A multimodal magnetic resonance visualization tremor treatment target positioning method and device and UDP image acquisition method

Abstract

The application discloses a multimodal magnetic resonance visualization tremor treatment target positioning method and device and a UDP image acquisition method. The multimodal magnetic resonance visualization tremor treatment target positioning method comprises the following steps: acquiring a preoperative UDP image of a patient to be positioned; acquiring a preoperative UDN image of the patient to be positioned; acquiring a postoperative best lesion location heat map LS of the patient to be positioned; generating a superimposed display image according to the preoperative UDP image of the patient to be positioned and the preoperative UDN image of the patient to be positioned; superimposing the postoperative best lesion location heat map LS of the patient to be positioned in the superimposed display image, so as to acquire a final view. The application can enhance the clear boundary of a treatment target and an axial position pyramidal tract and a medial lemniscus structure, and facilitate accurate delineation of a temperature monitoring volume (Temperature Monitoring Volume) to avoid treatment side effects.

Description

Technical Field

[0001] This application relates to the field of image processing technology for tremor patients, specifically to a multimodal magnetic resonance imaging (MRI) visualization method for tremor treatment target localization, a multimodal MRI visualization device for tremor treatment target localization, and a UDP image acquisition method. Background Technology

[0002] Epidemiological surveys show that the prevalence of essential tremor is 4.6% and the prevalence of Parkinson's disease is 1.7% among people aged 65 and above in my country. Based on 2021 population figures, it is estimated that there are over 8.7 million patients with essential tremor and over 3 million patients with Parkinson's disease aged 65 and above in China alone. Among these more than 10 million patients, those with a history of disease for more than 10 years often present with drug-resistant tremor that severely impacts their daily lives, requiring surgical treatment. Magnetic resonance-guided focused ultra-sound (MRgFUS) is a non-invasive treatment method that has rapidly gained popularity globally in recent years. It has broad application prospects in stereotactic functional neurosurgery and is one of the main methods for precise treatment of drug-resistant tremor. The absence of surgical incisions and precise treatment range are the main advantages of MRgFUS, but it also places higher demands on the visualization and localization accuracy of the treatment target. Traditional lesioning surgery involves craniotomy to insert lesioning electrodes. It can predict treatment side effects through macro-stimulation, and the theoretical lesioning temperature can reach up to 100°C, which can produce a larger lesion area. However, MRgFUS treatment cannot perform electrical stimulation testing, and the lesioning temperature generally does not exceed 60°C. The area of ​​a single lesion is significantly reduced, so the accuracy requirements for the positioning of the core treatment target are higher.

[0003] Currently, there are two main methods for targeting tremor therapy: ① Traditional empirical targets based on the anterior-posterior commissure (AC-PC) and the anatomical boundaries of the third ventricle, such as the ventral intermediate nucleus (Vim) of the thalamus. Figure 3 ② Tracking the imaging targets of the dentate-rubro-thalamic tract (DRT) using preoperative diffusion tensor imaging (DTI) fiber tracts. Figure 4Currently, traditional empirical target selection or magnetic resonance imaging (MRI) localization methods struggle to pinpoint the optimal treatment target to a very small area, easily leading to symptom recurrence. Furthermore, they cannot accurately demonstrate the relationship between the treatment target and surrounding important structures such as the pyramidal tract and medial lemniscus, undoubtedly increasing the likelihood of side effects. Therefore, the slow accumulation of traditional localization experience has become a major hurdle hindering the clinical application of MRgFUS, an advanced technology. Summary of the Invention

[0004] The purpose of this invention is to provide a multimodal magnetic resonance imaging method for tremor treatment target localization, which at least solves one of the above-mentioned technical problems.

[0005] One aspect of the present invention provides a method for locating therapeutic targets for tremor using multimodal magnetic resonance imaging (MRI), the method comprising:

[0006] Acquire preoperative UDP images of the patient to be located;

[0007] Acquire preoperative UDN images of the patient to be located;

[0008] Obtain a thermal image (LS) of the optimal postoperative location of the ablation lesion in the patient to be located.

[0009] An overlay display image is generated based on the patient's preoperative UDP image and the patient's preoperative UDN image.

