Improved readout-segmented EPI using deep learning reconstruction

Motivation: We explore the potential of deep learning reconstruction (DLR) to overcome challenges for readout-segmented EPI (rs-EPI) , ultimately leading to more efficient and high-quality diffusion-weighted imaging (DWI). Goal(s): We evaluate DLR's applicability for rs-EPI, aiming to improve image quality, reduce scan durations, and expand rs-EPI's clinical utility. Approach: We adapted the successful DLR method used in single-shot EPI (ss-EPI) to rs-EPI, conducting experiments for head and prostate diffusion imaging. Results: Our study demonstrates that DLR can improve image quality and reduce scan times in rs-EPI DWI, promising more efficient clinical imaging and potential applications in diverse diffusion imaging scenarios. Impact: The successful implementation of DLR in readout-segmented EPI DWI promises accelerated, high-quality diagnostics, directly benefiting clinicians and patients. Furthermore, DLR's potential for diverse diffusion imaging applications opens new research horizons, enhancing the field of MR imaging.