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Keywords: cone-beam ct
| MO-A-TRACK 6-3 | Head, Shoulders, Knees, and Toes: Towards Full Body Upright Cone-Beam CT Imaging T Reynolds*, O Dillon, R O'Brien, ACRF Image X Institute, University of Sydney,Sydney, NSW, AU |
| MO-EF-TRACK 4-3 | Fast 4D-CBCT Reconstruction Using a Novel Feature-Compensated Deformable Convolutional Network (FeaCo-DCN) Z Jiang1*, Y Chang2, Z Zhang3, F Yin4, L Ren5, (1) Duke Univeristy, Durham, NC, (2) Duke University, Durham, NC, (3) Duke Univeristy,Durham, NC, (4) Duke University, Durham, NC, (5) Duke University, Durham, NC |
| MO-EF-TRACK 4-9 | Non-Circular Orbits On a Clinical Robotic C-Arm for Reducing Metal Artifacts in Orthopedic Interventions Y Ma1*, T Reynolds2, G Gang1, O Dillon2, T Russ3, W Wang1, T Ehtiati4, C Weiss1, N Theodore1, J Siewerdsen1, R O'Brien2, J Stayman1, (1) Johns Hopkins University, Baltimore, MD, (2) ACRF Image X Institute, University of Sydney, Sydney, NSW, AU, (3) Heidelberg University, Mannheim, GER, (4) Siemens Healthineers, Forchheim, GER |
| MO-IePD-TRACK 2-1 | Dedicated Cone-Beam Breast CT with Offset-Detector: Estimates of Mean Glandular Dose (MGD) with Heterogeneous Tissue Distribution and Real Breast Shapes From Clinical Datasets H Tseng*, A Karellas, S Vedantham, University of Arizona, Tucson, AZ |
| MO-IePD-TRACK 3-1 | A Hybrid Approach for Correcting CBCT by Exploiting Individual Strength of Deep Learning and Deformable Registration J Uh*, C Wang, S Acharya, C Hua, St. Jude Children's Research Hospital, Memphis, TN |
| MO-IePD-TRACK 3-4 | High-Quality CBCT Reconstruction From Sparse and Limited-Size Projections Using a Joint Deformable Registration and Reconstruction Network (Reg-ReconNet) Y Zhang*, UT Southwestern Medical Ctr at Dallas, Dallas, TX |
| MO-IePD-TRACK 3-5 | Physics-Based Motion Artifact Simulation in CT/CBCT Images Using 4DCT Phases S Alam1*, T Li1, S Zhang2, P Zhang1, S Nadeem1, (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) Peking University Cancer Hospital & Institute |
| MO-IePD-TRACK 3-7 | Thoracic CBCT-Based Synthetic CT for Lung Stereotactic Body Radiation Therapy Y Lei*, L Qiu, A Kesarwala, J Roper, K Higgins, J Bradley, T Liu, X Yang, Emory Univ, Atlanta, GA |
| MO-IePD-TRACK 4-1 | Automatic Liver Tumor Localization Using a Combined Deep Learning and Biomechanical Model (DL-Bio) H Shao1*, X Huang2, M Folkert3, J Wang4, Y Zhang5, (1) The University of Texas Southwestern Medical Center, Dallas, TX, (2) Northwestern memorial hospital, Chicago, IL, (3) The University of Texas Southwestern Medical Center, Dallas, TX, (4) UT Southwestern Medical Center, Dallas, TX, (5) UT Southwestern Medical Ctr at Dallas, Dallas, TX |
| MO-IePD-TRACK 4-1 | Evaluation of Cone-Beam Computed Tomography-Based Radiomic Features Reproducibility: A Phantom Study T Adachi1,2*, M Nakamura1,2, H Iramina2, T Mizowaki2, (1) Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan, (2) Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan |
| PO-GePV-M-26 | A Multiple Modality Comparison of Radiomic Features for Prostate Cancer R Delgadillo*, B Spieler, J Ford, D Kwon, F Yang, M Studenski, K Padgett, M Abramowitz, A Dal Pra, N Dogan, University of Miami, Miami, FL |
| PO-GePV-M-75 | Importance of Incorporating Rotational Setup Differences for QACT Dosimetric Evaluations S Davis*, V Chaswal, R Tolakanahalli, Miami Cancer Institute, Miami, FL |
