Method, device and equipment for recognizing brain image after thrombectomy and storage medium

Abstract

Embodiments of the present disclosure provide a method, device and equipment for identifying brain images after thrombectomy and a storage medium, relating to the technical field of medical image processing, comprising: obtaining multi-modal brain images of a patient after thrombectomy; dividing the multi-modal brain images based on the signal intensity of pixels in the multi-modal brain images to obtain infarction areas and non-infarction areas; determining relative cerebral blood flow based on the infarction areas and determining maximum delay time based on the non-infarction areas; determining an identification result in the multi-modal brain images in combination with the relative cerebral blood flow and the maximum delay time; and wherein the identification result comprises tissue hypoperfusion type information. The present disclosure can distinguish the hypoperfusion types of the multi-modal brain images.

Description

Technical Field

[0001] This disclosure relates to the field of medical image processing technology, and in particular to a method, apparatus, device and storage medium for recognizing brain images after thrombectomy. Background Technology

[0002] In patients with acute ischemic stroke, cerebral blood vessels are blocked by thrombi, leading to cerebral ischemia and hypoxia. Mechanical thrombectomy uses interventional devices to directly remove or break up the thrombus blocking the blood vessel, rapidly restoring cerebral blood flow. Although the recanalization rate of large vessels after endovascular mechanical thrombectomy can reach over 85%-90%, a rate as high as 20%-50% still fails to achieve adequate blood flow reperfusion at the cerebral tissue level.

[0003] Traditional assessments of cerebral blood flow reperfusion after endovascular mechanical thrombectomy are mainly based on the eTICI scoring system. This scoring system relies on the surgeon's experience and visual interpretation, which is easily influenced by subjectivity. Furthermore, it depends on digital subtraction angiography (DSA) to assess arterial blood flow, treating incomplete tissue-level reperfusion as a single phenomenon and failing to distinguish between regional hypoperfusion caused by distal emboli and microcirculatory no-reflow caused by microvascular dysfunction. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems, or at least partially solve the above-mentioned technical problems, this disclosure provides a method, device, equipment and storage medium for recognizing brain images after thrombectomy.

[0005] This disclosure provides a method for recognizing brain images after thrombectomy. The method includes: acquiring multimodal brain images of a patient after thrombectomy; dividing the multimodal brain images into infarct and non-infarct areas based on the signal intensity of pixels in the multimodal brain images; determining the relative cerebral blood flow based on the infarct area and the maximum delay time based on the non-infarct area; and combining the relative cerebral blood flow and the maximum delay time to determine the recognition result in the multimodal brain images; wherein the recognition result includes information on the type of tissue hypoperfusion.

[0006] In this implementation, the present application objectively divides the infarct area and the non-infarct area by the intensity of image signal, which can solve the problem of subjective variation in personal experience and visual judgment; it specifically determines the relative cerebral blood flow in the infarct area and the maximum delay time in the non-infarct area, so as to match the perfusion parameters with the low perfusion mechanism in different regions; by combining the relative cerebral blood flow and the maximum delay time for multimodal brain image recognition, it can distinguish the low perfusion type in the infarct area and the non-infarct area, which can solve the problem that the traditional eTICI score cannot distinguish the low perfusion of different mechanisms and has large inter-observer variability, and provides a standardized, mechanism-specific assessment tool.

[0007] In one possible implementation, multimodal brain imaging includes CT perfusion imaging and magnetic resonance diffusion-weighted imaging within a predetermined time after thrombectomy.

[0008] In this approach, multimodal brain images are acquired after thrombectomy, overcoming the limitations of single-modal assessment and enabling the coordinated acquisition of tissue structure and hemodynamic information.

[0009] In one possible implementation, multimodal brain images are divided based on the signal intensity of pixels in multimodal brain images to obtain infarct areas and non-infarct areas, including: identifying the signal intensity of each pixel in magnetic resonance diffusion-weighted imaging; comparing the signal intensity of each pixel with a signal intensity threshold, classifying high-signal areas with signal intensity greater than or equal to the signal intensity threshold as infarct areas, and classifying low-signal areas with signal intensity less than the signal intensity threshold as non-infarct areas.

[0010] In this implementation, by comparing the pixel signal intensity thresholds of magnetic resonance diffusion-weighted imaging, infarct areas and non-infarct areas can be objectively identified, avoiding the bias of traditional subjective interpretation.

