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Keywords: Cone-beam CT
| MO-C930-IePD-F6-3 | A Monte Carlo Study to Investigate Material Subtraction Performance of a Multi-Layer KV-CBCT Detector I Ozoemelam1*, M Myronakis1, T Harris, P Huber2, M Jacobson1, R Fueglistaller2, M Lehmann2, D Morf2, R Berbeco1, (1) Dana Farber/Brigham and Women's Hospital, Boston, MA, (2) Varian Imaging Laboratory, Baden, Switzerland |
| MO-H345-IePD-F2-3 | Clinical Application of Synthetic CTs for Proton Therapy of Lung Cancer Patients VT Taasti1*, D Hattu1, I Hadzic1, S Pai1, M Gooding2, J Sage2, D De Ruysscher1, A Traverso1, R Canters1. (1) Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands; (2) Mirada Medical Ltd, Oxford, United Kingdom |
| MO-H345-IePD-F2-4 | Deep Learning-Based Simultaneous Multi-Phase Deformable Image Registration of Sparse 4D-CBCT I Herzig1*, P Paysan2, S Scheib2, F-P Schilling3, J Montoya3, M Amirian3, T Stadelmann3, P Eggenberger1, R Fuechslin1, L Lichtensteiger1, (1) Zurich University of Applied Sciences ZHAW, Institute for Applied Mathematics and Physics IAMP, Winterthur, CH (2) Varian Medical Systems Imaging Laboratory, Daettwil AG, CH (3) Zurich University of Applied Sciences ZHAW, Centre for Artificial Intelligence CAI, Winterthur, CH |
| MO-H345-IePD-F4-2 | Evaluation of Abdomen IMPT Treatments Using Deformed Treatment Planning CT Based On Pre-Treatment Cone-Beam CT S Charyyev, M McDonald, A Dhabaan*, Emory Univ, Atlanta, GA |
| PO-GePV-I-1 | Comparing Effective Doses for Hip Imaging Using Cone Beam CT (CBCT) and Multidetector CT (MDCT) J Dave1, X Jiang2*, P Machado1, C Roth1, T Minch3, (1) Thomas Jefferson University, Philadelphia, PA, (2) Ohio State University, Columbus, OH, (3) Curvebeam, Hatfield, PA |
| PO-GePV-I-2 | Short-Scan Cone-Beam Dedicated Breast CT: Self-Supervised Denoising From Single Image Z Fu1*, H Tseng1, A Karellas1, S Vedantham1,2, (1) Department of Medical Imaging, University of Arizona, Tucson, AZ, (2) Biomedical Engineering, University of Arizona, Tucson, AZ |
| PO-GePV-I-3 | Evaluation of the IVIscan Detector for Regulatory Dosimetric Quality Control Including Wide Radiation Beam for Computed Tomography and Cone-Beam Computed Tomography in Radiotherapy C POPOTTE1*, M Munier2, C Devic , N Guillochon2, D Paul1, (1) INSERM UMR 1296, Lyon, ,FR, (2) Fibermetrix, Strabourg, ,FR, |
| PO-GePV-I-5 | Effective Doses for Extremity Imaging Using Cone Beam CT Scanners J Dave1*, T Minch2, (1) Thomas Jefferson University, Philadelphia, PA, (2) Curvebeam, Hatfield, PA |
| PO-GePV-I-6 | An Approach to Convert Dose Quantities From CT- and CBCT-Imaging M Borowski1*, L Pirl1, S Ketelhut2, M Kuhlmann2, L Bueermann2, (1) Klinikum Braunschweig, Braunschweig, DE, (2) Physikalisch-Technische Bundesanstalt, Braunschweig, DE |
| PO-GePV-I-8 | Study On Image Quality Influenced by Two Decomposition Algorithms of Dual Energy Cone Beam CT Z Dong*, C Li, Institute of Medical Technology, Peking University Health Science Center. Beijing 100191 China |
| PO-GePV-I-9 | Segmentation of Tumor and Organs at Risk for CBCT-Based Online Adaptive Radiotherapy Using Recurrent Neural Networks with Multi-Scale Memory H Zhao*, X Liang, B Meng, M Dohopolski, B Choi, B Cai, M Lin, T Bai, D Nguyen, S Jiang, UTSW, Dallas, TX |
| PO-GePV-I-10 | Development of a Deformable Image Registration Technique for Abdominal Cone-Beam CT Using Deep Learning with Generative Adversarial Network Y Zhang1,2, Y Liu1,2, l tie1,2, H Gong2, W Zhao1, G Zhang1, S Xu3*, (1) Beihang University, School of Physics, Beijing, China. (2) The First Medical Center of PLA General Hospital, Department of Radiation Oncology, Beijing, China.(3) National Cancer Center/Cancer Hospital- Chinese Academy of Medical Sciences and Peking Union Medical College, Department of Radiation Oncology, Beijing, China |