[0010] The final view is obtained by overlaying a thermal image (LS) of the optimal postoperative lesion location of the patient to be located onto the overlaid display image.

[0011] Optionally, acquiring the preoperative UDP image of the patient to be located includes:

[0012] Acquire image data of the patient to be located, including the patient's preoperative INV1 image, preoperative INV2 image, preoperative UNI image, preoperative DWI image, and preoperative DICOM format image after processing.

[0013] The preoperative DWI images of the patient to be located are processed to obtain the preoperative DTI images of the patient to be located corresponding to the preoperative DWI images of the patient to be located.

[0014] The patient's preoperative UNI image and preoperative DTI image are fused to generate the patient's preoperative UDP image.

[0015] Optionally, processing the preoperative DWI image of the patient to be located to obtain the preoperative DTI image of the patient to be located corresponding to the preoperative DWI image of the patient to be located includes:

[0016] Preoperative DWI images of patients were corrected for magnetic susceptibility distortion, eddy current distortion, and diffusion tensor fitting to generate preoperative DTI images. By using different tensor anisotropy intensities, the pyramidal tract (PT), medial lemniscus (ML), and dentate thalamus tract (DRT) could be enhanced.

[0017] Optionally, fusing the preoperative UNI image of the patient to be located with the preoperative DTI image of the patient to be located to generate the preoperative UDP image of the patient includes:

[0018] The tensor mean, tensor variance, overall size of the tensor, tensor anisotropy intensity, and tensor modality of the preoperative DTI images of the patient are calculated based on the feature values ​​of the acquired preoperative DTI images.

[0019] Median filtering is applied to the anisotropy intensity of the tensor.

[0020] The square root of the anisotropy intensity of the filtered tensor is multiplied by the UNI image and then added to the UNI image to obtain the preoperative UDP image of the patient to be located.

[0021] Optionally, acquiring the preoperative UDN image of the patient to be located includes:

[0022] Obtain the INV2-ET template and UDP-ET template;

[0023] The first transformation matrix is ​​obtained by nonlinearly registering the INV2-ET template to the preoperative INV2 image of the patient to be located.

[0024] The first transformation matrix is ​​applied to the UDP-ET template to obtain the preoperative UDN image of the patient to be located.

[0025] Optionally, obtaining the optimal postoperative lesion location thermal image LS of the patient to be located includes:

[0026] Obtain a template of the MRI image of the location of the lesion for postoperative follow-up examination;

[0027] Obtain the postoperative tremor score improvement rate;

[0028] Based on the postoperative tremor score improvement rate and the MRI image template of the lesion location during postoperative follow-up, the optimal postoperative lesion location heat map (LS) of the patient to be located was obtained.

[0029] Optionally, the overlay display image includes:

[0030] First-layer UDP image: Color mode: CoolShade1, Opacity: 50%, Window width: 1000, Grayscale: 2200, Threshold: -1000~5000;

[0031] Second layer UDN image: Color mode: CoolTint2, Opacity: 50%, Window width: 600, Grayscale: 300, Threshold: -1000~5000.

[0032] This application also provides a multimodal magnetic resonance imaging (MRI) visualization device for tremor treatment target localization, the multimodal MRI visualization device for tremor treatment target localization comprising:

[0033] A preoperative UDP image acquisition module for acquiring preoperative UDP images of the patient to be located.

[0034] A preoperative UDN image acquisition module for patients, wherein the preoperative UDN image acquisition module is used to acquire preoperative UDN images of the patients to be located;

[0035] The LS acquisition module is used to acquire the optimal postoperative lesion location heat map (LS) of the patient to be located.

[0036] An overlay display image acquisition module is used to generate an overlay display image based on the preoperative UDP image and the preoperative UDN image of the patient to be located.

[0037] The final view acquisition module is used to overlay a heat map (LS) of the optimal postoperative lesion location of the patient to be located onto the overlaid display image, thereby acquiring the final view.

[0038] This application also provides a UDP image acquisition method, which uses the method described above for multimodal magnetic resonance visualization tremor treatment target localization.