[0011] In one possible implementation, the method for identifying brain images after thrombectomy further includes: aligning CT perfusion imaging and magnetic resonance diffusion-weighted imaging using an image registration method; and determining the corresponding infarct area and non-infarct area in CT perfusion imaging based on the infarct area and non-infarct area in the magnetic resonance diffusion-weighted imaging.

[0012] In this implementation, the infarct area and the non-infarct area are objectively divided in magnetic resonance diffusion-weighted imaging based on the comparison of pixel signal intensity thresholds. Then, the partitioning result is mapped to CT perfusion imaging through image registration, so as to achieve spatial alignment and anatomical partitioning of the two modal images, which can reduce the parameter extraction error caused by spatial misalignment of multimodal images.

[0013] In one possible implementation, determining relative cerebral blood flow based on the infarct area includes: determining a target area within the non-infarct area of ​​CT perfusion imaging; wherein the target area is a mirror image of the infarct area; obtaining the first cerebral blood flow corresponding to each pixel in the infarct area and the second cerebral blood flow corresponding to each pixel in the target area based on CT perfusion imaging; and for each pixel in the infarct area, calculating the ratio of the first cerebral blood flow to the second cerebral blood flow corresponding to the mirror image pixel in the target area to obtain the relative cerebral blood flow.

[0014] In this implementation, the healthy side mirror region is used as a reference to calculate the relative cerebral blood flow for the infarct area. This can quantify the degree of perfusion loss and help eliminate the interference of confounding factors such as individual baseline blood flow and scanning parameters, providing an objective data basis for assessing the perfusion status of the infarct area.

[0015] In one possible implementation, the identification results in multimodal brain images are determined by combining relative cerebral blood flow and maximum delay time, including: for infarcted areas, calculating the volume ratio of areas with relative cerebral blood flow less than a flow threshold to the infarcted area; when the volume ratio is greater than a volume ratio threshold, the tissue hypoperfusion type in the multimodal brain images is determined to be microcirculation no-reflow; for non-infarcted areas, comparing the maximum delay time with a maximum delay time threshold; when there are areas with a maximum delay time greater than the maximum delay time threshold and the area morphology meets the morphological requirements, the tissue hypoperfusion type in the multimodal brain images is determined to be regional hypoperfusion; when the tissue hypoperfusion type in the multimodal brain images includes both microcirculation no-reflow and regional hypoperfusion, the tissue hypoperfusion type in the multimodal brain images is determined to be a mixed phenotype.

[0016] This implementation employs an automated classification method based on threshold parameters, which can objectively distinguish between the two phenotypes of tissue hypoperfusion in multimodal brain imaging after thrombectomy, avoiding subjective variations based on personal experience and visual judgment. Furthermore, it considers the possibility of coexistence of the two features, further improving the accuracy of multimodal brain imaging.

[0017] In one possible implementation, when there is a region with a maximum delay time greater than a maximum delay time threshold and the region morphology meets the morphological requirements, the tissue hypoperfusion type in the multimodal brain image is determined to be regional hypoperfusion, including: dividing the region with a maximum delay time greater than the maximum delay time threshold into a hypoperfusion region; identifying the eccentricity and number of connected components in the hypoperfusion region; when the eccentricity of the hypoperfusion region is greater than the eccentricity threshold and the number of connected components is less than the number threshold, then the region morphology is determined to be wedge-shaped along the blood vessels; when the region morphology is wedge-shaped along the blood vessels, the tissue hypoperfusion type in the multimodal brain image is determined to be regional hypoperfusion.

[0018] In this implementation, low-perfusion areas are first screened for non-infarct areas using the maximum delay time threshold, and then quantitative indicators such as eccentricity and number of connected components are used to determine whether the low-perfusion areas are distributed in a wedge shape along the blood vessels, thereby improving the accuracy of regional low perfusion identification in multimodal brain images.

[0019] This disclosure also provides a device for recognizing brain images after thrombectomy. The device includes: an acquisition module for acquiring multimodal brain images of a patient after thrombectomy; a segmentation module for segmenting the multimodal brain images based on the signal intensity of pixels in the multimodal brain images to obtain infarcted and non-infarcted areas; an analysis module for determining relative cerebral blood flow based on the infarcted area and determining the maximum delay time based on the non-infarcted area; and a recognition module for combining relative cerebral blood flow and maximum delay time to determine the recognition result in the multimodal brain images. The recognition result includes information on the type of tissue hypoperfusion.

