Mohammed Saleh, Mayur Virarkar, Sanaz Javadi, Manoj Mathew, Sai Swarupa Reddy Vulasala, Jong Bum Son, Jia Sun, Ersin Bayram, Xinzeng Wang, Jingfei Ma, Janio Szklaruk, Priya Bhosale
OBJECTIVES: Evaluate deep learning (DL) to improve the image quality of the PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction technique) for 3 T magnetic resonance imaging of the female pelvis. METHODS: Three radiologists prospectively and independently compared non-DL and DL PROPELLER sequences from 20 patients with a history of gynecologic malignancy. Sequences with different noise reduction factors (DL 25%, DL 50%, and DL 75%) were blindly reviewed and scored based on artifacts, noise, relative sharpness, and overall image quality...
June 12, 2023: Journal of Computer Assisted Tomography