[0039] Beneficial effects

[0040] This application's multimodal magnetic resonance visualization method for tremor treatment target localization can limit the optimal treatment target from the relatively large thalamic Vim nucleus and the long-traversed dentate-red thalamic tract (DRT) to a precisely optimized target with a diameter of 2 mm, and objectively enhance its display on the fused image, significantly improving the visualization capability of thalamic anatomy. Overlaying postoperative follow-up lesion thermograms (LS) facilitates the surgeon's treatment planning, avoiding significant differences in target selection due to subjective settings of fiber tract tracking parameters or the surgeon's personal experience, reducing subjectivity in the selection of regions of interest and arbitrariness in fiber tract threshold settings. Simultaneously, it enhances the clear boundary between the treatment target and the axial pyramidal tract and medial lemniscus structures, facilitating accurate delineation of the temperature monitoring volume to avoid treatment side effects. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating a method for locating therapeutic targets using multimodal magnetic resonance imaging (MRI) according to an embodiment of this application.

[0042] Figure 2 It is used to implement Figure 1 A schematic diagram of an electronic device for a multimodal magnetic resonance visualization method for tremor treatment target localization.

[0043] Figure 3 This refers to traditional empirical targets in existing technologies that use brain atlases to locate thalamic subregions (Vim).

[0044] Figure 4 To track imaging targets of the dentate thalamus tract (DRT) using existing preoperative magnetic resonance DTI images;

[0045] Figure 5 This is a flowchart illustrating the process of creating multimodal magnetic resonance visualization positioning images in this embodiment;

[0046] Figure 6 This is a schematic diagram illustrating the creation of multimodal magnetic resonance visualization positioning images in this embodiment;

[0047] Figure 7 This is a comparison diagram of the Moral thalamus stereotactic atlas used for localization in this embodiment;

[0048] Figure 8 The brain template and postoperative follow-up thermal image (LS) of the lesion were generated by combining the localization of this embodiment with the average of 100 tremor patients in our center.

[0049] Figure 9 This is a screenshot showing the temperature detection area in the magnetic wave knife planning system used in this embodiment. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, embodiments of this application. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0051] like Figure 1 and Figure 5 The multimodal magnetic resonance imaging method for tremor therapy target localization includes:

[0052] Acquire preoperative UDP images of the patient to be located;

[0053] Acquire preoperative UDN images of the patient to be located;

[0054] Obtain a thermal image (LS) of the optimal postoperative location of the ablation lesion in the patient to be located.

[0055] An overlay display image is generated based on the patient's preoperative UDP image and the patient's preoperative UDN image.

[0056] The final view is obtained by overlaying a thermal image (LS) of the optimal postoperative lesion location of the patient to be located onto the overlaid display image.

[0057] In this embodiment, acquiring the preoperative UDP image of the patient to be located includes:

[0058] Acquire image data of the patient to be located, including the patient's preoperative INV1 image, preoperative INV2 image, preoperative UNI image, preoperative DWI image, and preoperative DICOM format image after processing.

[0059] The preoperative DWI images of the patient to be located are processed to obtain the preoperative DTI images of the patient to be located corresponding to the preoperative DWI images of the patient to be located.

[0060] The patient's preoperative UNI image and preoperative DTI image are fused to generate the patient's preoperative UDP image.

[0061] In this embodiment, the process of processing the preoperative DWI image of the patient to be located to obtain the preoperative DTI image of the patient to be located corresponding to the preoperative DWI image of the patient to be located includes:

[0062] Preoperative DWI images of patients were corrected for magnetic susceptibility distortion, eddy current distortion, and diffusion tensor fitting to generate preoperative DTI images. By using different tensor anisotropy intensities, the pyramidal tract (PT), medial lemniscus (ML), and dentate thalamus tract (DRT) could be enhanced.

[0063] In this embodiment, fusing the preoperative UNI image and the preoperative DTI image of the patient to be located to generate the preoperative UDP image of the patient includes:

[0064] The tensor mean, tensor variance, overall size of the tensor, tensor anisotropy intensity, and tensor modality of the preoperative DTI images of the patient are calculated based on the feature values ​​of the acquired preoperative DTI images.

[0065] Median filtering is applied to the anisotropy intensity of the tensor.

[0066] The square root of the anisotropy intensity of the filtered tensor is multiplied by the UNI image and then added to the UNI image to obtain the preoperative UDP image of the patient to be located.