[0020] This disclosure also provides a computing device, which includes: a processor; a memory for storing processor-executable instructions; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method for recognizing brain images after thrombectomy as provided in this disclosure.

[0021] This disclosure also provides a computer-readable storage medium storing a computer program for performing a method for recognizing brain images after thrombectomy as provided in this disclosure. Attached Figure Description

[0022] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0023] Figure 1 A flowchart illustrating a method for recognizing brain images after thrombectomy, provided in an embodiment of this disclosure; Figure 2 A schematic diagram of the structure of a brain image recognition device after thrombectomy provided in an embodiment of this disclosure; Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of the present disclosure. Detailed Implementation

[0024] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0025] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0026] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0027] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0028] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0029] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0030] Traditional assessments of cerebral reperfusion after endovascular mechanical thrombectomy primarily rely on the eTICI scoring system, which depends on digital subtraction angiography to assess arterial blood flow. Studies show that approximately 90% of patients with eTICI 2b exhibit wedge-shaped hypoperfusion, while 15-25% of patients with eTICI 2c / 3 still show hypoperfusion, mainly due to no-reflow phenomena. The eTICI scoring system views incomplete tissue-level reperfusion as a single phenomenon and cannot distinguish between regional hypoperfusion caused by distal emboli and microcirculatory no-reflow due to microvascular dysfunction.

[0031] To address the aforementioned issues, this disclosure provides a method for recognizing brain images after thrombectomy. The method includes: acquiring multimodal brain images of a patient after thrombectomy; dividing the multimodal brain images into infarct and non-infarct areas based on the signal intensity of pixels in the multimodal brain images; determining relative cerebral blood flow based on the infarct area and determining the maximum delay time based on the non-infarct area; and combining the relative cerebral blood flow and the maximum delay time to determine the recognition result in the multimodal brain images; wherein the recognition result includes information on the type of tissue hypoperfusion. This application objectively divides infarcted and non-infarcted areas based on image signal intensity, thus addressing the problem of subjective variability caused by personal experience and visual judgment. It specifically determines relative cerebral blood flow in infarcted areas and maximum delay time in non-infarcted areas, achieving the matching of perfusion parameters with the mechanisms of low perfusion in different regions. By combining relative cerebral blood flow and maximum delay time for multimodal brain image recognition, it can distinguish the types of low perfusion in infarcted and non-infarcted areas, solving the problem that traditional eTICI scoring cannot distinguish between different mechanisms of low perfusion and has large inter-observer variability, providing a standardized, mechanism-specific assessment tool.

[0032] The method will be described below with reference to specific embodiments.

[0033] Figure 1 This is a flowchart illustrating a method for recognizing brain images after thrombectomy, provided in an embodiment of this disclosure. The method can be executed by a device for recognizing brain images after thrombectomy, which can be implemented using software and / or hardware, and is generally integrated into a computing device. Figure 1 As shown, the method includes: S101. Obtain multimodal brain images of the patient after thrombectomy.

[0034] Brain images are acquired within a predetermined time period following endovascular thrombectomy (EVT). For example, the predetermined time period is within 48 hours post-procedure.

[0035] Among them, multimodal brain imaging refers to images that are routinely available in clinical settings. In one possible implementation, multimodal brain imaging includes computed tomography perfusion imaging (CTP) and magnetic resonance diffusion-weighted imaging (DWI) within a predetermined time after thrombectomy.

[0036] Specifically, within 48 hours after the patient's EVT procedure, CT perfusion imaging and magnetic resonance diffusion-weighted imaging were acquired using standard clinical equipment according to the Digital Imaging and Communications in Medicine (DICOM) standard. Standard clinical equipment included Siemens and GE CT / MRI scanners. The dynamic scanning sequence for CT perfusion imaging had a scan time greater than 30 seconds, covering the entire brain, and an image resolution greater than 1 mm × 1 mm × 1 mm. For magnetic resonance diffusion-weighted imaging, the b-value was set to 1000 s / mm. 2 The image resolution is greater than 0.5mm × 0.5mm × 3mm. Furthermore, the acquired CT perfusion imaging and magnetic resonance diffusion-weighted imaging are stored in the PACS system.