| PO-GePV-M-79 | Comparison of the Efficacy of CT On Rails Versus In-Room CBCT for Online Daily Adaptive Proton Therapy K Nesteruk1*, M Bobic1, A Lalonde1, H Lee1, G Sharp1, J Verburg1, B Winey1, A Lomax2, H Paganetti1, (1) Massachusetts General Hospital and Harvard Medical School, Boston, MA, (2) Paul Scherrer Institute, Villigen, CH |
| PO-GePV-M-85 | Plan Quality and Feasibility of Adaptive CBCT-Guided Prostate and Nodal Volume SBRT E Laugeman*, A Price, M Prusator, L Henke, B Baumann, Washington University in St. Louis, St. Louis, MO |
| PO-GePV-M-86 | Comparison of Online Adaptation and Offline Re-Planning for Head and Neck Intensity-Modulated Proton Therapy M Bobic1,2*, A Lalonde1, K Nesteruk1, H Lee1, J Verburg1, G Sharp1, 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-91 | Utilization of the QUART Phantom to Evaluate Imaging Stability of a CBCT-Based Online Adaptive Workflow A Dare*, K Homann, M Morales Paliza, Vanderbilt University Medical Center, Nashville, TN |
| PO-GePV-M-92 | Evaluation of Online Planning Strategies for CBCT-Based Adaptive Radiotherapy for Locally Advanced Lung Cancer J Kim*, W Mao, I Gallagher, J Riess, S Vance, B Movsas, IJ Chetty, A Kretzler, Henry Ford Health System, Detroit, MI |
| PO-GePV-M-104 | Management of Urgent Sim-And-Treat Palliative Cases Via Halcyon KV-ICBCT Images M Gei*, M Randall, D Pokhrel, University of Kentucky, Lexington, KY |
| PO-GePV-M-112 | Evaluation of Larynx Displacements in Pre-Treatment CBCT Scans for a Population of Laryngeal Cancer Patients A Antolak*, T Gray, N Woody, S Koyfman, P Xia, The Cleveland Clinic Foundation, Cleveland, OH |
| PO-GePV-M-113 | Dosimetric Validation of Surface Guided Patient Set-Up for Breast Radiotherapy A Gopal*, G Lasio, B Zhang, H Xu, S Chen, S Mcavoy, C Decesaris, University of Maryland School of Medicine, Baltimore, MD |
| PO-GePV-M-118 | Quantitative Analysis of the Accuracy of CT to CBCT Deformable Image Registration for Contour Mapping for Prostate Cancer R Schmidt*, N Dogan, J Ford, M Studenski, M Abramowitz, K Padgett, B Spieler, Y Xu, R Delgadillo, University of Miami, Miami, FL |
| PO-GePV-M-121 | Deep-Learning Based Auto-Classification of Anatomical Regions for IGRT Applications: Implementation and Explainability J Cruz Bastida*, S Li, E Pearson, H Al-Hallaq, The University of Chicago, Chicago, IL |
| PO-GePV-M-122 | Comparison of KV-CBCT Dose Measurement Techniques for Annual Linac QA B Rudek, K Osterman*, J Xue, NYU Langone Health, New York, NY |
| PO-GePV-M-141 | Using Previously Registered CBCT Image to Facilitate Online HCT to CBCT Image Registration in Lung Stereotactic Body Radiation Therapy J Liang*, D Yan, J Wloch, J Sliwinski, I Grills, C Stevens, T Guerrero, Q Liu, William Beaumont Hospital, Royal Oak, MI |
| PO-GePV-M-232 | Clinical Evaluation Study of Image Guided Radiotherapy (IGRT) for Prostate Cancer Comparing Kv Imaging of Fiducial Markers and Cone Beam Computed Tomography (CBCT) Using SpaceOAR-View as a Matching Structure R Mghari*, I Pataki, K Keene, Cape Fear Valley Medical Center, Holly Springs, NC |
| PO-GePV-T-86 | Quantitative & Statistical Analyses of Monthly OBI QA On Varian ProBeams Z Diamond*, A Stanforth, R Slopsema, K Langen, Emory Proton Therapy Center, Atlanta, GA |
| PO-GePV-T-232 | Evaluation of Bladder Dosimetric Property for Localized Prostate Cancer Using Deformable Registration of Simulation CT C Ma*, X Zhang, Y Zhang, N Yue, X Wang, Rutgers University, New Brunswick, NJ |
