Abstract #4782

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

Thomas Benkert¹, Elisabeth Weiland¹, Simon Arberet², Majd Helo¹, Fasil Gadjimuradov^1,3, Karl Engelhard⁴, Gregor Thoermer¹, and Dominik Nickel¹

¹MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, ²Digital Technology & Innovation, Siemens Medical Solutions USA, Princeton, NJ, United States, ³Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, ⁴Institute of Radiology, Martha-Maria Hospital, Nuremberg, Germany

Synopsis

Keywords: Diffusion/other diffusion imaging techniques, Translational StudiesDiffusion 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.

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