Abstract #4340

Diffusion Weighted Imaging using PROPELLER Acquisition and a Deep Learning based Reconstruction

Xinzeng Wang¹, Daniel Litwiller², Ali Ersoz³, Marc Lebel⁴, Sagar Mandava⁵, Lloyd Estkowski³, Arnaud Guidon⁶, Ann Shimakawa⁷, and Ersin Bayram¹

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, Waukesha, WI, United States, ⁴Global MR Applications & Workflow, GE Healthcare, Calgary, AB, Canada, ⁵Global MR Applications & Workflow, GE Healthcare, Tucson, AZ, United States, ⁶Global MR Applications & Workflow, GE Healthcare, Boston, MA, United States, ⁷Global MR Applications & Workflow, GE Healthcare, Menlo Park, CA, United States

PROPELLER DWI, a FSE based DWI method, is increasingly used to reduce susceptibility artifacts and motion artifacts. Multi-shot Echo-Planar diffusion method also can reduce susceptibility artifacts, but PROPELLER DWI shows better image quality where susceptibility artifacts are most problematic, such as in skull base, head-neck and pelvis. However, the acquisition time is often longer compared to ms-DW-EPI, therefore SNR is usually compromised to reduce acquisition time. In this work, we evaluated a deep-learning based reconstruction method (DL Recon PROP) intended to improve image quality and ADC measurements by reducing the noise and artifacts without increasing acquisition time.

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