[0067] In this embodiment, the INV2-ET template and UDP-ET template are obtained through the following method:

[0068] Multimodal magnetic resonance images of multiple patients are acquired (for example, images of 100 patients are acquired in this embodiment; it is understood that the patients to be located mentioned above may also be included). Specifically, images of each patient are acquired containing the following:

[0069] Including MP2RAGE (INV1, INV2, UNI) and diffusion-weighted imaging (DWI, at least 60 directions, including two B0 in opposite PE directions), all DICOM format images (MP2RAGE, diffusion-weighted imaging) are converted to NIFTI format. Using the INV2 magnetic resonance sequence as a registration template, all other images (INV1 images, UNI images, DWI images) are registered with it, so that all images are in the same volume space.

[0070] The multimodal magnetic resonance images of each patient were processed as follows:

[0071] DWI images are subjected to magnetic susceptibility distortion correction, eddy current distortion correction, and diffusion tensor fitting to generate DTI images. The pyramidal tract (PT), medial lemniscus (ML), and dentate thalamus tract (DRT) can be enhanced by different tensor anisotropy intensities.

[0072] By fusing UNI images with DTI images, enhanced structural images (UDP) are obtained, making both thalamic subregions and DRT visible.

[0073] In this embodiment, the UNI image and the DTI image can be fused using the following method:

[0074] It is known that MP2RAGE scanning simultaneously generates two sets of images INV1 and INV2 with different contrasts, and UNI can be calculated (in this embodiment, UNI is calculated from INV1 and INV2, while INV1 and INV2 are directly scanned by the NMR machine).

[0075]

[0076] Given the three eigenvalues ​​(λ1, λ2, λ3) of DTI, the following can be calculated:

[0077] Tensor mean:

[0078]

[0079] Tensor variance:

[0080]

[0081] Overall size of the tensor:

[0082] Anisotropy strength of tensors:

[0083] Median filtering is applied to R², then the square root is taken, multiplied by the UNI image, and fused. Finally, the result is added back to the UNI image and fused again to obtain the UDP image for each patient.

[0084]

[0085] In this embodiment, each tremor patient has a UDP image and an INV2 image. Taking 100 tremor patients as an example, the INV2 images of the 100 tremor patients are registered with the 152 images respectively to obtain the transformation matrix of the 100 tremor patients (one INV2 image corresponds to one transformation matrix). INV2-ET templates are obtained according to the transformation matrix of each tremor patient and the INV2 image of each tremor patient. UDP-ET templates are obtained according to each obtained transformation matrix and the UDP image of each tremor patient.

[0086] In this embodiment, acquiring the preoperative UDN image of the patient to be located includes:

[0087] Obtain the INV2-ET template and UDP-ET template;

[0088] The first transformation matrix is ​​obtained by nonlinearly registering the INV2-ET template to the preoperative INV2 image of the patient to be located.

[0089] The first transformation matrix is ​​applied to the UDP-ET template to obtain the preoperative UDN image of the patient to be located.

[0090] In this embodiment, obtaining the optimal postoperative lesion location thermal image (LS) of the patient to be located includes:

[0091] Obtain a template of the MRI image of the location of the lesion for postoperative follow-up examination;

[0092] Obtain the postoperative tremor score improvement rate;

[0093] Based on the postoperative tremor score improvement rate and the MRI image template of the lesion location during postoperative follow-up, the optimal postoperative lesion location heat map (LS) of the patient to be located was obtained.

[0094] Specifically, the MRI image template of the lesion location for postoperative follow-up is obtained through the following methods:

[0095] The MRI images of the lesion locations from the postoperative follow-up examinations of 100 patients used to generate the template (understandably, the MRI images here can be any of the following images: INV1 image, pre-INV2 image, UNI image, DWI image, T1 image, T2 image) were nonlinearly registered to the MNI152NLin2009bAsym template to obtain the MRI image template of the lesion locations from the postoperative follow-up examinations of the patients.

