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Abstract #2371

High-fidelity Ultra-fast Diffusion Tensor Imaging in Stroke Patients Using Transfer Learning

Yi Jing1,2,3, Ziyu Li2, Yang Yu4,5, Jinglin Zhou4,5, Zihan Li1, Jialan Zheng1, Hongjia Yang1, Mingxuan Liu1, Wenchuan Wu2, Qiyuan Tian1, and Jie Lu4,5
1School of Biomedical Engineering, Tsinghua University, Beijing, China, 2Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 3Department of Computer Science and Technology, Tsinghua University, Beijing, China, 4Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China, 5Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China

Synopsis

Keywords: Stroke, DWI/DTI/DKI

Motivation: Deep learning shows potential for accelerating diffusion tensor imaging (DTI) without compromising image quality, but its feasibility has not been comprehensively evaluated for clinical applications.

Goal(s): Achieve fast, high-quality DTI for clinical use with minimal training data.

Approach: We adopt a U-Net to map low-SNR 6-direction diffusion data to high-SNR data with more diffusion directions. The U-Net is pretrained on UKB data and fine-tuned with limited clinical data from stroke patients.

Results: Evaluations on clinical data reveal our model effectively reduces scan-time from 9.7 minutes to 1 minute while producing high-SNR diffusion images, accurate diffusion metrics, and high-quality tractography.

Impact: Our work supports and promotes the feasibility of deep learning approach to benefit the clinical adoption of DTI for diagnosis and/or pre-surgical planning in scenarios where the scan time is extremely limited (e.g., for stroke patients).

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