| PO-GePV-I-12 | A Hybrid Scatter Correction Algorithm for CBCT to Incorporate System-Specific Information with a Deep Learning-Based Scatter Correction Algorithm H Lee*, A Lalonde, B Winey, G Sharp, H Paganetti, Massachusetts General Hospital / Harvard Medical School, Boston, MA |
| PO-GePV-I-13 | Comparison of Patient-Specific Deep Learning Models for Enhancing 4D-CBCT Image for Radiomics Analysis Z Zhang1*, M Huang2, Z Jiang3, Y Chang4, K Lu5, Z Qiao6, F Yin7, P Tran8, D Wu9, C Beltran10, L Ren11, (1) Duke University, Durham, NC, (2) Mayo Clinic Florida, Jacksonville, FL, (3) Nanjing University, Nanjing, 32, CN, (4) University of Hospital of Pennsylvania, Philadelphia, PA, (5) Duke University, Chapel Hill, NC, (6) University of Florida, Gainesville, FL, (7) Duke University Medical Center, Chapel Hill, NC, (8) University of Maryland, Baltimore, MD, (9) University of Florida, ,,(10) Mayo Clinic, Jacksonville, FL, (11) University of Maryland, Baltimore, MD |
| PO-GePV-I-17 | Evaluation of Automatic Prostate Volume Generation for Cone-Beam CT-Based Delta-Radiomics of Prostate Cancer A Deana, R Delgadillo, R Schmidt, B Spieler, J Ford, D Kwon, M Studenski, K Padgett, M Abramowitz, A Dal Pra, R Stoyanova, N Dogan*, University of Miami, Miami, FL |
| PO-GePV-I-18 | Low-Dose Cone-Beam Dedicated Breast CT: Self-Supervised Denoising From Single Image Z Fu1*, H Tseng1, A Karellas1, S Vedantham1,2, (1) Department of Medical Imaging, University of Arizona, Tucson, AZ, (2) Department of Biomedical Imaging, University of Arizona, Tucson, AZ |
| PO-GePV-M-13 | Exploring the Combination of Deep-Learning Based Direct Segmentation and Deformable Image Registration for Cone-Beam CT Based Auto-Segmentation for Adaptive Radiotherapy X Liang*, H Morgan, T Bai, M Dohopolski, D Nguyen, S Jiang, University of Texas Southwestern Medical Center, Dallas, TX |
| PO-GePV-M-34 | Clinical 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-46 | Region Specific Optimization (RSO)-Based Deep Interactive Registration T Bai1*, M Lin1, X Liang1, B Wang1,2, M Dohopolski1, B Cai1, D Nguyen1, S Jiang1, (1) The University of Texas Southwestern Medical Ctr, Dallas, TX, (2) Southern Methodist University, Dallas, TX |
| PO-GePV-M-80 | Beam Path Length From Isocenter to Skin On Cone-Beam CT Images as An Adaptive Planning Indicator in Proton Therapy for Extremity Tumors N C Biswal*, B Zhang, E Nichols, M E Witek, W F Regine, B Yi, University of Maryland School of Medicine, Baltimore, MD |
| PO-GePV-M-82 | The Pearson Correlation Coefficient of Target and the Beam Path Length Using Cone-Beam CT Images as Adaptive Planning Indicators of Head and Neck Patients Undergoing Proton Therapy D Han1*, N Biswal2, B Zhang3, M Witek4, B Yi5, (1) University of Maryland, Baltimore, MD, (2) Unversity of Maryland, School of Medicine, Bel Air, MD, (3) University of Maryland School of Medicine, Baltimore, MD, (4) University Of Maryland School Of Medicine, ,,(5) University of Maryland School of Medicine, Baltimore, MD |
| PO-GePV-M-90 | Proton Radiography as a Quality Control Tool for 3D and 4D Thorax Synthetic CTs C Seller Oria1*, A Thummerer1, J Free1, G Guterres Marmitt1, A Meijers2, A Knopf123, J Langendijk1, S Both1, (1) Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands, (2) Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland, (3) Department of Internal Medicine, Center for Integrated Oncology Cologne, University Hospital of Cologne, Cologne, Germany |