[0096] In this embodiment, the application constructs a target visualization interface by fusing different enhanced structural images (UDP, UDN). The UDP and UDN images are displayed in layers, and the window width, window level, and transparency of each layer are adjusted to check whether the UDP image processing results deviate too much from the average results of 100 patients. The specific process is as follows:

[0097] First-layer UDP image: Color mode: CoolShade1, Opacity: 50%, Window width: 1000, Grayscale: 2200, Threshold: -1000~5000;

[0098] Second layer UDN image: Color mode: CoolTint2, Opacity: 50%, Window width: 600, Grayscale: 300, Threshold: -1000~5000.

[0099] This application further includes: re-slicing all images (i.e., the first layer, the second layer, or the first layer, the second layer, and the postoperative optimal lesion location heatmap LS) along the AC-PC coordinate system (the first coordinate system), editing the fiducial, setting three points AC, PC, and MID respectively, setting MID as the spatial origin, and finding important fiber bundles (DRT, PT, ML) on the side to be treated in the horizontal axial images near the AC-PC plane.

[0100] In this embodiment, the application further includes the following:

[0101] The center of the DRT is located in the anterior-posterior joint (AC-PC) plane of each UDP image. Using this point as the center, the cone tract and DRT contours are delineated in the axial and coronal planes. The DRT course in the coronal and sagittal planes is observed using the axial center point. The images are simultaneously adjusted in three planes to align with the geometric center of the DRT (e.g., ...). Figure 7 (As shown).

[0102] Select the DRT center projection on three planes and determine its geometric center (centroid) as the target point. Figure 5 This can be used for preoperative visualization and target localization in transcranial focused ultrasound (MRgFUS). An LS image is overlaid on the UDP image. Figure 8 This tool assists clinicians lacking localization experience in target selection, improves the success rate of target localization, and is used to set treatment target coordinates in the MRgFUS planning system.

[0103] In this embodiment, the application further includes: the multimodal magnetic resonance visualization image produced by the present invention can limit the optimal treatment target from the original relatively large thalamic Vim nucleus and the long-traversed dentate red nucleus thalamus tract (DRT) to a precisely optimized target with a diameter of 2 mm, and objectively enhance the display on the fused image, significantly improving the visualization ability of thalamic anatomy; superimposed brain template (UDP-ET) based on the average preoperative localization of 100 tremor patients and thermal images (LS) of lesions from 100 postoperative follow-up examinations (…). Figure 8 This facilitates treatment planning by the surgeon, avoiding significant differences in target selection due to subjective settings of fiber tract tracking parameters or the surgeon's personal experience. It reduces subjectivity in selecting regions of interest and arbitrariness in setting fiber tract thresholds. Simultaneously, it enhances the clarity of the boundaries between the treatment target and the axial pyramidal tract and medial lemniscus structures, facilitating accurate delineation of the temperature monitoring volume to avoid treatment side effects. Figure 9 Specifically, see Figure 9 , Figure 9 The UDP axial image on the left-hand main interface clearly displays the high-signal region of the cone beam, allowing clinicians to easily use a temperature monitoring volume tool to manually and continuously outline the pink temperature monitoring area layer by layer under direct vision for real-time temperature monitoring during ultrasound treatment. Figure 9 The pink curve in the middle displays the average temperature fluctuation within the monitoring area during a single ultrasound treatment. This alerts the doctor to manually stop the treatment in time if the average temperature within the cone tract exceeds 50°C, which may cause irreversible damage, thus avoiding postoperative side effects such as limb weakness.

[0104] In this embodiment, due to the long multimodal MRI scan time and the different positions of the patient's head in the image space, the reconstructed brain positions after obtaining the image sequences are also different. Therefore, it is necessary to fuse and register all modal images. Specifically, a rigid registration algorithm can be applied during registration to register the scan results of different modalities and different slice thicknesses / resolutions of the tremor patient. The registration template is the preoperative INV2 sequence (the image contrast is similar to that of traditional T1-MPRAGE). All image modalities obtained after registration and fusion should be in the same standard space as INV2 (since the three sets of images, INV1, INV2, and UNI, are generated simultaneously in one scan and are already in the same space, no registration is required between the three).