[0037] In this approach, multimodal brain images are acquired after thrombectomy, overcoming the limitations of single-modal assessment and enabling the coordinated acquisition of tissue structure and hemodynamic information.

[0038] S102. Based on the signal intensity of pixels in multimodal brain images, divide the multimodal brain images to obtain infarct areas and non-infarct areas.

[0039] First, magnetic resonance diffusion-weighted imaging is divided based on the signal intensity of pixels in the imaging, thus obtaining the infarct area and non-infarct area in the magnetic resonance diffusion-weighted imaging.

[0040] Before region segmentation, the magnetic resonance diffusion-weighted imaging is preprocessed. Specifically, noise reduction, grayscale normalization, and artifact correction are performed on the magnetic resonance diffusion-weighted imaging.

[0041] In one possible implementation, the method for dividing the infarct area into the non-infarct area is the threshold method.

[0042] The signal intensity of each pixel in the standardized magnetic resonance diffusion-weighted imaging is extracted. The signal intensity of each pixel is compared with the signal intensity threshold. The region with a signal intensity greater than or equal to the signal intensity threshold is a high signal region, which is divided into an infarct region. The region with a signal intensity less than the signal intensity threshold is a low signal region, which is divided into a non-infarct region.

[0043] In one possible implementation, the division of infarct and non-infarct regions is achieved using a deep learning model. Specifically, a semantic segmentation model is used to perform pixel-level automatic segmentation of magnetic resonance diffusion-weighted imaging. For example, semantic segmentation models include UNet, FCN, DeepLab series, and SegNet. These models are pre-trained using manually labeled magnetic resonance diffusion-weighted imaging samples. The semantic segmentation model includes a connected input layer, encoder, bottleneck layer, decoder, and output layer, which are used for feature downsampling, feature fusion, feature uptesting, and pixel-level classification of the magnetic resonance diffusion-weighted imaging, respectively.

[0044] Specifically, for the defined infarct and non-infarct regions, the number of pixels within the infarct region is calculated to obtain the infarct region volume. The infarct region volume is the product of the number of pixels in the infarct region, the pixel length spacing, the pixel width spacing, and the scan layer thickness. The pixel spacing and layer thickness parameters are extracted from DICOM image metadata.

[0045] In this implementation, by comparing the pixel signal intensity thresholds of magnetic resonance diffusion-weighted imaging, infarct areas and non-infarct areas can be objectively identified, avoiding the bias of traditional subjective interpretation.

[0046] Furthermore, the region segmentation results of magnetic resonance diffusion-weighted imaging are mapped onto CT perfusion imaging.

[0047] Specifically, the image registration method is used to align CT perfusion imaging and magnetic resonance diffusion-weighted imaging. The infarct area in magnetic resonance diffusion-weighted imaging is mapped to CT perfusion imaging to obtain the infarct area in CT perfusion imaging, and the non-infarct area in magnetic resonance diffusion-weighted imaging is mapped to CT perfusion imaging to obtain the non-infarct area in CT perfusion imaging.

[0048] More specifically, a strategy combining rigid and flexible registration was employed. Rigid registration was performed on CT perfusion imaging and MRI diffusion-weighted imaging, using the skull anatomy as a reference to correct spatial positional discrepancies between the two images. Flexible registration was then performed based on the grayscale distribution characteristics of brain tissue to compensate for local deviations caused by slight shifts in scanning position and tissue. After registration, the corresponding infarct and non-infarct areas were simultaneously determined in CT perfusion imaging based on the infarct and non-infarct boundaries defined in the MRI diffusion-weighted imaging.

[0049] Understandably, this step outputs a binary mask map of CT perfusion imaging and marks the boundaries between infarcted and non-infarcted areas.

[0050] In this implementation, the infarct area and the non-infarct area are objectively divided in magnetic resonance diffusion-weighted imaging based on the comparison of pixel signal intensity thresholds. Then, the partitioning result is mapped to CT perfusion imaging through image registration, so as to achieve spatial alignment and anatomical partitioning of the two modal images, which can reduce the parameter extraction error caused by spatial misalignment of multimodal images.

[0051] S103. Determine the relative cerebral blood flow based on the infarct area and determine the maximum delay time based on the non-infarct area.

[0052] In one possible implementation, relative cerebral blood flow is determined based on the infarct area from CT perfusion imaging.

