Deep-learning reconstruction of under-sampled readout-segmented echo-planar diffusion-weighted images at multiple b values

Motivation: Readout-segmented (rs-) EPI typically yields improved DWI image quality compared to single-shot EPI, but it is time-consuming. This presently precludes its clinical use for multiple b-value diffusion modeling like IVIM. Goal(s): To accelerate rs-EPI image acquisition without compromising quality using convolutional neural networks (CNNs) trained on high-resolution and under-sampled low-resolution images. Approach: Three CNNs were trained and tested on synthetic and in vivo DWI datasets. The CNNs were tasked with reconstructing high-resolution images at multiple b values, and IVIM parameter maps were estimated for comparison. Results: The CNNs reconstructed high-resolution DWI images and IVIM parameter maps of comparable quality to the fully-sampled data. Impact: This approach could substantially reduce the scan times of readout-segmented EPI when used for multiple b-value diffusion modeling. It therefore offers the potential for improved image quality for IVIM imaging, at scan times comparable to conventional single-shot EPI acquisition.