| PO-GePV-M-96 | Assessing the Ability to Detect Lung Target Amplitude Changes Between 3D CBCT and 4D CBCT by Geometric and Dosimetric Evaluation C Baley*, N Kirby, S Stathakis, N Papanikolaou, D Saenz, University of Texas HSC SA, San Antonio, TX |
| PO-GePV-M-106 | Online Plan Adaptation in Intensity-Modulated Proton Therapy Based On Planned Versus Cumulative Dose Indices M Bobic1,2*, K Nesteruk1, H Lee1, A Lalonde1, B Winey1, A Lomax2,3, H Paganetti1, (1) Massachusetts General Hospital and Harvard Medical School, Boston, MA, (2) ETH Zurich, Zurich, CH, (3) Paul Scherrer Institute, Villigen, CH |
| PO-GePV-M-107 | A New Metric of Contour and Dose Accuracy Applied to Automated Adaptation Features in a Commercial TPS M Bogue1*, J Chen2, E Yu3, J Patrick4, R Munbodh3, (1) University of Rhode Island, Kingston, RI, (2) Brown University, Providence, RI, (3) Alpert Medical School of Brown University/Rhode Island Hospital, Providence, RI, (4) London Regional Cancer Program, London, ON, CA |
| PO-GePV-M-114 | A Dosimetric Comparison for Adaptive Cone-Beam CT and MRI Based Radiotherapy for Stereotactic Partial Breast Irradiation D Parsons*, L Chen, H Lee, Y Zhang, B Cai, T Zhuang, B Hrycushko, C Park, Y Gonzalez, X Zhong, J Visak, C Nwachukwu, N Kim, P Alluri, A Rahimi, M Lin, University of Texas Southwestern Medical Center, Dallas, TX |
| PO-GePV-M-118 | Variations in Rectal Volume and Dose as Estimated On Daily CBCT and the Association with Radiation Proctitis During IMRT for Prostate Cancer W Martin*, W Bodner, V Cardozo, L Goddard, J Tang, S Kalnicki, M Garg, W Tome, P Brodin, Montefiore Medical Center, Bronx, NY |
| PO-GePV-M-126 | Metal Artifact Reduction in Cone-Beam Computed Tomography for Ultrasound-Guided Cardiac Radioablation Using Protons S Puvanasunthararajah1*, S Camps2, M Wille3, P Ramachandran4, D Fontanarosa5, (1) Queensland University of Technology, Brisbane, Queensland, Australia, (2) EBAMed SA, Geneva, Switzerland,(3) Queensland University Of Technology, Brisbane, Queensland, Australia,(4) Princess Alexandra Hospital, Brisbane, Queensland, Australia,(5) Queensland University of Technology, Brisbane, Queensland, Australia |
| PO-GePV-M-144 | Generation of Synthetic CT From CBCT Images to Reconstruct Dose for Lung Cancer Radiotherapy D Patel1*, T Stanescu2, J-P Bissonnette2, (1) University of Toronto, Toronto, ON, CA, (2) Princess Margaret Cancer Centre, Toronto, ON, CA |
| PO-GePV-M-150 | Feasibility of Using the Planning CT (pCT) Electron Density Calibration Curve for Prostate Cancer Adaptive Planning Based On Daily Cone-Beam CT P Shukla*, D Mynampati, P Brodin, A Lukaj, R Yaparpalvi, A Basavatia, W Tome, Montefiore Medical Center, Bronx, NY |
| PO-GePV-M-153 | Using StyleGAN for Unpaired Image Translation Between CBCT and CT for Adaptive Proton Treatment D Tang*, T Bortfeld, S Yan, Massachusetts General Hospital and Harvard Medical School, Boston, MA |
| PO-GePV-M-161 | Interfractional Motion and Setup Errors for Compression-Belt-Immobilized Lung-SBRT Quantified with CBCT B Rudek*, Jamaica Plain VA Medical Center, Boston, MA |
| PO-GePV-M-162 | Variation in Relative Left and Right Diaphragm Positions Across Imaging Sessions Using CT Simulation and Cone-Beam CT Images M Lauria1*, K Singhrao2, J Lewis3, D O'Connell1, W Lin4, A Santhanam1, L Naumann1, B Stiehl1, P Boyle1, P Lee5, D Low1, (1) UCLA, Los Angeles, CA, (2) UCSF, San Francisco, CA, (3) Cedars-Sinai Medical Center, Pacific Palisades, CA, (4) Pepperdine University, Malibu, CA, (5) MD Anderson Cancer Center, Houston, TX |
