Improved Clinical Diffusion Weighted Imaging by Combining Deep Learning Reconstruction, Partial Fourier, and Super Resolution

Diffusion weighted imaging (DWI) has found widespread use in daily clinical routine but can still be limited by long acquisition times and low spatial resolution. In this work, combining deep learning-based k-space to image reconstruction with super resolution processing tailored to support partial Fourier acquisitions is demonstrated to efficiently mitigate these obstacles. The approach is shown for various applications, including liver, breast, prostate, and brain DWI at 0.55T, 1.5T, and 3T.