Intravoxel incoherent motion magnetic resonance imaging reconstruction from highly under-sampled diffusion-weighted PROPELLER acquisition data via physics-informed residual feedback unrolled network

Abstract

Objective: The acquisition of diffusion-weighted images for intravoxel incoherent motion (IVIM) imaging is time-consumed. This work aims to accelerate the scan by highly under-sampling diffusion-weighted turbo spin echo PROPELLER (DW-TSE-PROPELLER) scheme and develop a reconstruction method for accurate IVIM parameter mapping from the under-sampled data.
Approach: The proposed under-sampling DW-TSE-PROPELLER scheme for IVIM imaging is that a few blades per b-value are acquired and rotated along b-value dimension to cover high-frequency information. A physics-informed residual feedback unrolled network (PIRFU-Net) was proposed to directly estimate distortion-free and artifact-free IVIM parametric maps (i.e., the perfusion-free diffusion coefficient D and the perfusion fraction f) from highly under-sampled DW-TSE-PROPELLER data. PIRFU-Net used unrolled convolution network to explore data redundancy in the k-q space to remove under-sampling artifacts. An empirical IVIM physical constraint was incorporated into the network to ensure that the signal evolution curves along the b-value follow bi-exponential decay. The residual between realistic and estimated measurements was fed into the network to refine the parametric maps. Meanwhile, the use of synthetic training data eliminated the need for actually acquired DW-TSE-PROPELLER data.
Main results: The experimental results show that the DW-TSE-PROPELLER acquisition was six times faster than full k-space coverage PROPELLER acquisition and within a clinically acceptable time. Compared with the state-of-the-art methods, the distortion-free D and f maps estimated by PIRFU-Net were more accurate and had better-preserved tissue boundaries on simulated human brain and realistic phantom/rat brain/human brain data.
Significance: Our proposed method greatly accelerates the IVIM imaging. It is capable of directly and simultaneously reconstructing distortion-free, artifact-free, and accurate D and f maps from six-fold under-sampled DW-TSE-PROPELLER data.