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Taxonomy: IM/TH- image segmentation: CT
| BReP-SNAP-I-9 | Building a Patient-Specific Model Using Transfer Learning for 4D-CBCT Augmentation L Sun*, Y Chang, Z Jiang, L Ren, Duke University Medical Center, Cary, NC |
| BReP-SNAP-I-11 | Comparison of a Deep Learning-Based CT Reconstruction Algorithm (AiCE) to Other Reconstruction Techniques in a Pediatric Population S Brady*, E Somasundaram, J Dillman, A Trout, Cincinnati Childrens Hospital Med Ctr, Cincinnati, OH |
| BReP-SNAP-I-12 | Cone-Beam CT Image Reconstruction with Spherical Harmonics T Shimomura*, A Haga, Tokushima UniversityTokushimaJP |
| BReP-SNAP-I-13 | Dedicated Breast CT: Comparative Evaluation of Multi-Scale Residual Dense Network and Residual Encoder-Decoder Network for Deep Learning-Driven Sparse-View Reconstruction Z Fu, H Tseng, S Vedantham*, A Karellas, A Bilgin, University of Arizona, Tucson, AZ |
| BReP-SNAP-I-17 | Dynamic Range Reducer for C-Arm Cone-Beam CT Acquisitions: Initial Prototype and Evaluation H Zhang1*, N Bennett2, S Hsieh3, K Mueller4, R Fahrig5, A Maier6, M Levenston7, G Gold8, A Wang9, (1) Stanford University, Stanford, CA, (2) Stanford University, Stanford, CA, (3) Mayo Clinic, Rochester, MN, (4) Siemens Medical Solutions Inc. (5) Siemens Healthcare GmbH (6) University of Erlangen-Nuremberg, (7) Stanford University, Stanford, CA, (8) Stanford University, Stanford, CA, (9) Stanford University, Stanford, CA |
| BReP-SNAP-I-20 | Evaluation of CT-Based Radiomics Features for Predicting Parameters Measured Using a Pulmonary Function Test Y Ieko1,2*, N Kadoya1, K Abe1,3, S Tanaka1, H Takagi4, T Kanai5, K Ichiji6, T Yamamoto1, H Ariga2, K Jingu1, (1) Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan, (2) Department of Radiation Oncology, Iwate Medical University School of Medicine, Iwate, Japan, (3) Department of Radiology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan, (4) Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan, (5) Department of Radiation Oncology, Yamagata University Faculty of Medicine, Yamagata, Japan, (6) Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai, Japan. |
| BReP-SNAP-I-23 | Impact of a High Power Tube On Optimal KVp Selection in Function of Patient Size G Van Gompel*, N Buls, H Nieboer, J De Mey, Universitair Ziekenhuis Brussel, Brussels, Belgium, BE |
| BReP-SNAP-I-24 | Implementation of CT Protocol Management Software to Detect Deviations From Master Protocols K Little1*, J Jacobs2, N Fitousi2, M Robins1, J Carpenter1, A Rupe1, D Hintenlang1, (1) Ohio State Univ, Columbus, OH, (2) Qaelum NV, Leuven, BE |
| BReP-SNAP-I-28 | Integrated Intensity-Based Quantification of Small Airway Dimensions Using Computed Tomography Y Zhao, S Molloi*, University of California-Irvine, Irvine, CA |
| BReP-SNAP-I-31 | Machine Learning-Based Prediction of Contrast Enhancement in Transcatheter Aortic Valve Replacement CT E Macdonald*, Z Qi, N Bevins, Henry Ford Health System, Detroit, MI |
| BReP-SNAP-I-34 | Noise Power Spectrum Analysis E mckenzie*, D Gauntt, UAB Medical Center, Birmingham, AL |
| BReP-SNAP-I-35 | Orthogonal Limited Arc Scan Combinations of Cone-Beam CT Reconstructed Iteratively to Reduce Photon Starvation Artifacts Caused by Pedicle Screws M Hermansen*, S Banks, M Arreola, A Entezari, F Bova, University of Florida, Gainesville, FL |
| BReP-SNAP-I-43 | Reducing the Number of Projections in CT Imaging Using Domain-Transform Manifold Learning A Cramer1*, N Koonjoo2, B Zhu2, R Gupta3, M Rosen2, (1) Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, (2) MGH/Martinos Center for Biomedical Imaging, Boston, MA, (3) Massachusetts General Hospital, Boston, MA |
| BReP-SNAP-I-44 | Scatter Correction of Sparsely Acquired 4D Cone-Beam CT by Bayesian Monte Carlo Extrapolation S Blake*, O Dillon, R O'Brien, ACRF Image-X Institute, The University of Sydney, Eveleigh, NSW, Australia |