| PO-GePV-T-235 | Can a Seven-Day Subset of CBCTs Be Representative of All CBCTs When Reconstructing the Dose Delivered Over the Entire Course of Treatment for Prostate Bed Radiotherapy? D Soliman1*, M Noy2, M Karp3, A Dal Pra3, E Bossart3, (1) University of Miami, Coral Gables, FL, (2) Florida International University, Miami, FL, (3) University Of Miami, Miami, FL |
| SU-F-TRACK 3-4 | Image Quality Evaluation of a Compressed-Sensing Algorithm for Image Reconstruction in Dedicated Cone-Beam Breast CT Using Offset-Detector S Vedantham*, H Tseng, A Karellas, University of Arizona, Tucson, AZ |
| SU-F-TRACK 6-3 | Implementation of High-Quality Motion-Compensated Simultaneous Algebraic Reconstruction Technique (mc-SART) Cone-Beam CT (CBCT) Imaging Using the 5D Model in a Prospective Patient Study K Singhrao1*, D O'Connell1, J Fu1, M Lauria1, B Stiehl1, A Raldow1, J Hegde1, A Lee1, A Santhanam1, D Low1, J Lewis2, (1) Dept. of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, (2) Dept. of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA |
| SU-IePD-TRACK 1-1 | CBCT Manual Vs. Semi-Automatic Single and Multi-Slice Low Contrast Resolution Analysis N Becerra Espinosa1*, L Claps2, P Alaei3, (1) University of Minnesota, Minneapolis, MN, (2) Memorial Sloan Kettering, New York, NY |
| SU-IePD-TRACK 1-3 | Efficient Visualisation of Changes in CBCT During Radiotherapy to Detect Lung Infections M van Herk1*, G Price2, A Clough2, J Sanders2, C Faivre-finn1,2, C Eccles2, M Aznar1, (1) The University of Manchester, Manchester, GB, (2) The Christie Hospital NHS Foundation Trust, Manchester, GB |
| SU-IePD-TRACK 1-7 | Patient-Specific Deep Learning Model for Enhancing 4D-CBCT Image for Radiomic Analysis Z Zhang*, M Huang, Z Jiang, Y Chang, K Lu, F Yin, L Ren, Duke University Medical Center, Durham, NC |
| TH-B-TRACK 6-2 | Near Real-Time Intra-Fractional 6D Monitoring of Prostate Position Using a Scheme Based On the Skeletonization of Implanted Marker Images H Kuo1,2*, D Lovelock1, C Della-Biancia1, A Damato1, L Happersett1, S Lin2, M Zelefsky1, L Cervino1, S Berry1, (1) Memorial Sloan Kettering Cancer Center, New York, CT, (2) Norwalk Hospital, Norwalk, CT, |
| TH-E-TRACK 3-2 | Development of a Prototype C-Arm Photon Counting Detector CT System for Interventional Imaging: First in Vivo Animal Results X Ji, S Periyasamy, M Feng, G Chen, P Laeseke, K Li*, University of Wisconsin, Madison, WI |
| TH-E-TRACK 3-3 | Dual Energy CT Using Flying Focal Spot and An Anti-Scatter Grid as a Filter S Hsieh1*, N Bennett2, A Wang2, (1) Mayo Clinic, Rochester, MN, (2) Stanford University, Stanford, CA |
| TH-IePD-TRACK 3-4 | Segmentation by Individualized Registration (SIR) for CBCT-Based Adaptive Radiation Therapy X Liang1*, J Chun2, H Morgan1, T Bai1, D Nguyen1, J Park1, S Jiang1, (1) Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, (2) Department of Radiation Oncology, Yonsei University, Seoul, KR |
| TH-IePD-TRACK 5-3 | Uncertainty in OSLD Dose Reading Due to CBCT-Based IGRT S Charyyev*, J Harms, Z Diamond, E Elder, B Ghavidel, M Axente, Emory University, Atlanta, GA |
| TU-B-TRACK 6-5 | Scatter Correction of 4D Cone Beam Computed Tomography for Time-Resolved Proton Dose Calculation: Porcine Lung Phantom Validation H Schmitz1*, M Rabe1, G Janssens2, J Dinkel3, S Rit4, K Parodi5, C Belka1,6, C Kurz1,5, F Kamp1,7, G Landry1,5, (1) Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany, (2) Ion Beam Applications SA, Louvain-la-neuve, Belgium, (3) Department of Radiology, University Hospital, LMU Munich, Munich, Germany, (4) Centre Leon Berard, Universite de Lyon, Lyon, France, (5) Department of Medical Physics, Ludwig-Maximilians-Universitaet Muenchen (LMU Munich), Garching (Munich), Germany, (6) German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany, (7) Department of Radiation Oncology, University Hospital Cologne, Cologne, Germany |