[0105] Distortions caused by magnetic susceptibility and eddy currents were corrected in 60-directional DWI images. A series of preprocessing steps, including correction of magnetic susceptibility distortion, correction of eddy current distortion, and fitting of diffusion tensors, yielded a set of diffusion tensor images (DTI), comprising fractional anisotropy (FA), mean diffusion tensor, three eigenvalues ​​(λ1–3), and three eigenvectors (vector 1–3). Since all the above results were generated from the same DWI images in a single batch, registration could be completed simply by rigidly registering the FA images to the INV2 image space and then substituting the remaining images into the same transformation matrix.

[0106] In this embodiment, the UNI image and DTI image are fused to generate a fused image (UDP). Specific steps are described above in the invention description and will not be repeated here. The UDP-ET template is nonlinearly registered to the individual patient space to obtain the UDN image. Both are placed on two separate layers to check whether the UDP image processing result deviates too much from the average result of 100 patients. The image display color mode, contrast, and transparency are adjusted to ensure that ventricular anatomical landmarks (AC, PC, third ventricle) and important fiber tracts (DRT, PT, ML) are visible (e.g., Figure 6 (As shown).

[0107] In this embodiment, the coronal and sagittal DRT distortion ranges are observed using the axial center point of the DRT, and the images are simultaneously adjusted in three planes to align with the geometric center of the DRT (e.g., ...). Figure 7 As shown), an LS graph (e.g., LS graph) is overlaid on the UDP graph. Figure 8 As shown in the figure, it assists clinicians who lack localization experience in target selection and is used to set the coordinates of treatment targets in the MRgFUS planning system.

[0108] In a preferred embodiment, due to the common existence of individual patient differences and bilateral thalamic structural asymmetry, the optimal treatment target is often not at the AC-PC level. In the past, surgeons generally started treatment 1-2 mm above the AC-PC level and gradually tried treatment downwards. However, there are countless cases where the optimal treatment target is below this level. Since MRgFUS treatments have an energy decay effect after multiple treatments, and the maximum treatment energy has a design upper limit, the present invention can correctly select the target location at the first time, avoiding wasting the few treatment opportunities for patients with low SDR.

[0109] This application also provides a multimodal magnetic resonance imaging (MRI) visualization device for tremor treatment target localization, which includes a preoperative UDP image acquisition module, a preoperative UDN image acquisition module, an LS acquisition module, an overlay display image acquisition module, and a final view acquisition module.

[0110] The preoperative UDP image acquisition module is used to acquire preoperative UDP images of the patient to be located.

[0111] The preoperative UDN image acquisition module is used to acquire the preoperative UDN image of the patient to be located.

[0112] The LS acquisition module is used to acquire the optimal postoperative lesion location heatmap (LS) of the patient to be located.

[0113] The overlay display image acquisition module is used to generate an overlay display image based on the patient's preoperative UDP image and the patient's preoperative UDN image.

[0114] The final view acquisition module is used to overlay a heat map (LS) of the optimal postoperative lesion location of the patient to be located onto the overlaid display image, thereby obtaining the final view.

[0115] This application also provides a UDP image acquisition method, which uses the method described above for multimodal magnetic resonance visualization tremor treatment target localization.

[0116] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and will not be repeated here.

[0117] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the above-described multimodal magnetic resonance visualization tremor treatment target localization method.

[0118] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, enables the implementation of the above-described multimodal magnetic resonance visualization tremor treatment target localization method.

[0119] Figure 2 This is an exemplary structural diagram of an electronic device capable of implementing the multimodal magnetic resonance visualization tremor treatment target localization method provided in one embodiment of this application.

[0120] like Figure 2 As shown, the electronic device includes an input device 501, an input interface 502, a central processing unit 503, a memory 504, an output interface 505, and an output device 506. The input interface 502, central processing unit 503, memory 504, and output interface 505 are interconnected via a bus 507. The input device 501 and output device 506 are connected to the bus 507 via the input interface 502 and output interface 505, respectively, and thus connected to other components of the electronic device. Specifically, the input device 501 receives input information from the outside and transmits it to the central processing unit 503 via the input interface 502. The central processing unit 503 processes the input information based on computer-executable instructions stored in the memory 504 to generate output information, temporarily or permanently storing the output information in the memory 504, and then transmitting the output information to the output device 506 via the output interface 505. The output device 506 outputs the output information to the outside of the electronic device for user use.

