusion imaging parameter map is obtained according to the venous output function VOF(n)';

[0102] The discrete time intervals of the tissue density time curve C(n)' and the venous output function VOF(n)' are the same, and the starting time of the tissue density time curve C(n)' and the venous output function VOF(n)' are the same. As shown in the figure, FIG3(a) includes the venous output function VOF(n) before the method described in the embodiment of the present application is adopted, and FIG3(b) includes the venous output function VOF(n)' after the method described in the embodiment of the present application is adopted.

[0103] In this embodiment, after the CT perfusion image is time-corrected, the venous output function at all pixel points of the CT perfusion image and the other acquisition parameters (e.g., tissue density change curve, arterial input function) have the same starting time (Tmin) and the same time interval (dt). This embodiment helps to increase the accuracy of the brain CT perfusion imaging parameter map obtained in the subsequent calculation process by making the venous output function VOF (n) and other function graph curves have the same starting time and time interval. This solves the problem of overestimation or underestimation of delay caused by different starting times in traditional methods, and also solves the problem of increased difficulty of the deconvolution algorithm caused by different time intervals. Therefore, the accuracy of the perfusion parameter map finally solved is improved.

[0104] To reveal the method of obtaining the relative acquisition time array t(n), in another embodiment, the acquisition time array T(n) is intercepted to obtain the relative acquisition time array t(n), which specifically includes:

[0105] According to the collection time array T(n), the minimum value Tmin of the collection time array T(n) is found, and the collection time array T(n) is processed according to Tmin to obtain the relative collection time array t(n) starting from the time Tmin.

[0106] Specifically, according to the format of the acquisition time in the CT perfusion image, the acquisition time array T(n) is converted into a time value measured in seconds; the minimum value t(0) of the acquisition time array of each layer of the CT perfusion image is traversed to find the minimum value Tmin in all acquisition time arrays T(n); Tmin is subtracted from each element in the acquisition time array T(n) of each pixel point to obtain the relative acquisition time array t(n) of each pixel point from the time Tmin. The "time-parameter value" of each CT perfusion imaging parameter map can be understood as an image, and Tmin is a node on the time axis. In different embodiments, the Tmin value used in each CT perfusion imaging parameter map is the same to ensure that each CT perfusion imaging parameter map has the same starting time.

[0107] For example, the acquisition time array of the first layer of images is T1(n), and the image acquisition starts at time t1(0). Then, relative to time Tmin, the acquisition starts at time t1(0)=T1(0)-Tmin; the acquisition time array of the i-th layer of images is Ti(n), and the image acquisition starts at time Ti(0). Then, relative to time Tmin, the acquisition starts at time ti(0)=Ti(0)-Tmin. The acquisition time array T(n) corresponding to the global arterial input function AIF(n) and the venous output function VOF(n) is also processed accordingly. Therefore, after processing, the elements in the relative acquisition time array t(n) of all pixels are the time relative to Tmin.

[0108] In summary, this application optimizes the method of obtaining brain CT perfusion imaging parameter maps through a technology based on time fitting and discretization, aiming to solve the above three problems, improve the accuracy of the perfusion parameter map solved by the deconvolution method, and reduce the difficulty of solving. The main implementation process of this method is as follows: First, obtain the tissue time change curve c(n) after pre-processing and the global arterial input function AIF(n) and venous output function VOF(n), where n is the nth acquisition moment; secondly, read the acquisition time information of the CT perfusion image to obtain the acquisition moment array T(n) of each pixel point; thirdly, process the acquisition moment array to obtain a new relative acquisition moment array t(n) for each pixel point relative to the same moment; finally, combine each function or curve with its corresponding relative acquisition moment array t(n), perform fitting or interpolation processing on it, and then discretize it again. Taking the tissue density change curve c(n) as an example, the tissue density change curve c(n) is combined with its corresponding relative acquisition moment array t(n) to obtain the tissue density time curve c(t n ), the tissue density time curve is re-discretized after fitting or interpolation processing, and a new tissue density time curve C(n)' with the same starting time and time interval is obtained.

[0109] It should be understood that although Figure 1-2 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 1-2 At 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.