| TU-B-TRACK 6-7 | Beam Path Length From Isocenter to Skin On Cone Beam CT Images as An Adaptive Planning Indicator of Head and Neck Patients Undergoing Proton Therapy N Biswal*, B Zhang, J Molitoris, M Witek, B Yi, Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD |
| TU-C-TRACK 3-1 | A Deep Learning Technique for Projection Interpolation in CBCT Reconstruction K Lu1*, Z Zhang1, L Ren2, F Yin2, (1) Duke University, Durham, NC, (2) Duke University Medical Center, Durham, NC |
| TU-C-TRACK 3-3 | A Method for 4D Cone-Beam CT in Under 20 Seconds with New Generation Linacs S Blake*, O Dillon, R O'Brien, ACRF Image-X Institute, University of Sydney, Sydney, NSW, AU |
| TU-C-TRACK 3-4 | Dual-Encoder Convolutional Neural Network (DeCNN) for Average Image-Constrained 4D-CBCT Reconstruction Z Jiang1*, Z Zhang2, Y Chang3, F Yin4, L Ren5, (1) Duke Univeristy, Durham, NC, (2) Duke Univeristy, Durham, NC, (3) Duke University, Durham, NC, (4) Duke University, Durham, NC, (5) Duke University, Durham, NC |
| TU-C-TRACK 3-5 | Faster Safer Clearer 4DCBCT: Results of the ADAPT Clinical Trial O Dillon1*, B Lau1, T Reynolds1, P Keall1, S Vinod2, A Wallis3, S Smith4, J Sonke5, R O'Brien1, (1) University of Sydney, Sydney, NSW, AU, (2) University Of New South Wales, Sydney, NSW, AU,(3) Liverpool Hospital Sydney, Sydney, NSW, AU, (4) South Western Sydney Lhd, Sydney, NSW, AU, (5) Netherlands Cancer Institute, Amsterdam, NH, NL |
| TU-C-TRACK 3-6 | Rapid Low Dose 4DCBCT B Lau1*, O Dillon1, T Reynolds1, P Keall1, J Sonke2, S Vinod3,4, R O'Brien1, (1) ACRF Image X Institute, University of Sydney, Sydney, NSW, AU, (2) Netherlands Cancer Institute, Amsterdam, (3) University Of New South Wales, Sydney, NSW, AU, (4) Liverpool Hospital Sydney, Sydney, NSW, AU |
| TU-C-TRACK 3-7 | Reference-Free, Learning-Based Rigid Motion Compensation in Cone-Beam CT for Interventional Neuroradiology H Huang1*, J Siewerdsen1,2, W Zbijewski1, C Weiss2, T Ehtiati3, A Sisniega1, (1) The Johns Hopkins University, Baltimore, MD (2) Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD (3) Siemens Healthineers, Forchheim, Germany |
| TU-D-TRACK 3-3 | Multi-Resolution Residual Deep Neural Network for Generating Synthetic CT Images with High HU Accuracy and Structural Fidelity W WU1*,J Qu2, J Cai3, R Yang1, (1) Peking University Third Hospital, Beijing, China (2) Capital Medical University, Beijing, China (3) The Hong Kong Polytechnic University, Hong Kong, China |
| TU-EF-TRACK 4-2 | BEST IN PHYSICS (MULTI-DISCIPLINARY): Focused KV X-Rays for Radiotherapy and Imaging of Small Targets W Liu1*, N R Bennett1, Y Li1, L Shi1, Z Zhang1, H Yan2, W Sun3, E Graves1, L Xing1, C MacDonald3, A Wang1, (1) Stanford University, Stanford, CA, (2) Capital Medical University, Beijing, China, (3) University of Albany, SUNY, Albany, NY |
| TU-EF-TRACK 4-9 | Thoracic 4D Cone-Beam CT Registration Incorporating a Fan-Beam CT-Derived Feasible Motion Descriptor Y Sang*, D Ruan, Department of Radiation Oncology and Department of Bioengineering, University of California, Los Angeles, CA |
