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

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.