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Keywords: Image Artifacts
| PO-GePV-I-26 | Comparison Between EPI DWI and PROPELLER DWI in MR Brain Imaging P Mavroidis1*, E Giankou2, M Papaioannou3, V Roka4, A Tsikrika5, S Kostopoulos6, D Glotsos7, G Sakkas8, E Dardiotis9, D Chaniotis10, S Stathakis11, E Kapsalaki12, E Lavdas13, (1) University of North Carolina, Chapel Hill, NC, (2) ,,,(3) Animus Kyanoys Stavros, Larissa, ,GR, (4) Health Center of Farkadona, Trikala, ,GR, (5) General University Hospital of Larissa, Larissa, ,GR, (6) University of West Attica, Athens, ,GR, (7) ,,,(8) University of Thessaly, Trikala, ,GR, (9) ,,,(10) ,,,(11) Mays Cancer Center - MD Anderson Cancer Center, San Antonio, TX, (12) ,,,(13) University of West Attica, Athens, ,GR |
| PO-GePV-I-54 | Optimized Practice for Metal Artifact Reduction J Dumas*, A Bruget, R Dal, L De Marzi, V Calugaru, R Ferrand, Institut Curie, Paris FR |
| PO-GePV-I-61 | Personalized Patient-Adaptive Sparse-View CT Deep Reconstruction B Song*, L Shen, L Xing, Stanford University School of Medicine, Stanford, CA |
| PO-GePV-M-151 | Evaluation of Efficacy of Iterative Metal Artifact Reduction Algorithm with the Beam Hardening Correction in Image Quality and Radiation Therapy C Kim*, N Vassell, Y Na, Mount Sinai Health System, New York, New York |
| PO-GePV-M-320 | TransCBCT: Improving the Image Quality of Cone-Beam Computed Tomography with Transformer X Chen1*, Y Liu1, B Yang1, J Zhu1, Y Liu1, J Dai1, K Men1, (1) National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, 100021,CN |
| SU-E-201-2 | Cone-Beam CT From Complete Data Using Saddle Trajectories On a Mobile Robotic CBCT Scanner J Albrecht1,2*, S Rit3, P Steininger4, F Ginzinger4, P Huber4, I Messner4, M Kraihamer4, H Schmitz1, S Corradini1, C Belka1,5, C Kurz1, M Riboldi2, G Landry1, (1) Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany, DE, (2) Department of Medical Physics, Ludwig-Maximilians-Universitaet Muenchen (LMU Munich), Garching (Munich), Germany, DE, (3) Univ Lyon, INSA-Lyon, Universite Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69373, LYON, France, FR, (4) Research & Development, medPhoton GmbH, Salzburg, Austria, AT, (5) German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany, DE |
| SU-H330-IePD-F5-3 | A Deep Learning Method to Improve the Quality for High-Speed Imaging in a 1.5 T MRI Radiotherapy System J Zhu*, X Chen, B Yang, R Wei, S Qin, Z Yang, Z Hu, J Dai, K Men, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, 11CN, |
| SU-H330-IePD-F5-6 | Deep Learning-Based Metal Artifact Reduction for Planning CT in Radiation Therapy for Head and Neck Cancer J Lee1, J Lee2, S Jung3*, (1) Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Ulsan, KR, (2) Department of Computer Engineering, Ulsan National Institute of Science and Technology, KR, (3) Department of Radiation Oncology, Seoul National University Hospital, Seoul, KR |
| SU-J-201-1 | A Quantitative Assessment of Image Distortion During Fluoroscopically-Guided Orthopedic Surgery X Nie*, A Siddique, P Hardy, J Zhang, University of Kentucky, Lexington, KY |
| TU-D930-IePD-F2-3 | Accurate Correction of Abdominopelvic CBCT with Internal Air Pockets Using a Hybrid Method of Deep Learning and Deformable Registration for Online Verification of Proton Range J Uh*, C Wang, C Hua, St. Jude Children's Research Hospital, Memphis, TN |
| TU-E-201-2 | Deep-Unfolding-Network-Based Non-Blind Deblurring for Fast-Rotating Wide-Angle Digital Breast Tomosynthesis S Hyun*, S Lee, H Kim, S Cho, Korea Advanced Institute of Science and Technology, Daejeon, 44KR, |
| WE-G-201-5 | Metal Artifact Correction Using High-Energy Data in Photon-Counting CT D Richtsmeier1*, J O'Connell1, P Rodesch1, K Iniewski2, M Bazalova-Carter1, (1) University of Victoria, Victoria, BC, CA, (2) Redlen Technologies, Saanichton, BC, CA |