| TU-F-TRACK 3-2 | JACK FOWLER EARLY-CAREER INVESTIGATOR COMPETITION WINNER: Imaging From the Cervical to the Lumbar Spine with a Continuous Multi-Turn Reverse Helical 3D Cone-Beam CT Scan T Reynolds1*, Y Ma2, G Gang2, O Dillon1, T Russ3, W Wang2, T Ehtiati4, C Weiss2, N Theodore2, J Siewerdsen2, R O'Brien1, J Stayman2, (1) ACRF Image X Institute, University of Sydney, Sydney, NSW, AU (2) Johns Hopkins University, Baltimore, MD, (3) Heidelberg University, Mannheim, GER, (4) Siemens Healthineers, Forchheim, GER |
| TU-F-TRACK 3-4 | Ordered Subsets PWLS Reconstruction of Multi-Layer Detector MV-CBCT Data Using TIGRE M Jacobson1*, M Lehmann2, P Huber2, M Myronakis1, T Harris1, I Valencia Lozano1, R Fueglistaller2, C Williams1, D Morf2, R Berbeco1, (1) Brigham and Women's Hospital, Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA (2) Varian Medical Systems, Baden-Dattwil, Switzerland |
| TU-F-TRACK 3-6 | Weight-Bearing C-Arm Cone-Beam CT Using a Dynamic Range Reducer (DyRaR): Initial Prototype and Phantom Studies N Bennett1*, H Zhang2, K Mueller3, R Fahrig4, A Maier5, M Levenston1, G Gold1, A Wang1, (1) Stanford University, Stanford, CA, (2) Memorial Sloan Kettering Cancer Center, New York, NY, (3) Siemens Medical Solutions, San Francisco, CA, (4) Siemens Healthcare GmbH, Forchheim, Germany, (5) FAU University, Erlangen, Germany |
| TU-F-TRACK 6-5 | Improving Cone-Beam CT Based Organ Segmentation with Attention and Knowledge Transfer H Zhou1*, Y Min2, A Kishan2, S Yoon2, M Cao2, D Ruan12, (1) Department of Bioengineering, UCLA, Los Angeles, CA, (2) Department of Radiation Oncology, UCLA, Los Angeles, CA |
| TU-IePD-TRACK 1-3 | Development and Characterization of a Dynamic, Anatomically Realistic, Abdominal Phantom for Simulating Complex Deformable Motion in Cone-Beam CT Y Liu1*, H Huang1, J Siewerdsen1, W Zbijewski1, C Weiss1, T Ehtiati2, A Sisniega1, (1) Johns Hopkins University, Baltimore, MD, (2) Siemens Medical Solutions USA |
| TU-IePD-TRACK 3-1 | Deep Learning-Based Real-Time Volumetric Imaging Using Single CBCT Projections Y Lei*, Z Tian, T Wang, J Roper, A Kesarwala, E Schreibmann, K Higgins, J Bradley, T Liu, X Yang, Emory Univ, Atlanta, GA |
| TU-IePD-TRACK 3-5 | Initial Experience of CBCT-Based Adaptive Radiotherapy with Ethos for Postoperative Prostate SBRT H Morgan*, D Nguyen, A Garant, N Desai, M Folkert, R Hannan, B Cai, M Lin, S Jiang, J Park, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX |
| TU-IePD-TRACK 3-7 | CBCT-Based, Physicist-Driven Daily Adaptive Protocol for Locally Advanced Cervical Cancer: Improved Target Coverage and Reduced Margins in a 20-Minute Treatment Slot D Branco*, J Mayadev, K Moore, X Ray, UC San Diego, La Jolla, CA |
| TU-IePD-TRACK 4-1 | A Clinically Implemented Watchdog Program to Streamline Adaptive LINAC-Based Head and Neck Radiotherapy M Aristophanous*, E Aliotta, A Caringi, B Rochford, N Allgood, P Zhang, Y Hu, P Zhang, L Cervino, Memorial Sloan Kettering Cancer Center, New York, NY |
| WE-AB-TRACK 3-0 | Robotic X-Ray Systems: Emerging Platform for Improved Radiography, 3D Imaging, and Interventional Radiology T Reynolds1*, T Reynolds1*, G Gang2*, L Ritschl3*, (1) University of Sydney, Eveleigh, AU, (2) Johns Hopkins University, Baltimore, MD, (3) Siemens Healthineers |
| WE-AB-TRACK 3-1 | Adaptive Imaging - Syncing with Patients for Safer and Smarter 3D Imaging T Reynolds1*, T Reynolds1*, G Gang2*, L Ritschl3*, (1) University of Sydney, Eveleigh, AU, (2) Johns Hopkins University, Baltimore, MD, (3) Siemens Healthineers |
