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Abstract #5121

Rapid Quantitative Susceptibility Mapping for Intracranial Hemorrhage using Deep Learning-based 2.5D Diffusion Models

Zhuang Xiong1, Yang Gao1,2, Wei Jiang1, Ken Butcher3,4, Alan H. Wilman 4, and Hongfu Sun1,5
1University of Queensland, Brisbane, Australia, 2Central South University, Changsha, China, 3University of New South Wales, Sydney, Australia, 4University of Alberta, Edmonton, AB, Canada, 5University of Newcastle, Newcastle, Australia

Synopsis

Keywords: Image Reconstruction, Acquisition Methods, QSM

Motivation: EPI-based Quantitative Susceptibility Mapping (QSM) benefits intracranial hemorrhage (ICH) imaging with reduced scan times for patients scan but faces limitations in image quality.

Goal(s): a generalized deep learning QSM method that is robust to motion artifact, and capable of image quality boost from EPI acquisitions, while preserving the accuracy of ICH susceptibility quantification.

Approach: A novel deep learning-based QSM method using 2.5D Diffusion Models (QSMDiff) is developed, with synthetic hemorrhagic susceptibility features extending the capability to ICH patients.

Results: A deep learning QSM method robust to motion artifacts, enhancing EPI image quality while maintaining accurate ICH susceptibility quantification.

Impact: This reliability across imaging conditions highlights QSMDiff’s potential as a versatile and accurate tool for clinical susceptibility mapping for ICH patients.

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