| PO-GePV-M-163 | Daily Coverage Assessment Using Deep Learning Generated Synthetic CT for Lung SBRT Patients R LJ Qiu1, Y Lei1, J Shelton1,2, K Higgins1, J Bradley1, W Curran1, T Liu1, A Kesarwala1, X Yang1, (1) Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA (2) Department of Radiation Oncology, Grady Memorial Hospital, Atlanta, GA |
| PO-GePV-M-164 | Evaluation of the Dose Delivered by Cone-Beam CT Patient Positioning Exams During Prostate VMAT Treatments with An Innovative Plastic Scintillating Detector C POPOTTE1*, R Letellier2, P Retif2, M Munier3, D Paul1, (1) INSERM UMR 1296, Strabsourg, ,FR, (2) Chr Metz-thionville, Metz, ,FR,(3) Fibermetrix, strasbourg, ,FR |
| PO-GePV-M-167 | Feasibility Study of Quantifying the Dosimetric Discrepancies of Planned Dose in Comparison to Delivered Dose Utilizing Daily Cone-Beam Computed Tomography (CBCT) T Cuthbert1*, S Stathakis2, P Mavroidis3, N Papanikolaou4, W Jones5, (1) UT Health Sciences Center, San Antonio, TX, (2) Mays Cancer Center - MD Anderson Cancer Center, San Antonio, TX, (3) University of North Carolina, Chapel Hill, NC, (4) University of Texas HSC SA, San Antonio, TX, (5) University of Texas HSC San Antonio, San Antonio, TX |
| PO-GePV-M-168 | Feasibility Study of Deep Learning Based ITV Prediction in Cone Beam CT Images & It’s Dosimetric Study of Lung SBRT S Zhang1, Z Li2, E Yang3, Y Li4, L Zhang5, X Zheng6, J Qiu7*, (1) Department of Radiation Oncology, Huadong Hospital, Fudan University,, Shanghai, 31, CN, (2) Duke Kunshan University, Kunshan, 32, (3) Department of Radiation Oncology, Huadong Hospital, Fudan University,, Shanghai, 31, CN, (4) Department of Radiation Oncology, Huadong Hospital, Fudan University,, Shanghai, 31, CN, (5) Department of Radiation Oncology, Huadong Hospital, Fudan University,, Shanghai, 31, CN, (6) Department of Radiation Oncology, Huadong Hospital, Fudan University,, Shanghai, 31, (7) Department of Radiation Oncology, Huadong Hospital, Fudan University,, Shanghai, 31, CN |
| PO-GePV-M-287 | A Two-Step Method to Improve Image Quality of CBCT with Phantom-Based Supervised and Patient-Based Unsupervised Learning Strategies Y Liu1,2*, X Chen1, J Zhu1, B Yang1, R Wei1, R Xiong2, H Quan2, 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, (2) Wuhan University, Wuhan, 430072 ,CN, |
| PO-GePV-M-298 | Multi-Site Evaluation of the AI-Assisted Auto Segmentation Quality for a CBCT Based Online Adaptive Radiotherapy System B Meng*, M Dohopolski, S Jiang, M Lin, B Cai, University of Texas Southwestern Medical Center, Dallas, TX |
| 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 |
| PO-GePV-M-323 | Auto-Segmentation for Limited Field of View CBCT in Male Pelvic Region Using Deep Learning Method H Hirashima1*, M Nakamura2, K Imanishi3, M Nakao4, T Mizowaki5, (1) Kyoto University, Graduate School of Medicine, Department of Radiation Oncology and Image-Applied Therapy, Kyoto, JP, (2) Kyoto University, Graduate School of Medicine, Department of Human Health Sciences, Kyoto, JP, (3) e-Growth Co., Ltd., Hyogo, JP, (4) Kyoto University, Graduate School of Informatics, Department of Systems Science, Kyoto, JP,(5) Kyoto University, Graduate School of Medicine, Department of Radiation Oncology and Image-Applied Therapy, Kyoto, JP |