[0053] Understandably, the infarct area in CT perfusion imaging is located on one side of the brain. Therefore, in this step, the infarct area in CT perfusion imaging is mirrored to identify the target area within the non-infarct area in CT perfusion imaging on the other side of the brain.

[0054] Specifically, using the longitudinal fissure of the brain as the axis of symmetry, a region with the same volume and shape as the infarct area is selected at the corresponding position in the healthy hemisphere to obtain the target area.

[0055] Further, CT perfusion imaging data is processed, and the blood flow signal is inverted using the Arterial Input Function (AIF) and Venous Output Function (VOF) to calculate the first cerebral blood flow corresponding to each pixel in the infarct area and the second cerebral blood flow corresponding to each pixel in the target area. For each pixel in the infarct area, the ratio of the first cerebral blood flow in the infarct area to the second cerebral blood flow corresponding to the mirror pixel in the target area is calculated to obtain the relative cerebral blood flow of that pixel. Specifically, the relative cerebral blood flow is: rCBF=CBF1 / CBF2 In the formula, rCBF is the relative cerebral blood flow, CBF1 is the first cerebral blood flow, and CBF2 is the second cerebral blood flow.

[0056] Furthermore, the open-source software RAPID was used to statistically analyze the distribution of relative cerebral blood flow within the infarct area, and to calculate the mean, standard deviation, and percentiles.

[0057] In this implementation, the healthy side mirror region is used as a reference to calculate the relative cerebral blood flow for the infarct area. This can quantify the degree of perfusion loss and help eliminate the interference of confounding factors such as individual baseline blood flow and scanning parameters, providing an objective data basis for assessing the perfusion status of the infarct area.

[0058] In one possible implementation, the maximum delay time is determined based on the non-infarcted area of ​​CT perfusion imaging.

[0059] Specifically, CT perfusion imaging data is processed, and a time-density curve is constructed based on the change of contrast agent concentration over time. The time when the contrast agent concentration in the non-infarct area reaches its peak, i.e., the maximum delay time T, is then calculated using the time-density curve. max .

[0060] Furthermore, the presence of a low-perfusion zone can be determined within the non-infarct area based on the maximum delay time.

[0061] Specifically, a maximum delay time threshold is determined. Generally, the maximum delay time threshold is 6 seconds. The maximum delay time is compared with the maximum delay time threshold. When the maximum delay time is greater than the maximum delay time threshold, it is determined that low perfusion may exist, and the area where low perfusion may exist is classified as a low perfusion zone.

[0062] Furthermore, regional morphological identification was performed on low-irrigation areas.

[0063] Specifically, the low-perfusion area is divided into multiple grid regions using a gridding method. For each grid region, the eccentricity and the number of connected components are identified at the pixel level. When the eccentricity of the low-perfusion area is greater than the eccentricity threshold and the number of connected components is less than the number threshold, the region is determined to have a wedge-shaped distribution along the blood vessel. For example, the eccentricity threshold is 0.7 and the number threshold is 5.

[0064] Furthermore, the distribution histogram of maximum latency time is calculated using the ITK or SimpleITK library, and spatially connected components are analyzed.

[0065] In this implementation, low-perfusion areas are first screened for non-infarcted areas using the maximum delay time threshold. Then, quantitative indicators such as eccentricity and number of connected components are used to determine whether the low-perfusion areas are distributed in a wedge shape along the blood vessels, thereby improving the accuracy of identifying regional low perfusion in subsequent multimodal brain images.

[0066] S104. Combine relative cerebral blood flow and maximum delay time to obtain the recognition results in multimodal brain images.

[0067] Among them, the identification results in multimodal brain imaging include tissue hypoperfusion types.

[0068] In one possible implementation, a threshold-based method classifies multimodal brain images separately based on infarcted and non-infarcted areas.

[0069] Specifically, for the infarct area, the volume ratio of the area with relative cerebral blood flow less than the flow threshold to the infarct area is calculated. When the volume ratio is greater than the volume ratio threshold, the tissue hypoperfusion type in the multimodal brain imaging is determined to be microcirculation no-reflow.

[0070] For example, when there is an area with rCBF < 40% within the infarct area, and the proportion of such area is greater than 10%, it is judged as microcirculation no-reflow. Here, microcirculation no-reflow refers to hypoperfusion at the brain tissue level caused by microvascular failure.