| BReP-SNAP-I-46 | Self-Supervised Metal Artifact Reduction in X-Ray Computed Tomography by Joint Sinogram Completion and Image Refinement L Yu*, Z Zhang, L Xing, Stanford Univ School of Medicine, Stanford, CA |
| BReP-SNAP-I-48 | Spectral Inconsistency Analysis On a CdTe Photon-Counting Detector Binxiang Qi*, Hewei Gao, Tsinghua University, Haidian Dist, 11CN, |
| BReP-SNAP-I-58 | Variations in Radiomics Features of a Multi-Texture Phantom Introduced by Deep Learning Iterative Reconstruction Algorithms N Baughan*, J P Cruz-Bastida, H Al-Hallaq, I Reiser, The University of Chicago, Chicago, IL |
| BReP-SNAP-I-59 | Reconstructing C-Arm Cone-Beam CT Knee Scans Using An Open-Source GPU-Based Toolbox H Zhang1*, K Mueller2, R Fahrig3, A Maier4, M Levenston5, G Gold6, A Wang7, (1) Stanford University (2) Siemens Medical Solutions Inc. (3) Siemens Healthcare GmbH (4) University of Erlangen-Nuremberg (5) Stanford University (6) Stanford University (7) Stanford University, Stanford, CA |
| BReP-SNAP-M-15 | An Adversarial Machine Learning Framework and Biomechanical Model Guided Approach for Generating 3D Lung Tissue Elasticity From Low Dose End-Exhalation CT A Santhanam1, B Stiehl1*, M Lauria1, I Barjaktarevic1, S Hsieh2, D Low1, (1) University of California, Los Angeles, Los Angeles, CA, (2) Mayo Clinic, Rochester, MN |
| BReP-SNAP-M-16 | Assessing Inter and Intrafraction Target Motion in Lung SBRT Using Deformable Image Registration J Liang*, D Lack, Q Liu, R Sandhu, L Benedetti, C Stevens, D Yan, Beaumont Health, Royal Oak, MI |
| BReP-SNAP-M-21 | Automatic CT Segmentation for Radiotherapy Treatment Planning: How Good Is Good Enough? W S Ingram*, L Dong, University of Pennsylvania, Philadelphia, PA |
| BReP-SNAP-M-25 | Automatic Tumor and Multi-Organ Segmentation Technique in CT Based On Deep Learning for Radiation Therapy After Breast-Conserving Surgery J Lee1*, H Cho1*, S Ye1, D Choi2, W Park2, H Kim2, W Cho2, H Kim2, (1) Seoul National University, Seoul, 41, KR, (2) Samsung Medical Center, Seoul, 41, KR (*J Lee and H Cho contributed equally to this work.) |
| BReP-SNAP-M-37 | Combining Delta-Radiomics and Clinical Biomarkers Based On KNN-PCA Classification to Improve Treatment Outcome Prediction for Pancreatic Cancer H Nasief1*, W Hall2, C Zheng3, S Tsai4, B Erickson5, X Li6, (1) Medical College of Wisconsin, Milwaukee, WI, (2) Medical College of Wisconsin, Milwaukee, WI, (3) University of Wisconsin Milwaukee, Milwaukee, WI, (4) Medical College of Wisconsin, Milwaukee, WI, (5) Medical College of Wisconsin, Milwaukee, WI, (6) Medical College of Wisconsin, Milwaukee, WI |
| BReP-SNAP-M-45 | CT-Based Convolutional-Neural-Network Segmentation of HCC Regions with Lung-Cancer-Based Transfer Learning N Nagami12*, H Arimura2, J Nojiri3, R Nakano2, K Ninomiya2, M Ogata1, S Takita1, S Kitamura1, H Irie3, (1) Saga university hospital, Saga-shi, Saga, JP, (2) Kyushu University, Fukuoka, Fukuoka, JP, (3) Saga University, Saga-shi, Saga, JP, |
| BReP-SNAP-M-46 | CT-Based Radiomics Analysis: A New Imaging Biomarker in Chronic Obstructive Pulmonary Disease? R Au1*, V Liu1, M Koo1, W Tan2, J Bourbeau3, J Hogg2, H Coxson2, M Kirby1, (1) Ryerson University, Toronto, Ontario, Canada, (2) Centre For Heart Lung Innovation, University Of British Columbia, Vancouver, British Columbia, Canada, (3) McGill University, Montreal, Quebec, Canada |
| BReP-SNAP-M-49 | Deep Learning Augmented Proton Portal Imaging: A Phantom Study S Charyyev1*, Y Lei2, J Harms3, B Eaton4, M McDonald5, W Curran6, T Liu7, J Zhou8, R Zhang9, X Yang10, (1) Emory University, Atlanta, GA, (2) Emory University, Atlanta, GA, (3) Emory University, Atlanta, GA, (4) Emory University, Atlanta, ,(5) Emory University, Atlanta, GA, (6) Emory University, Atlanta, GA, (7) Emory University, Atlanta, GA, (8) Emory University, Atlanta, GA, (9) Dartmouth College, Lebanon, NH, (10) Emory University, Atlanta, GA |