[0121] In other words, Figure 2 The illustrated electronic device may also be implemented as including: a memory storing computer-executable instructions; and one or more processors, which can be coupled when executing the computer-executable instructions. Figure 1 A method for locating therapeutic targets using multimodal magnetic resonance imaging (MRI) is described.

[0122] In one embodiment, Figure 2 The electronic device shown can be implemented as including: a memory 504 configured to store executable program code; and one or more processors configured to run the executable program code stored in the memory 504 to perform the multimodal magnetic resonance visualization tremor treatment target localization method in the above embodiments.

[0123] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0124] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0125] Computer-readable media include both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, DVD or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0126] 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 application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutively marked 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 a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or the overall flowchart, 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.

[0127] In this embodiment, the processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0128] Memory can be used to store computer programs and / or modules. The processor implements various functions of the device / terminal equipment by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area can store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). In addition, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0129] In this embodiment, if the modules / units integrated into the device / terminal equipment are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the scope of protection of this application should be determined by the scope defined in the claims of this application.

[0130] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] Furthermore, it is clear that the word "comprising" does not exclude other units or steps. Multiple units, modules, or devices recited in the apparatus claims may also be implemented by a single unit or overall apparatus via software or hardware.

[0132] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A multimodal magnetic resonance imaging (MRI) visualization method for tremor treatment target localization, characterized in that, The multimodal magnetic resonance visualization method for tremor treatment target localization includes: Acquire the preoperative UDP image of the patient to be located; acquiring the preoperative UDP image of the patient to be located includes: Acquire image data of the patient to be located, including the patient's preoperative INV1 image, preoperative INV2 image, preoperative UNI image, preoperative DWI image, and preoperative DICOM format image after processing. The preoperative DWI images of the patient to be located are processed to obtain the preoperative DTI images of the patient to be located corresponding to the preoperative DWI images of the patient to be located. The patient's preoperative UNI image and the patient's preoperative DTI image are fused to generate the patient's preoperative UDP image. The step of fusing the preoperative UNI image and the preoperative DTI image of the patient to be located to generate the preoperative UDP image of the patient includes: The tensor mean, tensor variance, overall tensor size, and tensor anisotropy intensity of the patient's preoperative DTI image are calculated based on three feature values ​​of the obtained preoperative DTI image. The anisotropy intensity of the tensor is processed by median filtering. The square root of the filtered anisotropy intensity is multiplied by the UNI image and then added to the UNI image to obtain the preoperative UDP image of the patient to be located. Acquire the preoperative UDN image of the patient to be located; the acquisition of the preoperative UDN image of the patient to be located includes: Obtain the INV2-ET template and the UDP-ET template; obtaining the INV2-ET template and the UDP-ET template includes: The INV2 images of multiple tremor patients were registered with the 152 images to obtain the transformation matrix of each tremor patient. The INV2-ET template was obtained based on the transformation matrix and the INV2 image of each tremor patient. The UDP-ET template was obtained based on the obtained transformation matrix and the UDP image of each tremor patient. The first transformation matrix is ​​obtained by nonlinearly registering the INV2-ET template to the preoperative INV2 image of the patient to be located. The first transformation matrix is ​​applied to the UDP-ET template to obtain the patient's preoperative UDN image. Obtaining a thermal image (LS) of the optimal postoperative lesion location in the patient to be located; the acquisition of the thermal image (LS) of the optimal postoperative lesion location in the patient to be located includes: Obtain a template of the MRI image of the location of the lesion for postoperative follow-up examination; Obtain the postoperative tremor score improvement rate; Based on the postoperative tremor score improvement rate and the magnetic resonance imaging template of the lesion location during the postoperative follow-up of the patient, a heat map (LS) of the optimal postoperative lesion location of the patient to be located was obtained. An overlay display image is generated based on the patient's preoperative UDP image and the patient's preoperative UDN image. The optimal postoperative lesion location heat map LS of the patient to be located is superimposed on the overlay display image to obtain the final view; After obtaining the final view, the multimodal magnetic resonance visualization method for tremor treatment target localization further includes: The final view is re-segmented along the AC-PC coordinate system to label the important fiber bundles on the side to be treated; Select the DRT center projection on the axial plane, the crown plane, and the sagittal plane, and determine its geometric center as the target point.

