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|PO-GePV-M-186||Quantitative Evaluation and Feasibility Study of Using AI Denoising Technique to Minimize Image Dose of 2.5MV Beam|
H Kuo1*, S Lim1, S Lin2, J Sillanpaa1, L Cervino1, (1) Memorial Sloan Kettering Cancer Center, New York, NY (2) Norwalk Hospital, Norwalk, CT
|SU-E-207-1||Noise2noise Deep Learning Based Acceleration for MRI Echo-Planar Imaging|
L Qin1*, C Lindsay2, A Konik1, G Young3, (1) Dana-Farber Cancer Institute, Boston, MA, (2) University Of Massachusetts Chan Medical School, Worcester, MA (3) Brigham And Women's Hospital, Boston, MA
|TH-D-207-6||Texture Transformer Super-Resolution (TTSR) for Patient CT Images|
S Zhou1*, L Yu2, M Jin1, (1) University of Texas at Arlington, Arlington, TX, (2) Mayo Clinic, Rochester, MN
|WE-A-201-4||Joint K-B Space Image Reconstruction and Data Fitting for Diffusion-Weighted Magnetic Resonance Imaging|
J Deng*, X Jia, The University of Texas Southwestern Medical Ctr, Garland, TX