Abstract #1121

Deep Learning-Based High b-Value Image Synthesis: Application to SANDI Microstructure Map Prediction

Rencheng Zheng¹, Yunheng Li¹, Hui Zhang¹, Boyu Zhang², Xueqin Xia¹, Zihao Tang³, Chenyu Wang³, YingHua Chu⁴, Hui Zhang⁵, Chengyan Wang⁶, Hao Li¹, and He Wang¹

¹Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, ²Department of Radiology, Shanghai Fourth People's Hospital Affliated to Tongji University School of Medicine, Shanghai, China, ³Brain and Mind Centre, The University of Sydney, Sydney, Australia, ⁴MR Research Collaboration Team, Siemens Healthineers, Shanghai, China, ⁵Centre for Medical Image Computing and Dept of Computer Science, University College London, London, United Kingdom, ⁶Human Phenome Institute, Fudan University, Shanghai, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: High b-value data is crucial for Soma and Neurite Density Imaging (SANDI). However, the acquisition of high b-value images is highly dependent on the gradient performance of the equipment, making it not applicable to most clinical MRI systems.

Goal(s): This study presented a deep learning solution to synthesize high b-value images based on multi-phase initial low b-value images.

Approach: We proposed a multi-task learning image synthesis framework that combined generative adversarial loss and SANDI-sensitive loss.

Results: The proposed model achieved high-performance high b-value image synthesis and provided benefits for downstream SANDI map prediction.

Impact: This study explores the feasibility of replacing real high b-value images with synthesized images generated by deep learning models, which holds promise for transfer to other MRI systems with lower gradient performance, thereby expanding the application scope of SANDI.

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