| PO-GePV-M-348 | Interpretable Feature-Specific Generative Adversarial Network Model for 3D CBCT Image Enhancement for Radiomics Z Qiao1*, Z Zhang2, Z Jiang3, Y Lai4, J Lee5, D Wu6, C Beltran7, L Ren8, M Huang9, (1) University of Florida, Gainesville, FL, (2) Duke University, Durham, NC, (3) Duke University, Durham, NC, (4) University of Texas at Arlington, Arlington, TX, (5) Duke Radiation Oncology, ,,(6) University of Florida, ,,(7) Mayo Clinic, Jacksonville, FL, (8) University of Maryland, Baltimore, MD, (9) Mayo Clinic Florida, Jacksonville, FL |
| PO-GePV-T-338 | Improving Imaging-Based Winston-Lutz Results with Synthetic CT Phantoms M Fan, D Quiring, P Sansourekidou*, Y Natsuaki, University of New Mexico, Albuquerque, NM |
| PO-GePV-T-426 | Adaptive Workflow for Prostate, Seminal Vesicle, and Nodal Volume SBRT On a CBCT Adaptive AI Driven System E Laugeman*, A Price, L Henke, B Baumann, Washington University School of Medicine in St. Louis, St. Louis, MO |
| SU-E-201-1 | A Practical and Robust Method for Beam-Blocker Based Cone Beam CT Scatter Correction H Cui, X Jiang, Y Yang*, University of Science and Technology of China, Hefei, China |
| 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-E-201-8 | Scatter Correction in Known-Component Model-Based Cone-Beam CT Reconstruction in the Presence of Metal Hardware A Lopez Montes1*, W Zbijewski2, J Siewerdsen3, J Stayman4, S Liu5, (1) Johns Hopkins University, Baltimore, MD, (2) Johns Hopkins University, Baltimore, MD, (3) Johns Hopkins University, Baltimore, MD, (4) Johns Hopkins University, Baltimore, MD, (5) Johns Hopkins University, Baltimore, MD |
| SU-F-201-2 | Improving Cone-Beam CT Auto-Segmentation Accuracy Using Cycle GANs for Domain Adaptation K Shah1*, J Shackleford1, N Kandasamy1, G Sharp2, (1) Drexel University, Philadelphia, PA, (2)Massachusetts General Hospital, Boston, MA |
| SU-F-201-3 | Leveraging the Elliptical Shape of the Uterocervix On Semi-Axial Cross-Sections for Improved Deep-Learning Segmentation On Cone-Beam CT S Mason1*, L Wang2, K Zormpas-Petridis2, M Blackledge2, S Lalondrelle1, H Mcnair1, E Harris2, (1) Royal Marsden NHS Foundation Trust, Sutton, SRY, GB, (2) Institute Of Cancer Research |
| SU-F-BRA-2 | Development and Inter-Institutional Validation of An Automatic Vertebral-Body Misalignment Error Detector for Cone-Beam CT Guided Radiotherapy D Luximon1*, T Ritter2, E Fields2, J Neylon1, R Petragallo1, Y Abdulkadir1, J Charters1, D Low1, J Lamb1, (1) University of California, Los Angeles, Los Angeles, CA, (2) VCU Health System, Chesterfield, VA |
| SU-F-BRC-6 | Patient-Specific Synthetic MRI Generation From CBCT for Image Guidance in Liver SBRT Z Zhang1*, Z Jiang2, K Lu3, F Yin4, L Ren5, (1) Duke University, Durham, NC, (2) Duke University, Durham, NC, (3) Duke University, Chapel Hill, NC, (4) Duke University Medical Center, Chapel Hill, NC, (5) University of Maryland, Baltimore, MD |
| SU-H300-IePD-F5-1 | A Deep Learning Approach to Correct Scatter in Dual-Energy CBCT Data: Accuracy and Potential for Adaptive Proton Therapy A Lalonde*, B Winey, H Paganetti, G Sharp, Department of Radiation Oncology Massachusetts General Hospital and Harvard Medical School, Boston, MA |
| SU-H300-IePD-F5-2 | Assessment of CBCT-Based Adaptive Intensity Modulated Proton Therapy (IMPT) Using Automated Planning for Head and Neck Cancer Y Xu*, N Dogan, K Padgett, M De Ornelas, University of Miami, Miami, FL |
