A method for detecting activation of mouse brain nuclei based on manganese-enhanced magnetic resonance imaging

By using the PLKA-nnUNet model and the SyNRACC registration method, the standardization and automation issues of MEMRI image processing were resolved, the accuracy of skull removal and image registration was improved, and stable and objective detection of mouse brain nucleus activation was achieved.

CN121482042BActive Publication Date: 2026-03-27JIANGSU INST OF METROLOGY
2 Cites 0 Cited by

Patent Information

Application Number
CN202610018699.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-01-08
Publication Date
2026-03-27
Estimated Expiration
2046-01-08

AI Technical Summary

Technical Problem

The existing MEMRI image processing workflow lacks standardization and automation, especially in the steps of skull removal and image registration, which is not robust enough. This leads to a decrease in the sensitivity and consistency of intergroup statistical analysis, and the non-uniformity of data intensity affects ROI quantification and voxel-level statistics.

Method used

The PLKA-nnUNet model is used for image segmentation and SyNRACC registration. The N4 algorithm is used to correct the bias field to achieve image standardization and automated processing. Parallel large kernel convolution and attention mechanism are used to improve the accuracy of skull removal. Normalized cross-correlation metric is used to improve the accuracy of image registration.

Benefits of technology

It achieves a high degree of standardization and automation in MEMRI image processing, improves processing efficiency and result repeatability, enhances skull removal accuracy and image registration applicability, and stably extracts neural activity information.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The application discloses a method for detecting mouse brain nucleus activation based on manganese-enhanced magnetic resonance imaging, and belongs to the field of image processing, which comprises the following steps: converting the manganese-enhanced magnetic resonance image of the mouse head collected into NIFTI format, keeping the direction and voxel size of the image consistent with the standard mouse brain atlas template; performing bias field correction on the image; loading the PLKA- nnUNet model, performing image segmentation on the image, outputting a binary brain mask, and extracting individual mouse brain images from the image after bias field correction; performing image registration and intensity normalization; calculating the mean relaxation rate of the four hippocampal subregions of the mouse brain R1 map for each individual mouse brain image after registration, which is used for quantitatively comparing the activation of the mouse brain; and performing statistical testing at the voxel level on the registered and normalized individual mouse brain images to identify the brain regions with intensity differences between different experimental conditions. The method reduces the dependence on professional operation.
Need to check novelty before this filing date? Find Prior Art