[0110] In one embodiment, Figure 5 As shown, a device for obtaining a brain CT perfusion imaging parameter map is provided, comprising: a pre-processing module, a time processing module and a discrete processing module, wherein:

[0111] A pre-processing module, which acquires CT perfusion images, pre-processes the CT perfusion images, and obtains the pre-processed brain CT perfusion images, including a tissue density change curve C(n) and a global arterial input function AIF(n) and a venous output function VOF(n);

[0112] The time processing module reads the acquisition time information of the CT perfusion image and obtains the acquisition time array T(n), where n is the nth moment, T(n) is the physical moment of the image acquisition device when the nth image is acquired, and T(n) corresponds to the tissue density change curve C(n); intercepts the acquisition time array T(n) to obtain the relative acquisition time array t(n);

[0113] The discrete processing module combines the tissue density change curve C(n) with the relative acquisition time array t(n) to obtain a tissue density time function diagram of each pixel point; after fitting or interpolating the function diagram, it is discretized again at the same time interval to obtain a discretized function diagram, and the discretized function diagram is a tissue density time curve after time correction.

[0114] For the specific definition of the device for obtaining the brain CT perfusion imaging parameter map, please refer to the definition of the method for obtaining the brain CT perfusion imaging parameter map above, which will not be repeated here. Each module in the above-mentioned device for obtaining the brain CT perfusion imaging 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.

[0115] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 5 As 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 imaging 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.

[0116] Those skilled in the art will understand that Figure 5The 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.

[0117] In one embodiment, a system for acquiring a brain CT perfusion imaging 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 acquires a CT perfusion image from the terminal; when the computer processor executes the computer program, the following steps are implemented:

[0118] Step S11, obtaining a tissue density change curve, specifically comprising: obtaining a CT perfusion image, pre-processing the CT perfusion image, and obtaining a discrete tissue density change curve C(n), wherein n is the nth acquisition moment, and C(n) is the tissue density corresponding to the nth moment;

[0119] Step S21, reading the image acquisition time information, specifically including: reading the acquisition time information of the CT perfusion image, obtaining an acquisition time array T(n), wherein n is the nth time, T(n) is the physical time of the image acquisition device when acquiring the nth image, and T(n) includes T(cn) corresponding to the discrete tissue density change curve C(n);

[0120] Step S31, processing the acquisition time array, specifically including: intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), t(n) including t(cn) corresponding to the discrete tissue density change curve C(n); combining the tissue density change curve C(n) with the corresponding relative acquisition time array t(n) to obtain the tissue density time curve C(tn) of each pixel point;

[0121] Step S41, fitting or interpolating the tissue density time curve. After fitting or interpolating the discrete tissue density time curve C(tn), the discrete tissue density time curve C(n) is obtained by discretizing it at the same time interval dt, and the brain CT perfusion imaging parameter map is obtained according to the tissue density time curve C(n)'.

[0122] 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:

[0123] Step S11, obtaining a tissue density change curve, specifically comprising: obtaining a CT perfusion image, pre-processing the CT perfusion image, and obtaining a discrete tissue density change curve C(n), wherein n is the nth acquisition moment, and C(n) is the tissue density corresponding to the nth moment;

[0124] Step S21, reading the image acquisition time information, specifically including: reading the acquisition time information of the CT perfusion image, obtaining an acquisition time array T(n), wherein n is the nth time, T(n) is the physical time of the image acquisition device when acquiring the nth image, and T(n) includes T(cn) corresponding to the discrete tissue density change curve C(n);

[0125] Step S31, processing the acquisition time array, specifically including: intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), t(n) including t(cn) corresponding to the discrete tissue density change curve C(n); combining the tissue density change curve C(n) with the corresponding relative acquisition time array t(n) to obtain the tissue density time curve C(tn) of each pixel point;

[0126] Step S41, fitting or interpolating the tissue density time curve. After fitting or interpolating the discrete tissue density time curve C(tn), it is discretized according to the same time interval dt to obtain the discretized tissue density time curve C(n)'.

