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Taxonomy: IM/TH- Image Registration: CT

MO-H345-IePD-F1-2A Patient-Specific Approach for Estimating the Voxel-Level Uncertainty Distribution of Deformable Image Registration in Adaptive Radiotherapy
S Meyer*, S Alam, L Kuo, Y Hu, L Cervino, P Zhang, Memorial Sloan Kettering Cancer Center, New York, NY
MO-H345-IePD-F1-3A Novel Edge Gradient Distance Metric for Automated Evaluation of Deformable Image Registration Quality
Y Xu1*, J Ford1, J Williamson3, N Dogan1, (1) University of Miami, Miami, FL, (2) Washington University, Richmond, VA
PO-GePV-M-34Clinical Implementation of the Pancreatic Cancer Fast CT-CBCT Registration Network Model
E LoCastro1*, J Hong2, Y Hu3, X Han4, A Apte5, G Mageras6, (1) Memorial Sloan Kettering Cancer Center, New York, New York, (2) MSKCC, New York, NY, (3) Memorial Sloan Kettering Cancer Center, New York, NY, (4) Unc Chapel Hill, ,,(5) Memorial Sloan-Kettering Cancer Center, Maywood, NJ, (6) Memorial Sloan-Kettering Cancer Center, New York, NY
PO-GePV-M-183A Hybrid Framework to Quantitatively Assess Deformable Image Registration Accuracy with a Complete Implementation of TG-132 Based Validation
L Naumann*, B Stiehl, M Lauria, P Boyle, D Low, A Santhanam, UCLA, Los Angeles, CA
SU-F-206-8Quantifying the Impact of Deformable Dose Accumulation Using Voxel-Level Dose-Response Analysis for Non-Small Cell Lung Cancer
Y He*, G Cazoulat, C Wu, L Almodovar-Abreu, E Mccollum, B Rigaud, P Balter, J Pollard-Larkin, D Rhee, L Court, Z Liao, R Mohan, K Brock, The University of Texas MD Anderson Cancer Center, Houston, TX
WE-C1030-IePD-F2-5Deformable CT Image Registration Using Unsupervised Deep Learning Networks
Y Lei, Y Fu, Z Tian, T Wang, J Zhang, X Dai, J Zhou, J Roper, M McDonald, D Yu, J Bradley, T Liu, X Yang*, Emory University, Atlanta, GA