| BReP-SNAP-M-54 | Deep-Learning-Based Autosegmentation Outperforms Atlas-Based Autosegmentation in a Clinical Cohort of Breast Cancer Patients JJE Kleijnen1*, A Akhiat2, MS Hoogeman1, SF Petit1, (1) Department of radiotherapy, Erasmus MC, Rotterdam, the Netherlands, (2) Elekta AB, Stockholm, Sweden |
| BReP-SNAP-M-58 | Development and Implementation of a Knowledge Base for Automated Segment Review E Pryser*, M Schmidt, F Reynoso, W Smith, Washington University in St. Louis, St. Louis, MO |
| BReP-SNAP-M-65 | Evaluation of a Localized Correlation Based Predictive Metric as a Decision Making Tool in Online Image Guidance and Offline Adaptive Prostate Radiotherapy A Gopal*, B Zhang, G Lasio, S Lee, B Yi, Univ. of Maryland School Of Medicine, Baltimore, MD |
| BReP-SNAP-M-66 | Evaluation of Lung Cancer SBRT Plans with Large Daily Tumor Motion Variations Using Daily 4D-CBCT S Balik1*, C Yurtsever2, T Zhuang3, P Qi2, G Videtic2, K Stephans2, P Xia2, (1) University of Southern California, Los Angeles, CA, (2) The Cleveland Clinic Foundation, Cleveland, OH,(3) Texas Oncology, Keller, TX |
| BReP-SNAP-M-67 | Evaluation of Proton Computed Tomography Detected by Multiple-Layer Ionization Chamber and Strip Chambers Through Monte Carlo Simulation with Human Head Phantoms X Chen*, T Zhao, R Liu, B Sun, F Reynoso, S Mutic, T Zhang, Washington University School of Medicine, Saint Louis, MO |
| BReP-SNAP-M-79 | GPU-Based Acceleration of MV-CBCT Simulation M Shi1,2*, M Myronakis2, M Jacobson2, M Lehmann3, D Ferguson2, P Baturin4, P Huber3, R Fueglistaller3, T Harris2, I Valencia Lozano2, C Williams2, D Morf3, R Berbeco2, (1) University of Massachusetts Lowell, Lowell, MA, (2) Brigham and Women's Hospital & Dana Farber Cancer Institute & Harvard Medical School, Boston, MA, (3) Varian Medical Systems, Baden, Switzerland, (4) Varian Medical Systems, Palo Alto, CA |
| BReP-SNAP-M-81 | High-Precision Dosimetry in Yttrium-90 Radioembolization Through Post-Procedural CT Imaging of Radiopaque Microspheres in a Porcine Model C Henry1*, M Strugari1,2, G Mawko1,3,4,6, K Brewer1,2,5,R Abraham4,7, A Syme1,3,6, (1) Department of Physics, Dalhousie University, Halifax, NS, CA, (2) Biomedical Translational Imaging Centre (BIOTIC), Halifax, NS, CA, (3) Department of Medical Physics, Nova Scotia Health Authority, Halifax, NS, CA, (4) Department of Diagnostic Imaging and Interventional Radiology, Dalhousie University, Halifax, NS, CA, (5) Department of Biomedical Engineering, Dalhousie University, Halifax, NS, CA, (6) Department of Radiation Oncology, Dalhousie University, Halifax, NS, CA, (7) ABK Biomedical Inc., Halifax, NS, CA |
| BReP-SNAP-M-91 | Incorporating GTV Information in a Multi-Stage Process to Improve Automatically Generated Field Apertures for Rectal Cancer Radiotherapy K Huang*, P Das, L Zhang, M Amirmazaheri, C Nguyen, D Rhee, T Netherton, S Beddar, T Briere, D Fuentes, E Holliday, L Court, C Cardenas, MD Anderson Cancer Center, Houston, TX |
| BReP-SNAP-M-95 | Inter-Vendor Compatibility and Transfer Learning for MR-Based Synthetic CT Deep Learning Models for Domain Adaptation P Klages*, N Tyagi, H Veeraraghavan, Memorial Sloan-Kettering Cancer Center, New York, NY |
| BReP-SNAP-M-108 | Off-Line Treatment Monitoring of Head and Neck Radiotherapy Using Daily Cone-Beam Computed Tomography: A Preliminary Study S Lee1*, B Zhang1, G Lasio1, A Gopal1, I Lee2, H Xu1, S Chen1, B Yi1, (1) University of Maryland School of Medicine, Baltimore, MD, (2) University of Maryland, College Park, MD |
| BReP-SNAP-M-111 | Patient-Specific Deep Learning Model for Deformable Image Registration s amini*, Z Jiang, Y Chang, Y Mowery, L Ren, Duke University Medical Center, Cary, NC |
| BReP-SNAP-M-134 | Segmentation of Invisible Target Volume with Estimated Uncertainties for Post-Operative Prostate Cancer Radiotherapy A Balagopal*, D Nguyen, M Lin, H Morgan, N Desai, R Hannan, A Garant, Y Gonzalez, A Sadeghnejad Barkousaraie, S Jiang, UT Southwestern Medical Center, Dallas, TX |
