A multi-blood flow parameter synchronous imaging method based on cerebral angiography images

Abstract

This invention relates to cerebral angiography. A method for post-processing multi-frame image files from digital subtraction angiography (DSA) includes: opening the multi-frame images, performing subtraction, and calculating the overall vascular projection using a maximum density projection algorithm; outlining the carotid artery template based on the overall vascular projection, and extracting the arterial input function (AIF) from the multi-frame images; extracting the signal variation function for each pixel in the multi-frame images; using the singular value decomposition (SVD) algorithm, deconvolving the signal variation function of each pixel with the AIF to obtain the blood flow velocity (CBF) and contrast agent peak time for each intracerebral location (pixel); obtaining the correlation between carotid artery blood flow and blood flow at each intracerebral location based on the peak time using a cross-correlation algorithm; classifying arteries and veins and labeling them with pseudo-color based on the blood flow arrival time; and outputting a whole-brain blood flow velocity map, a peak time map, and a carotid artery blood flow distribution correlation map. This invention facilitates the standardized interpretation of DSA cerebral blood flow images and the standardized clinical diagnostic process.

Description

Technical Field

[0001] This invention relates to cerebral angiography, and more particularly to a synchronous imaging method based on cerebral angiography images. Background Technology

[0002] Digital subtraction angiography (DSA) of cerebral blood vessels is the most standard method for imaging cerebral blood vessels in medicine. This technique involves inserting a catheter into the carotid artery and injecting contrast agent through the catheter, enabling continuous imaging of intracranial blood vessels under X-ray conditions. After removing the background image using X-ray subtraction, the morphology of the cerebral blood vessels and blood flow can be displayed in real time, allowing physicians to assess the health of the cerebral blood vessels. This mature technology has been widely used clinically for decades and provides the gold standard for diagnosing various cerebrovascular diseases. The technology provides multi-frame images unfolded over time, dynamically showing the contrast agent flowing in from the carotid artery, through intracranial arteries and capillaries, and finally out through the veins. However, currently, this technology can only qualitatively describe the morphology of cerebral blood vessels, requiring physicians to rely on experience to roughly judge the distribution of blood vessels and blood flow. It has three major limitations: 1. It cannot quantitatively describe the cerebral blood flow (CBF) of each blood flow point, nor can it quantify the time to arrival (MTT) of blood flow from the carotid artery to each brain region; 2. It cannot quantitatively describe the shunt distribution of blood flow entering the brain from the carotid artery; 3. It cannot distinguish between arteries and veins. The above-mentioned shortcomings limit the standardized interpretation of DSA cerebral blood flow images, which is not conducive to the standardization of clinical diagnostic procedures, nor to the application of DSA data for scientific research. Summary of the Invention

[0003] This invention aims to overcome the shortcomings of existing technologies and provide a method for simultaneous imaging of multiple blood flow parameters based on cerebral angiography images. This invention performs post-processing on multiple frames of cerebral vascular DSA images, enabling the simultaneous output of multi-parameter imaging maps. This facilitates the standardized interpretation of DSA cerebral blood flow images, standardizes the clinical diagnostic process, and also benefits scientific research using DSA data.

[0004] The technical solution of this invention is implemented as follows: a method for post-processing multi-frame image files of digital subtraction angiography includes the following steps:

[0005] Step 1: Open multiple frames of images, complete the subtraction, and calculate the projection of the entire blood vessel according to the maximum density projection algorithm;

[0006] Step 2: Based on the projection of the entire blood vessels, the carotid artery template is delineated, and the arterial input function AIF is extracted from multiple frames of images;

[0007] Step 3: Extract the signal variation function for each pixel of the multi-frame images;

[0008] Step 4: Using the Singular Value Decomposition (SVD) algorithm, deconvolve the signal change function of each pixel into the AIF to obtain the cerebral blood flow (CBF) and the time to peak contrast agent blood flow (Tmax) for each intracranial location (i.e., pixel).

[0009] Step 5: Based on the peak time, obtain the correlation between carotid artery blood flow and blood flow at each intracranial location using a cross-correlation algorithm;

[0010] Step 6: Based on the arrival time of blood flow, classify arteries and veins and mark them with pseudo-color.

[0011] Step 7: Finally, output the whole brain blood flow velocity map, peak time map, and carotid artery blood flow distribution correlation map.

