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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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