[0071] For non-infarcted areas, when there are hypoperfusion regions within the non-infarcted area, and the region morphology is wedge-shaped and distributed along the blood vessels, the tissue hypoperfusion type in multimodal brain imaging is determined to be regional hypoperfusion.

[0072] For example, when T exists in the non-infarct region max A hypoperfusion region with a duration of >6 seconds, exhibiting a wedge shape and distribution along the blood vessel, and characterized by an eccentricity >0.7 and a number of connected components <5, is considered a regional hypoperfusion. Regional hypoperfusion is defined as delayed perfusion caused by distal emboli.

[0073] When the tissue hypoperfusion type in multimodal brain imaging is determined to include both microcirculatory no-reflow and regional hypoperfusion, the tissue hypoperfusion type in multimodal brain imaging is determined to be a mixed phenotype.

[0074] In one possible implementation, decision trees are used to classify tissue hypoperfusion types in multimodal brain imaging.

[0075] Furthermore, it outputs multimodal brain images showing tissue hypoperfusion types, perfusion volumes, and spatial distribution maps.

[0076] Specifically, when hypoperfusion is present in multimodal brain imaging, the corresponding hypoperfusion volume is determined based on the pixels of the hypoperfused region, and a corresponding spatial distribution visualization is generated. Finally, a structured report is generated and output based on the tissue hypoperfusion type, perfusion volume, and spatial distribution map. The output format is DICOM compatible and supports integration into clinical workstations. The structured report indicates the corresponding hypoperfusion volume, mean rCBF, and T... max Peak values, etc. For example, the visualization is a color mask overlaid on the original image, with infarct areas in red and low-perfusion areas in blue.

[0077] This implementation employs an automated classification method based on threshold parameters, which can objectively distinguish between the two phenotypes of tissue hypoperfusion in multimodal brain imaging after thrombectomy, avoiding subjective variations based on personal experience and visual judgment. Furthermore, it considers the possibility of coexistence of the two features, further improving the accuracy of multimodal brain imaging.

[0078] In summary, this application integrates multimodal brain imaging, performs spatial matching and threshold analysis, objectively distinguishes between two mechanisms, and avoids subjective variations based on personal experience and visual judgment. It can accurately identify whether hypoperfusion occurs within or outside the infarct area, overcomes the limitations of the traditional eTICI scoring system, and provides a standardized, mechanism-specific assessment tool to help predict clinical prognosis and guide targeted interventions and treatments. Examples include thrombolytic therapy for distal emboli and neuroprotective therapy for microcirculatory disturbances. Therefore, this application can guide early intervention and treatment after EVT in acute ischemic stroke, reduce the proportion of poor prognoses, and provide reliable imaging biomarkers for clinical research.

[0079] To achieve the above embodiments, this disclosure also proposes a device for recognizing brain images after thrombectomy.

[0080] Figure 2 This is a schematic diagram of a device for recognizing brain images after thrombectomy, provided as an embodiment of this disclosure. This device can be implemented by software and / or hardware, and is generally integrated into a computing device. Figure 2 As shown, the brain imaging recognition device after thrombectomy includes: The acquisition module 201 is used to acquire multimodal brain images of the patient after thrombectomy.

[0081] The segmentation module 202 is used to segment multimodal brain images based on the signal intensity of pixels in multimodal brain images to obtain infarct areas and non-infarct areas.

[0082] Analysis module 203 is used to determine relative cerebral blood flow based on the infarct area and to determine the maximum delay time based on the non-infarct area.

[0083] The identification module 204 is used to determine the identification results in multimodal brain images by combining relative cerebral blood flow and maximum delay time; wherein the identification results include information on tissue hypoperfusion type.

[0084] In one possible implementation, the partitioning module 202 includes: The first identification submodule is used to identify the signal intensity of each pixel in magnetic resonance diffusion-weighted imaging.

[0085] The segmentation submodule is used to compare the signal strength of each pixel with the signal strength threshold, classify high signal areas with signal strength greater than or equal to the signal strength threshold as infarct areas, and classify low signal areas with signal strength less than the signal strength threshold as non-infarct areas.

[0086] In one possible implementation, the partitioning module 202 further includes: The alignment submodule is used to align CT perfusion imaging and magnetic resonance diffusion-weighted imaging using image registration methods.