| BReP-SNAP-M-140 | Synthetic Digital Reconstructed Radiograph for MR-Only Robotic Radiosurgery with Deep Convolutional Adversarial Networks G Szalkowski1*, D Nie1, T Zhu1, M Dance1, X Xu1, A Wang1, T Royce1, R Chen2, D Shen1, J Lian1, (1) University of North Carolina at Chapel Hill, Chapel Hill, NC, (2) University Of Kansas |
| BReP-SNAP-M-143 | Tumor Motion in Locally Advanced Lung Cancer: A Study of 46 Patients with 4DCT and 4DCBCT L Su1*, N O'donnell2, K Ding1, R Hales1, (1) Johns Hopkins University, Baltimore, MD, (2) Johns Hopkins Hospital |
| BReP-SNAP-M-146 | Uncertainty-Aware Reconstructed Image Correction for Proton Computed Tomography Using Bayesian Deep Learning Y Nomura1*, S Tanaka1, J Wang1, H Shirato1, S Shimizu1, L Xing1,2, (1) Hokkaido University, Sapporo, Hokkaido, Japan, (2) Stanford University, Palo Alto, CA |
| BReP-SNAP-T-70 | Gated Proton Imaging Using Fiducial Marker and X-Ray Fluoroscopy S Tanaka*, N Miyamoto, Y Shimada, T Yoshimura, S Takao, Y Matsuo, S Shimizu, T Matsuura, Hokkaido University, Sapporo, Hokkaido, JP |
| MO-CD-TRACK 1-7 | A Generative Adversarial Network (GAN)-Based Technique for Synthesizing Realistic Respiratory Motion in the Extended Cardiac-Torso (XCAT) Phantoms Y Chang1*, Z Jiang2, K Lafata3, Z Zhang4, P Segars5, J Cai6, F Yin7, L Ren8, (1) Duke University, Durham, NC, (2) Duke Univeristy, Durham, NC, (3) Duke University, Durham, NC, (4) Duke Univeristy, Durham, NC, AF, (5) Duke Univ, Durham, NC, (6) Hong Kong Polytechnic University, Hong Kong, HK, CN, (7) Duke University, Durham, NC, (8) Duke University Medical Center, Cary, NC |
| MO-E-TRACK 2-4 | Experimental Comparison of Particle Vs Photon Imaging for Relative Stopping Power Prediction E Baer1*, L Volz2, C-A Collins-Fekete1, S Brons3, A Runz4, R Schulte5, J Seco2, (1) University College London, London, GB, (2) Biomedical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, DE, (3) Heidelberg Ion Beam Therapy Center (HIT), Heidelberg, DE, (4) Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany, (5) Loma Linda University, Loma Linda, CA, USA, (2) Medical Physics in Radiation Oncology, German Cancer Research Center, Heidelberg, DE |
| MO-E-TRACK 2-5 | Prescribing Image Noise Using Dynamic Fluence Field Optimization: Experimental Results Using a Pre-Clinical Proton CT Scanner J Dickmann*1, C Sarosiek2, V Rykalin3,4, M Pankuch3, S Rit5, N Detrich3,6, G Coutrakon2, RP Johnson7, RW Schulte8, K Parodi1, G Landry9,10,1, G. Dedes1, (1) Ludwig-Maximilians-Universitaet Muenchen, Munich, Germany, (2) Northern Illinois University, DeKalb, IL, (3) Northwestern Medicine Chicago Proton Center, Warrenville, IL, (4) ProtonVDA Inc., Naperville, IL, (5) Universite de Lyon, Lyon, France, (6) IBA Group, Louvain-La-Neuve, Belgium, (7) U.C. Santa Cruz, Santa Cruz, CA, (8) Loma Linda University, Loma Linda, CA, (9) University Hospital, LMU Munich, Munich, Germany, (10) German Cancer Consortium (DKTK), Munich, Germany |
| MO-F-TRACK 2-3 | Intensity-Based Thresholding of Probability Maps in Deep-Learning-Based Segmentation N Bice*, N Kirby, R Li, T Bahr, J Rembish, M Agarwal, S Stathakis, M Fakhreddine, UT Health San Antonio, San Antonio, TX |
| MO-F-TRACK 2-4 | OARnet: Organs-At-Risk Delineation in Head and Neck CT Images M H Soomro*, H Nourzadeh, V Leandro Alves, W Choi, J Siebers, University of Virginia, Charlottesville, VA |
| PO-GeP-I-2 | A 5D Motion Phantom for Simulating Simultaneous Cardiac and Respiratory Motions X Wu*, S Goddu, A Curcuru, H Gach, H Li, D Yang, Washington University in St. Louis, St. Louis, MO |
| PO-GeP-I-3 | A Body Mass Index-Based Method for Size-Specific Dose Estimates (SSDE) in Adults D Yan1,2*, B Chen2, W Lu1, J Qiu1, L Shi1, (1) Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, CN (2) Hwamei Hospital, university Of Chinese Academy Of Sciences, Ningbo, CN |
