Improved Image Quality with Deep Learning-Based Image Reconstruction for Multi-shot Diffusion-Weighted Imaging of the Prostate

Diffusion-weighted imaging (DWI) is a key component of identifying prostate tumors on MRI. Multi-shot DWI techniques (e.g. MUSE) have been shown to enable high resolution prostate DWI and, compared to single-shot DWI, reduce distortion artifacts due to rectal gas and hip implants at the expense of increased scan time. In this study, we evaluated a CNN-based deep learning (DL) image reconstruction method for MUSE. Our results indicate that for high b-value images the DL-based reconstruction improved perceived image quality even with half the original NEX, suggesting potential scan time reduction using DL.