Abstract #3000

T2-weighted Pelvic MR Imaging Using PROPELLER with Deep Learning Reconstruction for Improved Motion Robustness

Mohammed Saleh¹, Sanaz Javadi¹, Manoj Mathew², Jong Bum Son³, Jia Sun⁴, Ersin Bayram⁵, Xinzeng Wang⁵, Jingfei Ma³, Janio Szklaruk¹, and Priya Bhosale¹

¹Radiology, MD Anderson Cancer Center, Houston, TX, United States, ²Radiology, Stanford University, Stanford, CA, United States, ³Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States, ⁴Biostatistics, MD Anderson Cancer Center, Houston, TX, United States, ⁵Global MR Applications and Workflow, GE Healthcare, Houston, TX, United States

In oncologic MRI, sagittal T2 weighted images (WI) are usually acquired to assess gynecologic malignancy. Motion artifacts may render pathology difficult to detect. PROPELLER has shown promising results to reduce motion-related artifacts. Our work shows that DL-Recon can be combined with PROPELLER and further help reduce noise and improve the overall image quality for T2WI. The combination of PROPELLER (Non-DL) and DL reconstruction could be synergistic in improving image quality.

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