2. The multimodal magnetic resonance visualization tremor treatment target localization method as described in claim 1, characterized in that, The process of processing the preoperative DWI image of the patient to be located to obtain the corresponding preoperative DTI image of the patient to be located includes: Preoperative DWI images of patients were subjected to magnetic susceptibility distortion correction, eddy current distortion correction, and diffusion tensor fitting to generate preoperative DTI images of patients.

3. The multimodal magnetic resonance visualization tremor treatment target localization method as described in claim 2, characterized in that, The overlay display image includes: First layer UDP image: Color mode: CoolShade1, Opacity: 50%, Window width: 1000, Grayscale: 2200, Threshold: -1000~5000; Second layer UDN image: Color mode: CoolTint2, Opacity: 50%, Window width: 600, Grayscale: 300, Threshold: -1000~5000.

4. A multimodal magnetic resonance visualization tremor treatment target localization device, characterized in that, The multimodal magnetic resonance visualization tremor treatment target localization device includes: A preoperative UDP image acquisition module for patients, wherein the preoperative UDP image acquisition module is used to acquire preoperative UDP images of the patient to be located; the acquisition of preoperative UDP images of the patient to be located includes: Acquire image data of the patient to be located, including the patient's preoperative INV1 image, preoperative INV2 image, preoperative UNI image, preoperative DWI image, and preoperative DICOM format image after processing. The preoperative DWI images of the patient to be located are processed to obtain the preoperative DTI images of the patient to be located corresponding to the preoperative DWI images of the patient to be located. The patient's preoperative UNI image and the patient's preoperative DTI image are fused to generate the patient's preoperative UDP image. The step of fusing the preoperative UNI image and the preoperative DTI image of the patient to be located to generate the preoperative UDP image of the patient includes: The tensor mean, tensor variance, overall tensor size, and tensor anisotropy intensity of the patient's preoperative DTI image are calculated based on three feature values ​​of the obtained preoperative DTI image. The anisotropy intensity of the tensor is processed by median filtering. The square root of the filtered anisotropy intensity is multiplied by the UNI image and then added to the UNI image to obtain the preoperative UDP image of the patient to be located. A preoperative UDN image acquisition module for patients, wherein the preoperative UDN image acquisition module is used to acquire preoperative UDN images of the patients to be located; the acquisition of preoperative UDN images of the patients to be located includes: Obtain the INV2-ET template and the UDP-ET template; obtaining the INV2-ET template and the UDP-ET template includes: The INV2 images of multiple tremor patients were registered with the 152 images to obtain the transformation matrix of each tremor patient. The INV2-ET template was obtained based on the transformation matrix and the INV2 image of each tremor patient. The UDP-ET template was obtained based on the obtained transformation matrix and the UDP image of each tremor patient. The first transformation matrix is ​​obtained by nonlinearly registering the INV2-ET template to the preoperative INV2 image of the patient to be located. The first transformation matrix is ​​applied to the UDP-ET template to obtain the patient's preoperative UDN image. The LS acquisition module is used to acquire a thermal image (LS) of the optimal postoperative lesion location of the patient to be located; acquiring the thermal image (LS) of the optimal postoperative lesion location of the patient to be located includes: Obtain a template of the MRI image of the location of the lesion for postoperative follow-up examination; Obtain the postoperative tremor score improvement rate; Based on the postoperative tremor score improvement rate and the magnetic resonance imaging template of the lesion location during the postoperative follow-up of the patient, a heat map (LS) of the optimal postoperative lesion location of the patient to be located was obtained. An overlay display image acquisition module is used to generate an overlay display image based on the preoperative UDP image and the preoperative UDN image of the patient to be located. The final view acquisition module is used to overlay a heat map LS of the optimal postoperative lesion location of the patient to be located onto the overlay display image, thereby acquiring the final view. After obtaining the final view, the device can also re-segment the brain image along the AC-PC coordinate system and label the important fiber bundles on the side to be treated. Select the DRT center projection on the axial plane, the crown plane, and the sagittal plane, and determine its geometric center as the target point.

Images