[0127] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0128] Step S12, obtaining the arterial input function, specifically including: obtaining a CT perfusion image, pre-processing the CT perfusion image, and obtaining the arterial input function AIF(n), wherein n is the nth acquisition moment; Step S22, reading the image acquisition time information, specifically including: reading the acquisition time information of the CT perfusion image, and obtaining an acquisition time array T(n), wherein n is the nth moment, T(n) is the physical moment of the image acquisition device when acquiring the nth image, and T(n) also includes Ta(n) corresponding to the arterial input function AIF(n); Step S32, processing the acquisition time array, Specifically, the method includes: intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), wherein t(n) also includes ta(n) corresponding to the arterial input function AIF(n); combining the arterial input function AIF(n) with the corresponding relative acquisition time array t(n) to obtain the arterial input function AIF(tn); step S42, fitting or interpolating the arterial input function AIF(tn), and then re-discretizing it at the same time interval to obtain the discretized arterial input function AIF(n)', and obtaining a brain CT perfusion imaging parameter map according to the arterial input function AIF(n)'.

[0129] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0130] Step S13, obtaining the venous output function, specifically including: obtaining a CT perfusion image, pre-processing the CT perfusion image, and obtaining the venous output function VOF(n), wherein n is the nth acquisition moment; Step S23, reading the image acquisition time information, specifically including: reading the acquisition time information of the CT perfusion image, and obtaining an acquisition time array T(n), wherein n is the nth moment, T(n) is the physical moment of the image acquisition device when acquiring the nth image, and T(n) also includes Tv(n) corresponding to the venous output function VOF(n); Step S33, processing the acquisition time array, specifically including: intercepting the acquisition time array T(n) to obtain a relative acquisition time array t(n), t(n) It also includes tv(n) corresponding to the venous output function VOF(n); the venous output function VOF(n) is combined with the corresponding relative acquisition time array t(n) to obtain the venous output function VOF(tn); step S43, after fitting or interpolating the venous output function VOF(tn), it is discretized again according to the same time interval to obtain the discretized venous output function VOF(n)', and the brain CT perfusion imaging parameter map is obtained according to the venous output function VOF(n)'; the tissue density time curve C(n)' and the venous output function VOF(n)' have the same discrete time interval, and the tissue density time curve C(n)' and the venous output function VOF(n)' have the same starting time. As shown in the figure, Figure 3(a) includes the venous output function VOF(n) before the method described in the embodiment of the present application is adopted, and Figure 3(b) includes the venous output function VOF(n)' after the method described in the embodiment of the present application is adopted.

[0131] 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:

[0132] Step S11, obtaining a tissue density change curve, specifically comprising: obtaining a CT perfusion image, pre-processing the CT perfusion image, and obtaining a discrete tissue density change curve C(n), wherein n is the nth acquisition moment, and C(n) is the tissue density corresponding to the nth moment;

[0133] Step S21, reading the image acquisition time information, specifically including: reading the acquisition time information of the CT perfusion image, obtaining an acquisition time array T(n), wherein n is the nth time, T(n) is the physical time of the image acquisition device when acquiring the nth image, and T(n) includes T(cn) corresponding to the discrete tissue density change curve C(n);

[0134] Step S31, processing the acquisition time array, specifically including: intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), t(n) including t(cn) corresponding to the discrete tissue density change curve C(n); combining the tissue density change curve C(n) with the corresponding relative acquisition time array t(n) to obtain the tissue density time curve C(tn) of each pixel point;

[0135] Step S41, fitting or interpolating the tissue density time curve. After fitting or interpolating the discrete tissue density time curve C(tn), it is discretized according to the same time interval dt to obtain the discretized tissue density time curve C(n)'.

[0136] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0137] Step S12, obtaining the arterial input function, specifically including: obtaining a CT perfusion image, pre-processing the CT perfusion image, and obtaining the arterial input function AIF(n), wherein n is the nth acquisition moment; Step S22, reading the image acquisition time information, specifically including: reading the acquisition time information of the CT perfusion image, and obtaining an acquisition time array T(n), wherein n is the nth moment, T(n) is the physical moment of the image acquisition device when acquiring the nth image, and T(n) also includes Ta(n) corresponding to the arterial input function AIF(n); Step S32, processing the acquisition time array, Specifically, the method includes: intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), wherein t(n) also includes ta(n) corresponding to the arterial input function AIF(n); combining the arterial input function AIF(n) with the corresponding relative acquisition time array t(n) to obtain the arterial input function AIF(tn); step S42, fitting or interpolating the arterial input function AIF(tn), and then re-discretizing it at the same time interval to obtain the discretized arterial input function AIF(n)', and obtaining a brain CT perfusion imaging parameter map according to the arterial input function AIF(n)'.

