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Abstract #3238

DTI with Minimal Data: Image Translation Based Distortion Correction and FA Map Generation for Clinical Efficiency

Ya Cui1, Hongjia Qi1, Zengji Zhang1, Haiqing Zhang1, Siyu Yuan1, Bingyang Cai1, Jiwei Li1, Miao Zhang2, Zhenkui Wang3, Li Tong3, and Jie Luo1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 3Shanghai United Imaging Healthcare, Shanghai, China

Synopsis

Keywords: Analysis/Processing, Data Processing

Motivation: Current DTI processing requires around 30 directions per shell to ensure data quality, which is difficult to obtain in certain vulnerable patient groups.

Goal(s): To reduce number of DWI directions needed in clinical study.

Approach: This study proposes a pipeline with translation-based registration method to correct both distortions and motion artifacts, followed by a generative adversarial network (GAN) to generate fractional anisotropy map, leveraging high quality data from HCP, with fine-tuning for applications.

Results: We validate this approach in a subset of HCP DTI, healthy volunteer, and on epilepsy patient data, using only 6 DWI images while preserving lesion details.

Impact: Success of the proposed pipeline will enable much shorter DTI acquisition time for patients who cannot stay still throughout a multidirectional DTI scans, which holds great potential to becoming a promising tool for clinical applications.

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