[0087] The mapping submodule is used to determine the corresponding infarct and non-infarct areas in CT perfusion imaging based on the infarct and non-infarct areas in magnetic resonance diffusion-weighted imaging.

[0088] In one possible implementation, the analysis module 203 includes: The first determination submodule is used to determine the target area within the non-infarct area of ​​CT perfusion imaging; wherein the target area is a mirror image of the infarct area.

[0089] The first calculation submodule is used to obtain the first cerebral blood flow corresponding to each pixel in the infarct area and the second cerebral blood flow corresponding to each pixel in the target area based on CT perfusion imaging.

[0090] The second calculation submodule is used to calculate the ratio of the first cerebral blood flow to the second cerebral blood flow corresponding to the mirror pixel in the target area for each pixel in the infarct area, so as to obtain the relative cerebral blood flow.

[0091] In one possible implementation, the identification module 203 includes: The first analysis submodule is used to calculate the volume ratio of the region with relative cerebral blood flow less than the flow threshold to the infarct region. When the volume ratio is greater than the volume ratio threshold, the tissue hypoperfusion type in the multimodal brain imaging is determined to be microcirculation no-reflow.

[0092] The second analysis submodule is used to compare the maximum delay time with the maximum delay time threshold for non-infarct areas. When there is a region with a maximum delay time greater than the maximum delay time threshold and the region morphology meets the morphological requirements, the tissue hypoperfusion type in the multimodal brain images is determined to be regional hypoperfusion.

[0093] The third analysis submodule is used to determine the tissue hypoperfusion type in multimodal brain images as a mixed phenotype when the tissue hypoperfusion type in multimodal brain images includes both microcirculation no-reflow and regional hypoperfusion.

[0094] In one possible implementation, the second analysis submodule includes: The division unit is used to divide the region where the maximum delay time is greater than the maximum delay time threshold into a low-perfusion zone.

[0095] The identification unit is used to identify the eccentricity and number of connected components in the low-perfusion zone.

[0096] The judgment unit is used to determine the region morphology as wedge-shaped along the blood vessel when the eccentricity of the low perfusion zone is greater than the eccentricity threshold and the number of connected components is less than the quantity threshold.

[0097] The determination unit is used to identify regional hypoperfusion in multimodal brain imaging when the region morphology is wedge-shaped and distributed along blood vessels.

[0098] The brain image recognition device after thrombectomy provided in this embodiment can execute the brain image recognition method after thrombectomy provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of the method.

[0099] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program / instructions, which, when executed by a processor, implements the method for recognizing brain images after thrombectomy as described in the above embodiments.

[0100] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of the present disclosure.

[0101] The following is a detailed reference. Figure 3 The diagram illustrates a structural schematic suitable for implementing the computing device 300 in the embodiments of this disclosure. The computing device 300 in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The computing device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0102] like Figure 3 As shown, the computing device 300 may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 302 or a program loaded from memory 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the computing device 300. The processor 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0103] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows computing device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3A computing device 300 with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0104] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 309, or installed from a memory 308, or installed from a ROM 302. When the computer program is executed by the processor 301, it performs the functions defined in the method for recognizing brain images after thrombectomy according to embodiments of this disclosure.

[0105] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0106] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0107] The aforementioned computer-readable medium may be included in the aforementioned computing device; or it may exist independently and not assembled into the computing device.

[0108] The aforementioned computer-readable medium carries one or more programs, which, when executed by the computing device, cause the computing device to perform the aforementioned method for recognizing brain images after thrombectomy.

[0109] The computing device can be programmed with computer program code for performing the operations of this disclosure in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0110] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0111] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0112] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0113] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0114] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0115] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0116] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for recognizing brain images after thrombectomy, characterized in that, The method includes: Obtain multimodal brain images of patients after thrombectomy; The multimodal brain images are divided based on the signal intensity of pixels in the multimodal brain images to obtain infarct areas and non-infarct areas; The relative cerebral blood flow is determined based on the infarct area, and the maximum delay time is determined based on the non-infarct area; The recognition results in the multimodal brain images are obtained by combining the relative cerebral blood flow and the maximum delay time; wherein, the recognition results include information on the type of tissue hypoperfusion.

2. The method for recognizing brain images after thrombectomy according to claim 1, characterized in that, The multimodal brain imaging includes CT perfusion imaging and magnetic resonance diffusion-weighted imaging within a preset time after the patient's thrombectomy.