[0012] The method for post-processing multi-frame image files of digital subtraction angiography includes, in step one, maximum density projection, which comprises:

[0013] 1) DSA is a 3D multi-frame image, one dimension of which is the time axis. Each frame only shows a local part of the blood vessel indicated by the contrast agent at the current time point.

[0014] 2) 3D images are stacked on the timeline, and the value of each pixel is set to the maximum value of that point on the timeline;

[0015] 3) Finally, a 2D projection image is generated, showing the maximum contrast agent distribution at all time points during the DSA process, which serves as a vascular template for users to select the AIF value box for the arterial input function.

[0016] The method for post-processing multi-frame image files of digital subtraction angiography includes a method for post-processing digital subtraction angiography. In step seven, the peak time map includes the arrival time and identification of arterial and venous blood flow; and the blood flow distribution map includes the correlation coefficient of carotid artery blood flow distribution in the whole brain.

[0017] This invention performs post-processing on multi-frame DSA images of cerebral blood vessels, enabling the simultaneous output of multi-parameter imaging maps. These parameters include the time, velocity, flow rate, and correlation of blood flow from the carotid artery into blood vessels at all levels throughout the brain. These parametric images will be beneficial for: 1. Quantitatively describing the cerebral blood flow (CBF) of each brain region, thereby accurately identifying sparse and non-flowing areas; 2. Quantitatively describing the correlation between blood flow in each brain region and the inflow from the carotid artery, thereby assisting clinicians in accurately interpreting the distribution of carotid inflow within the brain; 3. Differentiating the distribution of arteries, capillaries, and veins by using the contrast agent peak time, which is beneficial for better displaying anatomical structures. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings:

[0019] Figure 1This is a flowchart of the present invention.

[0020] Figure 2 This is a whole-brain blood flow velocity diagram according to an embodiment of the present invention;

[0021] Figure 3 This is a diagram showing the distribution of blood flow in the internal carotid artery according to an embodiment of the present invention;

[0022] Figure 4 This refers to the blood flow arrival time (seconds) and the transitional color markings between arteries and veins in this embodiment of the invention.

[0023] Figure 5 This is a partial image of the blood vessels as shown by the contrast agent at a specific time point.

[0024] Figure 6 This is a diagram illustrating the overlay of images on the timeline (the white box represents the user-defined AIF value box). Detailed Implementation

[0025] like Figures 1-4 As shown, a method for post-processing multi-frame image files of digital subtraction angiography includes the following steps:

[0026] Step 1: Open multiple frames of images, complete the subtraction, and calculate the projection of the entire blood vessel according to the maximum density projection algorithm;

[0027] Step 2: Based on the projection of the entire blood vessels, the carotid artery template is delineated, and the arterial input function AIF is extracted from multiple frames of images;

[0028] Step 3: Extract the signal variation function for each pixel of the multi-frame images;

[0029] Step 4: Using the Singular Value Decomposition (SVD) algorithm, deconvolve the signal function of each pixel into the AIF to obtain the cerebral blood flow (CBF) and the time to peak contrast agent blood flow (Tmax) for each intracranial location (i.e., pixel).

[0030] Step 5: Based on the peak time, obtain the correlation between carotid artery blood flow and blood flow at each intracranial location using a cross-correlation algorithm;

[0031] Step 6: Based on the arrival time of blood flow, classify arteries and veins and mark them with pseudo-color.

[0032] Step 7: Finally, output the whole brain blood flow velocity map, peak time map, and carotid artery blood flow distribution correlation map.

[0033] The method for post-processing multi-frame image files of digital subtraction angiography includes a method for post-processing digital subtraction angiography. In step seven, the peak time map includes the arrival time and identification of arterial and venous blood flow; and the blood flow distribution map includes the correlation coefficient of carotid artery blood flow distribution in the whole brain.

[0034] Maximum density projection steps:

[0035] 1) DSA is a 3D multi-frame image (one dimension of which is the time axis). Each frame only displays a local area of ​​the blood vessel indicated by the contrast agent at the current time point, such as... Figure 5 Example:

[0036] 2) 3D images are superimposed and stacked on the timeline, with the value of each pixel set to its maximum value on the timeline, such as... Figure 6 :

[0037] 3) Finally, a 2D projection image is generated, showing the maximum contrast agent distribution at all time points during the DSA process, which serves as a vascular template for the user to select the AIF value box for the arterial input function.