| SU-H300-IePD-F5-3 | Deep Learning Based 4D Synthetic CTs Generated From CBCTs for Proton Dose Calculations in Adaptive Proton Therapy A Thummerer1*, C Seller Oria1, S Visser1, P Zaffino2, A Meijers3, R Wijsman1, G Guterres Marmitt1, J Seco4,5, J Langendijk1, A Knopf1,6, M Spadea2, S Both1, (1) Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen,NL, (2) Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro,IT, (3) Center for Proton Therapy, Paul Scherrer Institute, Villigen, CH, (4) DKFZ Heidelberg, Heidelberg, DE, (5) Department of Physics and Astronomy, Heidelberg University, Heidelberg, DE (6) Department I of Internal Medicine, Center for Integrated Oncology Cologne, University Hospital of Cologne, Cologne, DE |
| SU-H300-IePD-F5-5 | Variation of Bragg Peak Positions in Cone-Beam CT as An Indicator of Adaptive Planning of the Head and Neck IMPT Treatments B Zhang*, W Yao, N Biswal, J Zhou, J Xu, H Xu, S Chen, B Yi, University of Maryland School of Medicine, Columbia, MD |
| SU-H300-IePD-F6-1 | Dosimetric Evaluation of a CBCT-Based Daily Adaptive Radiotherapy Protocol for Locally Advanced Cervical Cancer D Branco*, J Mayadev, K Moore, X Ray, UC San Diego, La Jolla, CA |
| SU-H300-IePD-F8-2 | Introduction of Lambda: A Convenient and Robust Subvoxel Image Similarity Metric P Boyle*, M Lauria, B Stiehl, L Naumann, A Santhanam, D Low, UCLA, Los Angeles, CA |
| SU-H300-IePD-F8-3 | Application of Lambda: Subvoxel Image Similarity in Complex Images P Boyle*, M Lauria, B Stiehl, L Naumann, A Santhanam, D Low, UCLA, Los Angeles, CA |
| SU-H300-IePD-F8-4 | A Novel Model for Characterizing the Effects of Gantry Flex On 2D Antiscatter Grid Performance in CBCT Systems M Eldib*, F Bayat, M Miften, C Altunbas, University of Colorado School of Medicine, Aurora, CO |
| SU-H330-IePD-F5-2 | Deep Learning Projection Interpolation for Sparsely Sampled Real Patient CBCT Reconstruction K Lu1*, Z Zhang1, L Ren2, F Yin1,3, (1) Duke University, Durham, NC, (2) University of Maryland, Baltimore, MD, (3) Duke University Medical Center, Durham, NC |
| SU-H330-IePD-F8-1 | Generating a Phantom with a Motion Model Ground Truth B Lau*, O Dillon, T Reynolds, R O'Brien, University of Sydney, Sydney, NSW, AU, |
| SU-H430-IePD-F5-4 | Developing a Head-And-Neck CBCT Segmentation Network From Unlabeled Data Via Domain Adaptation and Self-Training T Mengke, X Liang, H Morgan, H Shao, S Jiang, Y Zhang*, UT Southwestern Medical Center, Dallas, TX |
| SU-J-202-2 | Accurate HU Generation for MR-LINAC Using MV-CBCT Y Hu1,2, C Williams1,2, R Berbeco1,2, M Myronakis*, (1) Brigham & Women's Hospital, Harvard Medical School, Jamaica Plain, MA, (2) Dana Farber Cancer Institute, Boston, MA |
| SU-J-202-3 | A Raw Data Correction and Iterative Reconstruction Pipeline for Quantitative CBCT-Guided Radiation Therapy F Bayat*, M Eldib, M Miften, C Altunbas, University of Colorado School of Medicine, Aurora, CO |
| SU-J-BRC-2 | An Automated Pipeline for AI-Based Analysis of CBCT-Guided Patient Alignment J Neylon1*, D Luximon1, T Ritter2, J Lamb1, (1) UCLA, Los Angeles, CA, (2) VCU Health System, Chesterfield, VA |
| SU-K-202-4 | Monte Carlo Model of a Prototype Dual-Layer Flat Panel Detector for Multi-Energy KV-CBCT I Ozoemelam1*, M Myronakis1, T Harris, P Huber2, M Jacobson1, R Fueglistaller2, M Lehmann2, D Morf2, R Berbeco1, (1) Dana Farber/Brigham and Women's Hospital, Boston, MA, (2) Varian Imaging Laboratory, Baden, Switzerland |
| SU-K-BRC-3 | Validation of CBCT-Based Machine-Learning Automated Planning for Adaptive Prostate Radiation Therapy A Khalifa1,2*, M Golshan1,3, I Navarro1,3, V Malkov3, C McIntosh1,2,3,4,5, T Purdie1,2,3, T Tadic1,3, J Winter1,3, (1) University of Toronto, Toronto, ON, CA, (2) Techna Institute, Toronto, ON, CA, (3) Princess Margaret Cancer Centre, Toronto, ON, CA, (4) Peter Munk Cardiac Center, Toronto, ON, CA, (5) Vector Institute, Toronto, ON, CA |