| PO-GeP-I-6 | A Deep Learning-Based End-To-End CT Reconstruction Method K Lu*, L Ren, F Yin, Duke University, Durham, NC |
| PO-GeP-I-7 | A Deep-Learning Neural Network Based Reconstruction Algorithm for Sparse-View CT I Herrera, P Mandke, W Feng, G Cao*, Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA |
| PO-GeP-I-8 | A Five Year Review of Established Local DRLs for Adult CT Examinations in Nova Scotia S Schofield1, E Tonkopi1,2*, (1) Nova Scotia Health Authority, Halifax, NS, CA, (2) Dalhousie University, Halifax, NS, CA |
| PO-GeP-I-9 | A Machine Learning Based Automatic Lung Lobe Segmentation in Fast Helical Free Breathing CT Scans L Naumann*, B Stiehl, M Lauria, R Pande, S Suthar, H Sundaram, S Narayanan, S Siva, D Low, A Santhanam, UCLA, Los Angeles, CA |
| PO-GeP-I-10 | A Method for Assessing Confidence of Dual-Contrast Photon-Counting CT Material Quantification C Leary*, T Griglock, Oregon Health & Science Univ, Portland, OR |
| PO-GeP-I-12 | A New Approach for Coronary Calcium Scoring at Reduced Dose with Lower Tube Voltage -A Simulation Study Based On Reference Image Acquisitions Y Zhou*, D Zhang, W Paz, Cedars-Sinai Medical Center, Los Angeles, CA |
| PO-GeP-I-15 | A Phantom-Based Assessment of Low-Contrast Performance Comparing Iterative Reconstruction Algorithms in CT S Leon*, C Schaeffer, E Olguin, M Arreola, University of Florida, Gainesville, FL |
| PO-GeP-I-19 | A Semi-Automatic Cardiac Substructure Segmentation Platform for Radiotherapy Planning CT E Zhang1*, J Li2, R Timmerman3, P Alluri4 , M Chen5, W Lu6, X Gu7, (1) The University of Texas Southwestern Medical Ctr, Dallas, TX, (2) Guangdong General Hospital, Guangzhou, CN, (3) UT Southwestern Medical Center, Dallas, TX, (4) UT Southwestern Medical Center, Dallas, TX, (5) UT Southwestern Medical Center, Dallas, TX, (6) UT Southwestern Medical Center, Dallas, TX, (7) UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-I-21 | A Study On Fast CBCT J Dai1, M Li2*, (1) National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, Beijing, CN, (2) , |
| PO-GeP-I-22 | Accuracy of Dual-Energy CT Virtual Non-Contrast/unenhanced and Material Density Images: A Phantom Study B Li*, M Pomerleau, A Gupta, J Soto, S Anderson, Boston University Medical Center, Boston, MA |
| PO-GeP-I-24 | Accurate 3D Stopping-Power Ratio Estimation by Statistical Image Reconstruction From Dual Energy CT Sinogram Data Exported From a Commercial Multi-Slice CT Scanner M Medrano1*, T Ge1, D Politte2, J Williamson2, T Zhao2, R Liu2, R Liao1, M Porras-Chaverri4, B Whiting3, J O'Sullivan 1 (1) Washington University in St. Louis, St. Louis, MO,(2) Washington University School of Medicine, St. Louis, MO, (3) University of Pittsburgh, Pittsburgh, PA, (4) Universidad de Costa Rica, San Jose, SJ |
| PO-GeP-I-31 | An Investigation of Using a Single CT Image to Assess Size-Specific Dose Estimates (SSDE) A Abuhaimed1*, C Martin2, (1) King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia, (2) University of Glasgow, Glasgow, UK |
| PO-GeP-I-38 | Automatic CT Air Bubble Artifact Detection in Routine QC Images A Verones1,2*, S Wong3, J Cheung4, T Lee4, T Bjarnason1,5,6, (1) University of British Columbia, Vancouver, CA, (2) Advanced Quality Systems, Inc., Vancouver, CA, (3) Tuen Mun Hospital, HK, (4) Prince of Wales Hospital, HK, (5) University of British Columbia, Okanagan, Kelowna, CA, (6) Interior Health, Kelowna, CA |
| PO-GeP-I-42 | Building a Comprehensive 4DCT QA Program M Camborde*, T Karan, K Luchka, BC Cancer - Vancouver Centre, Vancouver, BCCA, |
| PO-GeP-I-51 | Clinical Decision Making in CT: Risk Assessment Comparison Across 12 Risk Metrics in Patient Populations F Ria*, W Fu, J Hoye, P Segars, A Kapadia, E Samei, RAI Labs, Duke University Health System, Durham, NC |
| PO-GeP-I-57 | Comparison and Validation of Noise Magnitude Estimation Methods From Patient CT Images F Ria*, T Smith, E Abadi, J Solomon, E Samei, Duke University Health System, Durham, NC |