[0138] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0139] Step S13, obtaining the venous output function, specifically including: obtaining a CT perfusion image, pre-processing the CT perfusion image, and obtaining the venous output function VOF(n), wherein n is the nth acquisition moment; Step S23, reading the image acquisition time information, specifically including: reading the acquisition time information of the CT perfusion image, and obtaining an acquisition time array T(n), wherein n is the nth moment, T(n) is the physical moment of the image acquisition device when acquiring the nth image, and T(n) also includes Tv(n) corresponding to the venous output function VOF(n); Step S33, processing the acquisition time array, specifically including: intercepting the acquisition time array T(n) to obtain a relative acquisition time array t(n), t(n) It also includes tv(n) corresponding to the venous output function VOF(n); the venous output function VOF(n) is combined with the corresponding relative acquisition time array t(n) to obtain the venous output function VOF(tn); step S43, after fitting or interpolating the venous output function VOF(tn), it is discretized again according to the same time interval to obtain the discretized venous output function VOF(n)', and the brain CT perfusion imaging parameter map is obtained according to the venous output function VOF(n)'; the tissue density time curve C(n)' and the venous output function VOF(n)' have the same discrete time interval, and the tissue density time curve C(n)' and the venous output function VOF(n)' have the same starting time. As shown in the figure, Figure 3(a) includes the venous output function VOF(n) before the method described in the embodiment of the present application is adopted, and Figure 3(b) includes the venous output function VOF(n)' after the method described in the embodiment of the present application is adopted.

[0140] Those of ordinary skill in the art can understand that all or part of the processes in the methods described in the above embodiments 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 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.

[0141] 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.

[0142] 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 a person of ordinary skill in the art, 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 imaging parameter map, characterized in that: include: Acquire CT perfusion images, pre-process the CT perfusion images, obtain arterial input function AIF(n), and discrete tissue density change curve C(n), where n is the nth acquisition moment, and C(n) is the tissue density corresponding to the nth moment; Read the acquisition time information of the CT perfusion image to obtain the acquisition time array T(n), where n is the nth moment, T(n) is the physical moment of the image acquisition device when acquiring the nth image, T(n) includes Tc(n) corresponding to the discrete tissue density change curve C(n), and T(n) also includes Ta(n) corresponding to the arterial input function AIF(n); Intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), t(n) includes tc(n) corresponding to the discrete tissue density change curve C(n), and t(n) also includes ta(n) corresponding to the arterial input function AIF(n); The tissue density change curve C(n) is combined with the corresponding relative acquisition time array t(n) to obtain the tissue density time curve C(t n ), combining the arterial input function AIF(n) with the corresponding relative acquisition time array t(n), the arterial input function AIF(t n ); For the discrete tissue density time curve C(t n ) is fitted or interpolated, and then discretized again at the same time interval to obtain the discrete tissue density time curve C(n)'. n ) is fitted or interpolated, and then discretized again at the same time interval to obtain the discretized arterial input function AIF(n)'. According to the tissue density time curve C(n)' and the arterial input function AIF(n)', the brain CT perfusion imaging parameter map is obtained by the deconvolution method.

2. The method for obtaining a brain CT perfusion imaging parameter map according to claim 1, characterized in that: The method further comprises: According to the formula The tissue density time curve C(n)' and the arterial input function AIF(n)' are processed to obtain the discretized residual function k(t)'; Where t is the time variable and k(t) is the residual function.

3. The method for obtaining a brain CT perfusion imaging parameter map according to claim 1, characterized in that: The pre-processing includes: performing motion correction, filtering, segmenting the skull to extract brain tissue, and automatically searching for arteriovenous points on the CT perfusion image.