| TH-A-207-2 | Development of a Photon-Counting Based Multi-Energy Small Animal CBCT to Improve Radiation Dose Calculation Accuracy in Preclinical Radiation Research X Hu1*, Y Zhong1, K Yang2, X Jia1, (1) University of Texas Southwestern Medical Center, Dallas, TX, (2) Harvard Medical School, Massachusetts General Hospital, Boston, MA |
| TH-B-206-4 | Segmentation by Test-Time Optimization for CBCT-Based Adaptive Radiation Therapy X Liang*, T Bai, J Chun, H Morgan, D Nguyen, J Park, S Jiang, University of Texas Southwestern Medical Center, Dallas, TX |
| TH-C-201-2 | Catheter Tracking Using Gated Continuous-Sweep Limited Angle (CLA) Fluoroscopy M Wagner*, S Periyasamy, P Laeseke, A Raval, M Speidel, University of Wisconsin - Madison, Madison, WI |
| TH-C-201-3 | Evaluating X-Ray Source Arrays for Tomography and Tomosynthesis in Lung Cancer Radiotherapy O Dillon*, R O'Brien, University of Sydney, Sydney, NSW, AU, |
| TH-D-207-5 | Hybrid Adversarial Network for Ultra-Quality Pulmonary Anatomy Imaging From Cone-Beam CT Images J Zhu1, W Chen2, Y Huang1*,A Nicol3, Y Lam4, J Cai5, G Ren6, (1) Hong Kong Polytechnic University, Hong Kong (2) Chinese Academy of Sciences, Shenzhen Advanced Technology Academe, Shenzhen,CN(3) The Hong Kong Polytechnic University, Hong Kong(4) Hong Kong Polytechnic University, Hong Kong(5) Hong Kong Polytechnic University, Hong Kong (6) Hong Kong Polytechnic University, Hong Kong |
| TH-E-201-1 | A Simulation Platform to Evaluate the Effect of 2D Antiscatter Grid Primary Transmission On CBCT Image Quality M Eldib*, F Bayat, M Miften, C Altunbas, University of Colorado School of Medicine, Aurora, CO |
| TH-E-201-2 | Continuous Dual-Isocenter Imaging: Simultaneously Extending the Longitudinal and Lateral Intraoperative 3D CBCT Field-Of-View for Assessing Musculoskeletal Trauma T Reynolds1*, S Hatamikia2,3, O Dillon2, Y Ma4, A Kanawati5, J Stayman4, R O'Brien1, (1) University of Sydney, Sydney, AU, (2) Austrian Center for Medical Innovation and Technology, Wiener Neustadt, AUT, (3) Danube Private University, 3500 Krems an der Donau, AUT, (4) Johns Hopkins University, Balitmore, MD, (5) Westmead Hospital, Sydney, AU |
| TH-E-201-3 | BEST IN PHYSICS (IMAGING): Deep Learning-Based Motion Compensation for 4D-CBCT Reconstruction Z Zhang1*, J Liu2, D Yang3, U Kamilov2, G Hugo1, (1) Washington University School of Medicine in St. Louis, St. Louis, MO, (2) Washington University in St. Louis, St. Louis, MO, (3) Duke University, Chapel Hill, NC |
| TH-E-201-5 | Perovskite Direct Conversion Detector Design Optimisation and Characterisation for Truebeam KV-, MV-CBCT and Koning Breast-CT J O'Connell*, M Bazalova-Carter, University of Victoria, Victoria, BC,Canada |
| TH-E-201-6 | Sensitivity Analysis of Acquisition Precision in Fast Low-Dose 4DCBCT B Lau1*, O Dillon2, S Vinod3, R O'Brien2, T Reynolds2, (1) University Of Sydney, Sydney, NSW, AU, (2) University Of New South Wales, Sydney, NSW, AU |
| TH-E-BRC-3 | Longitudinal Unsupervised Deformable Image Registration Network for Adaptive Radiotherapy D Lee*, Y Hu, S Alam, J Jiang, L Cervino, P Zhang, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY |
| TH-F-201-3 | CT-Guided and Sparsity-Constrained Multi-Material Decomposition for Dual Energy CBCT Q Wang1*, H Xie1, T Wang1, J Roper1, X Tang2, J Bradley1, T Liu1, X Yang1, (1) Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, (2) Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA |
| TH-F-BRC-2 | Feasibility of CycleGAN Enhanced Low Dose CBCT Imaging for Prostate Radiotherapy Dose Calculation Y Chan1*, M Li1, K Parodi2, C Belka1,3, G Landry1, C Kurz1, (1) Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany, (2) Department of Medical Physics, Ludwig-Maximilians-Universitaet Muenchen (LMU Munich), Garching (Munich), Germany, (3) German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany |
| TU-D1000-IePD-F2-5 | Adaptive Radiotherapy Via Deep Learning-Based Quantitative Cone-Beam CT Imaging L Wan1*, H Wu2, S Xu3, B Sun4, W Zhao5, (1) Beihang University, Beijing, BJ, CN (2) Beijing Cancer Hospital, Beijing, BJ, CN, (3) National Cancer Center/Cancer Hospital- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, BJ, CN, (4) Beihang University,Beijing, BJ, CN(5) Beihang University, Beijing, BJ, CN |
| TU-D1030-IePD-F6-1 | Image-Guided Margin Assessment to LINAC-Based Radiosurgery for Single and Multiple Brain Metastases Based On Post-Treatment CBCT Shifts T Reynolds*, M Ozer, Minnesota Oncology, Maplewood, MN |
| 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-201-4 | Lesion Detectability in Contrast-Enhanced Breast CT Using Model Observers S Lyu1*, A Hernandez2, C Abbey3, J Boone4, (1) University of California Davis, Davis, CA, (2) University of California Davis Medical Center, Sacramento, CA, (3) University of California Santa Barbara, Santa Barbara, CA, (4) University of California Davis Medical Center, Sacramento, CA |
| TU-GH-BRB-5 | Automated Robotic Targeting System for In-Vivo Histotripsy Treatment M Wagner*, S Periyasamy, A Kutlu, J Swietlik, T Ziemlewicz, M Speidel, F Lee, P Laeseke, University of Wisconsin - Madison, Madison, WI |
| TU-GH-BRB-8 | Improved MV-CBCT Using a Novel Multilayer Imager T Harris1*, M Jacobson1, M Myronakis1, M Lehmann2, P Huber2, D Morf2, I Ozoemelam1, R Berbeco1, (1) Dana Farber/Brigham and Women's Hospital, Boston, MA, (2) Varian Imaging Laboratory, Baden, Switzerland |
| TU-J430-BReP-F2-2 | Daily Adaptive Replanning with Dose Accumulation for Prostate Ultra-Hypofractionated Radiotherapy Using Machine Learning Automated Planning On CBCT M Golshan1,2*, A Khalifa2,3, J Winter1,2,3, J Xie1, C McIntosh1,2,3,4,5, T Purdie1,2,3, V Malkov1,3, T Tadic1,2,3, (1) Princess Margaret Cancer Centre, Toronto, ON, CA, (2) University of Toronto, Toronto, ON, CA,(3) Techna Institute, Toronto, ON, CA,(4) Peter Munk Cardiac Center, Toronto, ON, CA, (5) Vector Institute, Toronto, ON, CA |
| WE-B-202-5 | Scatter Correction of 4D Cone Beam Computed Tomography Images for Time-Resolved Proton Dose Calculation: First Patient Application H Schmitz1*, M Rabe1, G Janssens2, S Rit3, K Parodi4, C Belka1,5, G Landry1,4, F Kamp1,6, C Kurz1,4, (1) Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany, DE, (2) Ion Beam Applications SA, Louvain-la-Neuve, Belgium, BE, (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) Department of Medical Physics, Ludwig-Maximilians-Universitaet Muenchen (LMU Munich), Garching (Munich), Germany, DE, (5) German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany, DE, (6) Department of Radiation Oncology, University Hospital Cologne, Cologne, Germany, DE |
| WE-C1000-IePD-F6-3 | Investigating the Feasibility of CT Ventilation Imaging On Fast, Low-Dose 4DCBCT to Enable Daily Adaptive Lung Function Sparing H Byrne1*, O Dillon1, S Blake1, J Kipritidis2, R O'Brien1, P Keall1, (1) ACRF Image X Institute, The University of Sydney, Sydney, AU (2) Northern Sydney Cancer Centre, Royal North Shore, Sydney, AU |
| WE-C930-IePD-F7-4 | Evaluating the Impact of Deformation-Based and Scatter-Correction-Based Synthetic CT Generation Algorithms for Proton Therapy Y Tseng*, B Shen, H Lin, P Tsai, New York Proton Center, New York, NY |