3. The method for recognizing brain images after thrombectomy according to claim 2, characterized in that, The step of dividing the multimodal brain image based on the signal intensity of pixels in the multimodal brain image to obtain infarct areas and non-infarct areas includes: Identify the signal intensity of each pixel in the magnetic resonance diffusion-weighted imaging; The signal intensity of each pixel is compared with a signal intensity threshold. High signal regions where the signal intensity is greater than or equal to the signal intensity threshold are classified as infarct regions, and low signal regions where the signal intensity is less than the signal intensity threshold are classified as non-infarct regions.

4. The method for recognizing brain images after thrombectomy according to claim 3, characterized in that, The method further includes: The CT perfusion imaging and the magnetic resonance diffusion-weighted imaging were aligned using an image registration method. Based on the infarcted area and the non-infarcted area in the magnetic resonance diffusion-weighted imaging, the corresponding infarcted area and non-infarcted area are determined in the CT perfusion imaging.

5. The method for recognizing brain images after thrombectomy according to claim 2, characterized in that, Determining the relative cerebral blood flow based on the infarct area includes: A target region is determined within the non-infarcted area of ​​the CT perfusion imaging; wherein the target region is a mirror image of the infarcted area; The first cerebral blood flow corresponding to each pixel in the infarct area and the second cerebral blood flow corresponding to each pixel in the target area are obtained based on the CT perfusion imaging. For each pixel in the infarct region, the ratio of the first cerebral blood flow to the second cerebral blood flow corresponding to the mirror pixel in the target region is calculated to obtain the relative cerebral blood flow.

6. The method for recognizing brain images after thrombectomy according to claim 5, characterized in that, The step of determining the recognition result in the multimodal brain image by combining the relative cerebral blood flow and the maximum delay time includes: For the infarcted area, the volume ratio of the region with relative cerebral blood flow less than the flow threshold to the infarcted area is calculated. When the volume ratio is greater than the volume ratio threshold, the tissue hypoperfusion type in the multimodal brain image is determined to be microcirculation no-reflow. For the non-infarcted area, the maximum delay time is compared with the maximum delay time threshold. When there is a region where the maximum delay time is greater than the maximum delay time threshold and the region morphology meets the morphological requirements, the tissue hypoperfusion type in the multimodal brain image is determined to be regional hypoperfusion. When the tissue hypoperfusion type in the multimodal brain images is determined to include both microcirculation no-reflow and regional hypoperfusion, the tissue hypoperfusion type in the multimodal brain images is determined to be a mixed phenotype.

7. The method for recognizing brain images after thrombectomy according to claim 6, characterized in that, When a region exists where the maximum delay time is greater than the maximum delay time threshold, and the region's morphology meets the morphological requirements, the tissue hypoperfusion type in the multimodal brain imaging is determined to be regional hypoperfusion, including: The region where the maximum delay time is greater than the maximum delay time threshold is classified as a low-perfusion zone; Identify the eccentricity and number of connected components in the low-perfusion zone; When the eccentricity of the low-perfusion zone is greater than the eccentricity threshold and the number of connected components is less than the quantity threshold, the morphology of the region is determined to be wedge-shaped and distributed along the blood vessel. When the region is wedge-shaped and distributed along blood vessels, the tissue hypoperfusion type in the multimodal brain image is determined to be the regional hypoperfusion.

8. A device for recognizing brain images after thrombectomy, characterized in that, The device includes: The acquisition module is used to acquire multimodal brain images of patients after thrombectomy. The segmentation module is used to segment the multimodal brain image based on the signal intensity of pixels in the multimodal brain image to obtain infarct areas and non-infarct areas; The analysis module is used to determine the relative cerebral blood flow based on the infarct area and to determine the maximum delay time based on the non-infarct area. The identification module is used to determine the identification result in the multimodal brain image by combining the relative cerebral blood flow and the maximum delay time; wherein the identification result includes tissue hypoperfusion type information.

9. A computing device, characterized in that, The computing device includes: a processor; a memory for storing executable instructions of the processor; the processor for reading the executable instructions from the memory and executing the instructions to implement the method for recognizing brain images after thrombectomy as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method for recognizing brain images after thrombectomy as described in any one of claims 1 to 7.

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