4. The method for obtaining a brain CT perfusion imaging parameter map according to claim 1, characterized in that: The intercepting the collection time array T(n) to obtain the relative collection time array t(n) specifically includes: According to the format of the acquisition time in the CT perfusion image, the acquisition time array T(n) is converted into a time value measured in seconds; Traverse the minimum value t(0) of the acquisition time array of each layer of the CT perfusion image, and find the minimum value Tmin in all acquisition time arrays T(n); Subtract Tmin from each element in the acquisition time array T(n) of each pixel point to obtain the relative acquisition time array t(n) of each pixel point starting from time Tmin.

5. The method for obtaining a brain CT perfusion imaging parameter map according to claim 1, characterized in that: The discrete time intervals of the tissue density time curve C(n)' and the arterial input function AIF(n)' are the same, and the starting moments of the tissue density time curve C(n)' and the arterial input function AIF(n)' are the same.

6. The method for obtaining a brain CT perfusion imaging parameter map according to claim 1, characterized in that: The method further comprises: Acquire CT perfusion images, pre-process the CT perfusion images, and obtain a venous output function VOF(n), where n is the nth acquisition time; Read the acquisition time information of the CT perfusion image to obtain the acquisition time array T(n), where n is the nth time, T(n) is the physical time of the image acquisition device when acquiring the nth image, and T(n) also includes Tv(n) corresponding to the venous output function VOF(n); Intercepting the acquisition time array T(n) to obtain the relative acquisition time array t(n), t(n) also includes tv(n) corresponding to the venous output function VOF(n); The venous output function VOF(n) is combined with the corresponding relative acquisition time array t(n) to obtain the venous output function VOF(t n ); The venous output function VOF(t n ) is fitted or interpolated, and then discretized again at the same time interval to obtain the discretized venous output function VOF(n)', and a brain CT perfusion imaging parameter map is obtained by a deconvolution method according to the venous output function VOF(n)'; The discrete time intervals of the tissue density time curve C(n)' and the venous output function VOF(n)' are the same, and the starting moments of the tissue density time curve C(n)' and the venous output function VOF(n)' are the same.

7. A device for obtaining a brain CT perfusion imaging parameter map, characterized in that: include: A pre-processing module, which acquires CT perfusion images, pre-processes the CT perfusion images, and obtains the pre-processed brain CT perfusion images, including a tissue density change curve C(n) and a global arterial input function AIF(n) and a venous output function VOF(n); The time processing module reads the acquisition time information of the CT perfusion image to obtain the acquisition time array T(n), T(n) includes Tc(n) corresponding to the discrete tissue density change curve C(n), T(n) also includes Ta(n) corresponding to the arterial input function AIF(n), wherein n is the nth moment, and T(n) is the physical moment of the image acquisition device when the nth image is acquired; intercepting the acquisition time array T(n), obtaining the relative acquisition time array t(n), t(n) includes tc(n) corresponding to the discrete tissue density change curve C(n), t(n) also includes ta(n) corresponding to the arterial input function AIF(n); The discrete processing module combines the tissue density change curve C(n) with the corresponding relative acquisition time array t(n) to obtain the tissue density time curve C(t n ), combining the arterial input function AIF(n) with the corresponding relative acquisition time array t(n), the arterial input function AIF(t n ); for the discrete tissue density time curve C(t n ) is fitted or interpolated, and then discretized again at the same time interval to obtain the discrete tissue density time curve C(n)'. The tissue density time curve C(n)' is the tissue density time curve after time correction. The arterial input function AIF(t n ) is fitted or interpolated, and then discretized again at the same time interval to obtain the discretized arterial input function AIF(n)'. According to the tissue density time curve C(n)' and the arterial input function AIF(n)', the brain CT perfusion imaging parameter map is obtained by the deconvolution method.

8. A system for obtaining a brain CT perfusion imaging 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 CT perfusion images from the terminal; when the computer processor executes the computer program, the method for obtaining a brain CT perfusion imaging parameter map as described in any one of claims 1 to 6 is implemented.

9. 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 imaging parameter map according to any one of claims 1 to 6 is implemented.

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