| PO-GeP-I-58 | Comparison of Image Noise in Dual Energy CT Images Reconstructed Using Filtered Back-Projection, Hybrid Iterative Reconstruction, and Deep Learning Methods P Prakash*, B Nett, J Tang, GE Healthcare Technologies, Waukesha, WI |
| PO-GeP-I-59 | Comparison of IMAR and AiMAR Techniques for Metal Artifact Reduction in CT-Guided Microwave Ablations M Jacobsen*, E Thompson, X Liu, B Odisio, E Cressman, R Layman, UT MD Anderson Cancer Center, Houston, TX |
| PO-GeP-I-62 | Compatibility Evaluation of Dose Modulations Among Various CT Scanners J James1*, G Anthony2, Y Liang3, (1) Indiana University, Imaging Sciences, Indianapolis, IN, (2) Indiana University, Imaging Sciences, Indianapolis, IN, (3) Indiana University, Imaging Sciences, Indianapolis, IN |
| PO-GeP-I-65 | Correlation of Body Mass Index (BMI) and Water Equivalent Diameter (Dw) Used for Size-Specific Dose Estimates (SSDE) A Abuhaimed1*, C Martin2, (1) King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia, (2) University of Glasgow, Glasgow, UK |
| PO-GeP-I-67 | CT Exam Alert Level Setting, Evaluation, and Case Examples R Lamoureux*, D Sandoval, G Chambers, R Selwyn, University of New Mexico, Albuquerque, NM |
| PO-GeP-I-70 | DAP Measurements in Dental/Maxillofacial Cone Beam CT E Gingold1*, A Stratis2, D Mihailidis3, (1) Thomas Jefferson University, Philadelphia, PA, (2) KU Leuven/UZ Leuven, Leuven, BE, (3) University of Pennsylvania, Philadelphia, PA |
| PO-GeP-I-74 | Dental and Maxillofacial Cone Beam Computed Tomography Dose Index in Full-Fan and Full-Scan Mode S Jia*, L Zhang, Y Xing, H Gao, Tsinghua University, Haidian Dist, 11CN, |
| PO-GeP-I-77 | Detectability of Urinary Stone Sizes and Compositions by Various Scanning Parameters in Dual Energy CT J Shin, J Shin*, Asan Medical Center |
| PO-GeP-I-85 | Development of An Optical/micro-CT Specimen Imaging System Optimized for Breast Conserving Surgery B Maloney1*, S Streeter1, M Jermyn1, M Gesner2, P Travis2, J Kempner2, J Meganck2+, K Paulsen1,3,4, B Pogue1,3,4, (1) Thayer School of Engineering, Dartmouth College, Hanover, NH, (2) PerkinElmer, Hopkinton, MA, (3) Geisel School of Medicine, Dartmouth College, Hanover, NH, (4) Norris Cotton Cancer Center, Lebanon, NH (+) Currently: Boston Scientific, Marlborough, MA |
| PO-GeP-I-93 | Dose Comparison of Elekta Infinity 4D Symmetry Scan Versus Cone Beam CT I Koistinen1*, M Koistinen2, (1) University of Maine, Orono, ME, (2) CHEM Center for Rad Oncology, Millis, MA |
| PO-GeP-I-96 | Dual Energy CT Protocol Optimization for Increased Blood Detectability of Virtual Non-Contrast Images in a Single Source Dual Energy CT System: A Phantom Study C Olguin*, S Leon, I Barreto, R De Jesus, M Arreola, University of Florida College of Medicine, Gainesville, FL |
| PO-GeP-I-99 | Effect of Contrast Agent Administration On Water Equivalent Diameter in CT B Viggiano*, T Szczykutowicz, University Wisconsin-Madison, Madison, WI |
| PO-GeP-I-102 | Enhanced 4DCT Combined with Image Histology Screening and Quantitative Analysis of Left Ventricular Myocardial Function Changes and Stability Characteristics M Su*, G Gong, Y Yin, Shandong Cancer Hospital Affiliated to Shandong University, Jinan, CN |
| PO-GeP-I-106 | Estimation of 3D Imaging Dose for KV-MV-STR CBCT M Shi1,2*, M Jacobson2, M Myronakis2, D Ferguson2, M Lehmann3, P Baturin4, T Harris2, P Huber3, R Fueglistaller3, I Valencia Lozano2, C Williams2, D Morf3, R Berbeco2, (1) University of Massachusetts Lowell, Lowell, MA, (2) Brigham and Women's Hospital & Dana Farber Cancer Institute & Harvard Medical School, Boston, MA, (3) Varian Medical Systems, Baden, Switzerland, (4) Varian Medical Systems, Palo Alto, CA |
| PO-GeP-I-113 | Evaluation of Dual-Energy CT Reconstructed Virtual Monoenergetic Images for Radiation Therapy Treatment Planning B Broekhoven*, W Godwin, D McDonald, Medical University of South Carolina, Charleston, SC |
