Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 5, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

RISNet: A variable multi-modal image feature fusion adversarial neural network for generating specific dMRI images.

Guolan Wang¹, Xiaohong Xue¹, Yifei Chen²

¹College of Computer and Information Engineering, Shanxi Technology and Business University, Taiyuan, China.

|October 9, 2025

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

ACCELERATE-BASSO: early experiences and emerging best practices for ontology development in behavioral and social science research.

Journal of biomedical semantics·2026

Same author

Transformer-DAPT: AI-based dynamic assessment of ischemic and bleeding risks in patients on DAPT following PCI.

NPJ digital medicine·2026

Same author

Prevotella copri impairs bone mass via osteoclast activation in mice.

Molecular and cellular biochemistry·2026

Same author

Hydrogels loaded with different substances for treating heart failure: a promising therapy.

Frontiers in cardiovascular medicine·2026

Same author

Construction and validation of a prognostic model for seroma after breast cancer surgery: a dual-center retrospective cohort study.

Frontiers in oncology·2026

Same author

Respiratory efficiency and thermoregulatory responses of L-citrulline-supplemented broiler chickens under acute and chronic stress conditions.

Frontiers in physiology·2026

Researchers developed RISNet, a novel neural network, to generate lower b-value diffusion MRI images for macaques. This method enhances computational neuroscience accuracy by addressing data imbalance in macaque brain imaging datasets.

Area of Science:

Neuroimaging
Computational Neuroscience
Medical Image Analysis

Background:

Diffusion magnetic resonance imaging (dMRI) b-values influence image contrast and calculation accuracy.
Imbalance in lower and higher b-value macaque dMRI data hinders computational neuroscience.
Existing generative adversarial networks struggle with multi-center, small-sample macaque brain datasets.

Purpose of the Study:

To address the scarcity of lower b-value dMRI data in macaques.
To improve the accuracy of computational neuroscience analyses using macaque brain imaging.
To develop a robust medical image conversion method for macaque dMRI.

Main Methods:

Proposed RISNet, a variable multi-modal image feature fusion adversarial neural network.

Related Experiment Videos

Last Updated: May 5, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Introduced Rapid Insertion Structural (RIS) to enhance model generalization by fusing multi-modal features.

Utilized T1 and higher b-value brain images as inputs to generate lower b-value images.

Main Results:

Achieved an average improvement of 1.8211 in PSNR and 0.0111 in SSIM compared to existing methods.
Demonstrated sound visual effects in qualitative observations.
Showcased strong generalization ability in Diffusion Tensor Imaging (DTI) estimation.

Conclusions:

RISNet effectively solves the dMRI brain image conversion problem in macaques.
The method enhances the quality and utility of macaque dMRI data.
Provides strong support for future neuroscience research utilizing macaque models.