Abstract #2779

Deep Learning-based Joint Image Reconstruction and Biomarker Estimation for Highly-accelerated Multi-shot Intravoxel Incoherent Motion DWI

Chenglang Yuan¹, Shihui Chen^1,2, Liyuan Liang^1,2, Xiaorui Xu³, Hailin Xiong¹, Yi Li¹, Tianbaige Liu¹, Wing Yat Alpha Cheung¹, Edward S. Hui^4,5,6, and Hing-Chiu Chang^1,2

¹Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China, ²Multi-Scale Medical Robotics Center, Hong Kong, China, ³Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China, ⁴Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China, ⁵Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China, ⁶CU Lab for AI in Radiology (CLAIR), The Chinese University of Hong Kong, Hong Kong, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, IVIM, 3D-DWI, multi-shot, DL

Motivation: The low clinical feasibility of time-consuming multi-shot interleaved acquisitions for IVIM-DWI.

Goal(s): To achieve effective and efficient image reconstruction and biomarker estimation from highly-accelerated four-shot IVIM-DWI data in the brain.

Approach: An end-to-end deep learning (DL)-based joint image reconstruction and biomarker estimation framework was proposed for highly-accelerated IVIM-DWI. It consists of a fully-supervised multi-b-value joint extraction and reconstruction module, and a self-supervised physics-informed estimation module.

Results: Our framework permits high-quality reconstruction of IVIM-DWI and estimation of biomarker maps with minimized residual artifacts, improved geometric fidelity and a significant reduction of acquisition time, surpassing other conventional reconstruction methods.

Impact: Our proposed DL-based technique is capable of precisely reconstructing IVIM-DWI and producing IVIM-related biomarker maps within a clinically feasible acquisition time, potentially improving the quantitative evaluation and analysis of IVIM-DWI-based assessment of cerebrovascular disease.

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