| PO-GeP-I-119 | Evaluation of Synthetic CT Generation From CBCT Using a Deep Learning Model A Haidari1,2*, D Granville2, E Ali1,2, (1) Carleton University, Ottawa, ON, CA, (2) The Ottawa Hospital Cancer Centre, Ottawa, ON, CA |
| PO-GeP-I-123 | Feasibility of Using Average Intensity Projection of 4D In-Treatment CBCT for Patient Setup Verification J Kim*, C Hong, K Keum, J Kim, Yonsei University College of MedicineSeoulKR |
| PO-GeP-I-133 | Image Quality in the Slice-Plane of Half-Reconstructed Computed Tomography in Filtered Back Projection and Iterative Reconstruction Methods N Tsuda1*, K Mitsui2, S Oda3, N Tanaka4, H Aibe5, (1) Division of Radiology, Saga-ken Medical Centre Koseikan, Saga-shi, ,JP, (2) Division of Radiology, Saga-ken Medical Centre Koseikan, Saga-shi, ,JP, (3) Division of Radiology, Saga-ken Medical Centre Koseikan, Saga-shi, ,JP, (4) Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka-shi, ,JP, (5) Department of Radiology, Saga-ken Medical Centre Koseikan, Saga-shi, ,JP |
| PO-GeP-I-135 | Impact of a 3D Convolution Neural Network Method On Liver Segmentation: An Accuracy and Time-Savings Evaluation NM Cole1*, H Wan1, J Niedbala2, YK Dewaraja3, A Kruzer1, D Pittock1, C Halley1, AS Nelson1, (1) MIM Software Inc., Cleveland, OH, (2) Michigan Medicine, Ann Arbor, MI, (3) University of Michigan, Ann Arbor, MI |
| PO-GeP-I-138 | Impact of Scanner Speed On the Rate of Radiologist Complaint of Motion Artifact On Pediatric Body CT M Lipford*, T Szczykutowicz, University Wisconsin-Madison, Madison, WI |
| PO-GeP-I-139 | Improvement of Image Quality Using Image-Domain Multi-Material Decomposition Framework for Dual-Energy CT H Lee*, H Kim, S Choi, Yonsei University, Wonju, GangwonKR, |
| PO-GeP-I-140 | Influence of Water Equivalent Diameter (Dw) Variability Inside the Scan Area On Size-Specific Dose Estimates (SSDE) A Abuhaimed1, C Martin2*, (1) King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia, (2) University of Glasgow, Glasgow, UK |
| PO-GeP-I-141 | Initial Empirical Investigation of Photon Counting Circuits Based On Polycrystalline Silicon TFTs AK Liang*, M Koniczek, Y El-Mohri, Q Zhao, LE Antonuk, University of Michigan, Ann Arbor, MI |
| PO-GeP-I-147 | Machine Learning Based On CT Radiomic Features Can Predict Residual Tumor From Radiation Changes in Head and Neck Cancer Patients Treated with Definitive Chemoradiotherapy E Florez*, T Thomas, C M. Howard, H Khosravi, J Storrs, S Lirette, A Fatemi, University of Mississippi Medical Center, Jackson, MS |
| PO-GeP-I-148 | Matching Convolution Kernels and Iterative Reconstruction for Quantitative Accuracy and Noise Power Spectrum in Dual-Layer and Dual-Source Spectral CT G Anthony*, Y Liang, Indiana University, Indianapolis, IN |
| PO-GeP-I-149 | Metal Artifact in CBCT and the Impact On Dental Implant Planning A Ismail, N L Ford*, University of British Columbia, Vancouver, BC, CA |
| PO-GeP-I-150 | Metal Artifact Reduction Algorithms for CT in the Trauma Setting: Do They Take Too Long? S Rose*, M Lipford, N Stabo, C Bartels, M Lubner, T Szczykutowicz, University Wisconsin-Madison, Madison, WI |
| PO-GeP-I-151 | Modification of the Vendor Computed Tomography Stray Radiation Data Using Pediatric Patient Parameters for Shielding Design Weiyuan Wang*, Victor Garcia, University of Oklahoma Health Science Center, Oklahoma City, OK |
| PO-GeP-I-155 | Non-Circular Source-Detector Trajectories Suitable for Limited Angle and Low-Dose CBCT-Based Interventions S Hatamikia1*, A Biguri2, G Kronreif3, J Kettenbach4, T Russ5, W Birkfellner6, (1) Austrian Center for Medical Innovation and Technology, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria, AT, (2) Institute of Sound and Vibration Research, University of Southampton, United Kingdom(3) Austrian Center Medical Innovation and Technology, Wiener Neustadt, ,AT, (4)Department of Diagnostic and Interventional Radiology and Nuclear Medicine, Wiener Neustadt, Austria(5)Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University,Germany(6) Medical University Vienna, Vienna, 9, AT |