[0038] Singular value decomposition deconvolution algorithm:

[0039] 1. After the user outlines the value box in the blood vessel template, this software automatically extracts the AIF of that region, i.e., Ca(t).

[0040] 2. Extract the time-density function for each pixel in the original DSA multi-frame image: C u (t)

[0041] 3. According to the following formula, C u (t) is proportional to C a Convolution of (t) and residual function R(t):

[0042]

[0043] 4. The curve of the residual function R(t) is obtained by using SVD deconvolution, that is:

[0044]

[0045] 5. Thus, the residual function R(t) curve of each pixel position is obtained. The time point corresponding to the maximum value of the curve is the time Tmax when the contrast agent blood flow reaches its peak. The R(t) value corresponding to Tmax is obtained simultaneously, which is the cerebral blood flow (CBF).

[0046] Cross-correlation algorithm

[0047] 1. Calculate C a (t) and C u (t) Cross-correlation of the two curves, where the delay value is set to Tmax;

[0048] 2. The cross-correlation value of each pixel represents the degree of distribution of the input arterial blood flow on the image;

[0049] 3. Since Tmax directly provides the difference between the input artery (i.e., the artery where the value box is located) and the peak contrast agent blood flow time Tmax of each pixel, the process of the cross-correlation algorithm constantly trying to find the maximum value is reduced, which significantly improves the calculation speed.

[0050] This invention cleverly uses Tmax obtained from SVD deconvolution to assign cross-correlation parameters, reducing cross-correlation computation time. Taking images generated by a GE flat-panel DSA as an example, each frame contains 10... 6 For each pixel, with a 0.25-second interval between each image frame, cross-correlation calculations are attempted at 0.25-second intervals, with a maximum interval of 5 seconds. Each pixel requires 20 calculations. Using an Intel Xeon W2145 CPU, performing parallel calculations on one dimension of the image (4 cores) and serial calculations on the other, completing all pixel calculations takes approximately 7 minutes. With the technical solution of this invention, completing all calculations under equivalent hardware conditions takes less than 15 seconds.

Claims

1. A method of post-processing multi-frame image files of digital subtraction angiography, characterized in that, Includes the following steps: Step 1: Open multiple frames of images, complete the subtraction, and calculate the projection of the entire blood vessel according to the maximum density projection algorithm; Step 2: Based on the projection of the entire blood vessels, the carotid artery template is delineated, and the arterial input function AIF is extracted from multiple frames of images; Step 3: Extract the signal variation function for each pixel of the multi-frame images; Step 4: Using the Singular Value Decomposition (SVD) algorithm, deconvolve the signal change function of each pixel into the AIF to obtain the cerebral blood flow (CBF) and the time to peak contrast agent flow (Tmax) for each intracerebral pixel. Step 5: Based on the peak time, obtain the correlation between carotid artery blood flow and blood flow at each intracranial location using a cross-correlation algorithm; After the user outlines the value frame in the blood vessel template, the system automatically extracts the arterial input function AIF, namely Ca(t), for that region; extracts the time-concentration function Cu(t) for each pixel in the original DSA multi-frame image; calculates the cross-correlation between the two curves Ca(t) and Cu(t), where the delay value is set to Tmax; the cross-correlation value of each pixel represents the degree of distribution of the input arterial blood flow in the image; Step 6: Based on the arrival time of blood flow, classify arteries and veins and mark them with pseudo-color. Step 7: Finally, output the whole brain blood flow velocity map, peak time map, and carotid artery blood flow distribution correlation map.

2. A method of post-processing multi-frame image files of digital subtraction angiography according to claim 1, characterized in that, The maximum density projection in step one includes: 1) DSA is a 3D multi-frame image, one dimension of which is the time axis. Each frame only shows a local part of the blood vessel shown by the contrast agent at the current time point. 2) 3D images are stacked on the timeline, and the value of each pixel is set to the maximum value of that point on the timeline; 3) Finally, a 2D projection image is generated, showing the maximum contrast agent distribution at all time points during the DSA process, which serves as a vascular template for users to select the AIF value box for the arterial input function.

3. A method of post-processing multi-frame image files of digital subtraction angiography according to claim 1, characterized in that, In step seven, the peak time map includes the arrival time and markers of arterial and venous blood flow; the blood flow distribution map includes the correlation coefficient of carotid artery blood flow distribution throughout the brain.

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