Functional magnetic resonance image decoding method based on explicit brain region interaction modeling

By employing explicit brain region interaction modeling, and training the network using information interaction networks between brain regions and outside the brain, combined with supervised and unsupervised methods, the image decoding and reconstruction quality of functional magnetic resonance imaging (fMRI) signals was improved. This solved the problem of poor brain region information interaction processing in existing technologies and achieved higher quality image decoding and reconstruction.

CN117497144BActive Publication Date: 2026-05-29SICHUAN UNIV
0 Cites 0 Cited by

Patent Information

Application Number
CN202311667279.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-12-06
Publication Date
2026-05-29
Estimated Expiration
2043-12-06

AI Technical Summary

Technical Problem

Existing functional magnetic resonance imaging (fMRI) signal data reconstruction models are not effective in processing information interactions between brain regions and outside the brain, resulting in room for improvement in the quality of stimulus image decoding and reconstruction.

Method used

We employ a method based on explicit brain region interaction modeling, which utilizes information interaction networks within and between brain regions. By combining supervised and unsupervised training methods, we improve the quality of image decoding and reconstruction using paired and unpaired labeled data.

Benefits of technology

By using explicit brain region interaction modeling, magnetic resonance imaging (MRI) data features are effectively extracted, improving the quality of image decoding and reconstruction, and providing an effective image decoding and reconstruction model for visual function exploration and brain-computer interface applications.

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

Abstract

The application discloses a functional magnetic resonance image decoding method based on explicit brain region interaction modeling. First, an encoder network is trained in a supervised manner through image-magnetic resonance signals, then a decoder network is trained in a supervised manner through magnetic resonance-image signals, and meanwhile, a decoding-encoding structure and an encoding-decoding structure formed by stacking the encoder and the decoder network are trained in a self-supervised manner using the magnetic resonance signals and the image signals, then an information interaction network containing brain region and inter-brain region information interaction modeling is trained in a combination of the supervised and unsupervised manners, finally, the training is completed using the images and the magnetic resonance signals provided by a data set, the magnetic resonance signals to be decoded are input into the information interaction network and the decoder network to obtain a final image decoding reconstruction result. The method can effectively perform image decoding reconstruction based on functional magnetic resonance signals, and provides an effective image decoding reconstruction model for various visual function exploration research based on magnetic resonance signals.
Need to check novelty before this filing date? Find Prior Art