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Abstract #4357

PROPELLER Diffusion-Weighted Imaging of the Prostate with Deep-Learning Reconstruction

Xinzeng Wang1, Ersin Bayram1, Daniel Litwiller2, Tetsuya Wakayama3, Alan B McMillan4, Lloyd Estowski5, Ty A Cashen5, and Ali Pirasteh4
1Global MR Applications & Workflow, GE Healthcare, Houston, TX, United States, 2Global MR Applications & Workflow, GE Healthcare, New York, NY, United States, 3Global MR Applications & Workflow, GE Healthcare, Hino, Japan, 4Radiology, University of Wisconsin Madison, Madison, WI, United States, 5Global MR Applications & Workflow, GE Healthcare, Waukesha, WI, United States

While echo-planar diffusion-weighted imaging (EP-DWI) is the main sequence for cancer detection in the prostate peripheral zone, it is susceptible to signal loss and distortion due to B0-field inhomogeneities secondary to a variety of causes, including rectal gas or metal hardware in the pelvis. We were able to demonstrate that a spin-echo based DWI sequence with radial k-space sampling (PROPELLER) can overcome such artifacts and the addition of a deep-learning reconstruction algorithm can overcome the poor signal-to-noise (SNR) profile of the PROPELLER-DWI, overall generating images with minimal-to-no appreciable artifact and favorable SNR.

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