| WE-AB-TRACK 3-2 | Non-Circular Trajectories in Cone-Beam CT Imaging T Reynolds1*, T Reynolds1*, G Gang2*, L Ritschl3*, (1) University of Sydney, Eveleigh, AU, (2) Johns Hopkins University, Baltimore, MD, (3) Siemens Healthineers |
| WE-AB-TRACK 3-3 | Task Driven/Metal Artefact Reduction Orbits T Reynolds1*, T Reynolds1*, G Gang2*, L Ritschl3*, (1) University of Sydney, Eveleigh, AU, (2) Johns Hopkins University, Baltimore, MD, (3) Siemens Healthineers |
| WE-AB-TRACK 3-4 | Advanced Imaging using Multitom Rax T Reynolds1*, T Reynolds1*, G Gang2*, L Ritschl3*, (1) University of Sydney, Eveleigh, AU, (2) Johns Hopkins University, Baltimore, MD, (3) Siemens Healthineers |
| WE-D-TRACK 3-5 | Longitudinal QC Testing of CBCT and Helical MDCT Systems with a Unified Image Quality and Dose Phantom M Mahesh*, M Fernandez, A Uneri, J Boone, J Siewerdsen, Johns Hopkins Univ, Baltimore, MD |
| WE-IePD-TRACK 1-1 | Deep Learning Model for Scatter Correction in Dedicated Breast CT J Pautasso1*, M Caballo1, K Michielsen 1, I Sechopoulos12, (1) Dept. of Medical Imaging, Radboud University Medical Center, Nijmegen, NL. (2) Dutch Expert Centre for Screening (LRCB), Nijmegen, NL. |
| WE-IePD-TRACK 1-1 | An X-Ray Fluence Modulation Apparatus for Cone-Beam CT P Ghazi*, S Youssefian, T Ghazi, MALCOVA LLC, Baltimore, MD |
| WE-IePD-TRACK 1-3 | Online Bayesian Geometric Calibration of CBCT Systems O Dillon*, T Reynolds, R O'Brien, University of Sydney, Sydney, NSWAU, |
| WE-IePD-TRACK 1-3 | Correcting Cone-Beam CT for Patient-Generated Scatter Using Bayesian Monte Carlo Extrapolation S Blake*, O Dillon, R O'Brien, ACRF Image-X Institute, University of Sydney, Sydney, NSW, AU |
| WE-IePD-TRACK 1-4 | Gas Bubble Motion Artifact Reduction Through Simultaneous Motion Estimation and Image Reconstruction K Wang*, H Shao, Y Zhang, J Wang, UT Southwestern Medical Center, Dallas, TX |
| WE-IePD-TRACK 1-5 | Scatter Correction for Cone Beam CT Using Half-Beam Blocker and Local-Filtration Scatter Estimation H Cui*, X Jiang, Y Yang, University of Science and Technology of China, Hefei, China |
| WE-IePD-TRACK 1-6 | Reducing Scan Time and Dose: Motion Compensated Reconstruction for Adaptive 4DCBCT B Lau1*, O Dillon1, T Reynolds1, P Keall1, J Sonke2, S Vinod3,4, R O'Brien1, (1) ACRF Image X Institute, University of Sydney, Sydney, NSW, AU, (2) Netherlands Cancer Institute, Amsterdam, (3) University Of New South Wales, Sydney, NSW, AU, (4) Liverpool Hospital Sydney, Sydney, NSW, AU |
| WE-IePD-TRACK 1-7 | A Generative Adversarial Network (GAN)-Based Technique for Synthesizing On-Board Imaging Artifacts in the Extended Cardiac-Torso (XCAT) Phantoms Y Chang1*, Z Zhang1, Z Jiang1, Y Lai2, L Ren1, (1) Duke University, Durham, NC, (2) University of Texas Southwestern Medical Center, Dallas, TX |
| WE-IePD-TRACK 1-7 | A Geometry-Guided Deep Learning Technique for CBCT Reconstruction K Lu1*, L Ren2, F Yin2, (1) Duke University, Durham, NC, (2) Duke University Medical Center, Durham, NC |
| WE-IePD-TRACK 4-1 | IGRT-Based On-Line Dose Estimation in Proton Therapy S Flampouri1*, W LePain2, M McDonald1, (1) Emory University, Atlanta, GA, (2) Georgia Tech, Atlanta, GA |
| WE-IePD-TRACK 4-6 | A Novel Reconstruction Method for 3D Cone Beam CT Images in Proton Therapy Using RTK J Lindsay1*, A Bryce-atkinson1, M Taylor1,2, S Meara2,1, M Aznar1,2, M van Herk1,2, (1) The University of Manchester, Manchester, GB, (2) The Christie NHS Foundation Trust, Manchester, GB |