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Abstract #3401

Virtual Cell Type Atlas of Mouse Brain from MRI Signatures using Attention Res-UNet

Yiqi Shen1, Yao Shen1, Haoan Xu1, Tianshu Zheng1, Yongquan Huang1, Sihui Li1, Qinfeng Zhu1, Zuozhen Cao1, Zhiyong Zhao1,2, and Dan Wu1
1Zhejiang University, Hangzhou, China, 2Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China

Synopsis

Keywords: Analysis/Processing, Simulations, Magnetic resonance histology;Virtual cell maps

Motivation: Magnetic resonance imaging (MRI) has shown promise in illustrating tissues’ microstructure. However, its potential on predicting cell distribution remains unexplored.

Goal(s): Our objective is to predict three-dimensional representations of different cell types across the entire mouse brain via deep learning on multi-contrast MRI.

Approach: We trained an Attention Res-UNet model using a template-based MRI dataset and an atlas-based cell distribution dataset.

Results: Our findings demonstrated that multi-contrast MRI with Attention Res-UNet model could predict composition of various cell types across whole mouse brain. Our predictions aligned well with typical cell distribution patterns and regional characteristic.

Impact: We demonstrated MRI-based deep learning could predict three-dimensional representations of different cell types at whole-brain level, which were consistent with typical cell distribution patterns and regional characters. Our findings highlight the potential of MRI for predicting three-dimensional cell atlas.

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Keywords