| PO-GeP-I-157 | NRsim: Normal Resolution Simulations Using High Resolution Acquisitions On a Commercial CT Scanner A Hernandez1*, D Shin2, C Abbey3, J Seibert1, N Akino4, T Goto4, J Vaishnav2, K Boedeker4, J Boone1, (1) University of California Davis, Sacramento, CA, (2) Canon Medical System USA, Tustin, CA, (3) University of California Santa Barbara, Santa Barbara, CA, (4) Canon Medical Systems Corporation, Otawara, Japan |
| PO-GeP-I-159 | Optimization and Validation of An Automatic Noise Measurement Algorithm for Clinical CT Image Data M Ahmad*, A Thomas, M Jacobsen, R Layman, K Brock, UT MD Anderson Cancer Center, Houston, TX |
| PO-GeP-I-162 | Optimizing Imaging Angles in CBCT Z Hui*, O Dillon, University of Sydney, Sydney, NSW, AU |
| PO-GeP-I-171 | Projection Acquisition Condition of Slow Gantry Rotation for Respiratory Correlated 4D Inverse Geometry Computed Tomography K Kim1*, D Shin1, T Kim2, J Chung3, S Kang1, W Cho4, T Suh1, (1) Catholic University of Korea, Seoul, ,KR, (2) Proton Therapy Center, National Cancer Center, Goyang-si, Gyeonggi-do, KR, (3) Seoul National University Bundang Hospital, Seongnam, 41, KR, (4) Boramae Medical Center, Seoul, ,KR, (7) Catholic Univ Medical College, Seoul, ,KR |
| PO-GeP-I-175 | Quantification of Small Airway Dimensions Using High Resolution Computed Tomography: A Phantom Study Y Zhao1*, A Hernandez2, J Boone2,3, S Molloi1, (1) University of California-Irvine, Irvine, CA, (2) University of California-Davis, Sacramento, CA, (3) UC Davis Medical Center, Sacramento, CA |
| PO-GeP-I-176 | Quantification of the HU Variation On KV CBCT for Direct Dose Calculation in Adaptive Radiotherapy N Givehchi*, A Strzelecki, M Lehmann, M Plamondon, S Scheib |
| PO-GeP-I-178 | Quantitative Evaluation of Image Quality of Deep-Learning-Based CT Reconstruction Using Structural SIMilarity (SSIM) K Yang*, A Parakh, R Gupta, A Kambadakone, X Li, B Liu, Massachusetts General Hospital, Harvard Medical School, Boston, MA |
| PO-GeP-I-180 | Quantitative Image Guided DECT Interventions: A Potential New Theranostic for Thermochemical Ablation E Thompson*, M Jacobsen, R Layman, E Cressman, UT MD Anderson Cancer Center, Houston, TX |
| PO-GeP-I-183 | Radiation Risk Assessment of Using Gold Nanoparticles as Contrast Agent in Image-Guided Radiotherapy: A Monte Carlo Study J Chow1*, D Mututantri-bastiyange2, (1) Princess Margaret Cancer Centre, Toronto, ON, CA, (2) Ryerson University, Toronto, ON, CA |
| PO-GeP-I-188 | Research Progress On Radiation Dose Optimization in CT Examination of Children in China D Xing1, W Lu1, L Jing2*, W Lu1, (1) Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, Shandong Province, CN, (2) Taian Tumor Hospital, Taian, Shandong Province, CN |
| PO-GeP-I-193 | Spatial Resolution Improvement with Unsupervised Estimation of Non-Ideal Focal Spot Effect for Computed Tomography Z Zhang*, X Li, L Yu, X Liang, L Xing, Stanford Univ School of Medicine, Stanford, CA |
| PO-GeP-I-196 | Stochastic Backprojection for Accelerated Model-Based Iterative 3D Image Reconstruction A Sisniega1*, J Stayman1, S Capostagno1, C Weiss1, T Ehtiati2, J Siewerdsen1, (1) Johns Hopkins University, Balitmore, MD, (2) Siemens Healthineers, Forchheim, Germany |
| PO-GeP-I-207 | The Design and Fabrication of a Dynamic Anthropomorphic Thorax Phantom Z Xu1*, Z Sun1, X Shi2, C Lin3, W Lu1, W Lu1, J Qiu1, L Shi1, (1) Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, CN, (2) Shengli College, China university of petroleum, Dongying, CN, (3) Taian Disabled soldiers' Hospital of Shandong Province, Taian, CN |
| PO-GeP-I-208 | The Effect of Reconstruction and Volume Preset Parameters On Low-Contrast Visibility for KV CBCT H Lee1, M Goss2*, D Pavord3, S Palefsky4, J Sohn5, (1) Allegheny Health Network, Pittsburgh, PA, (2) Allegheny Health Network, Pittsburgh, PA, (3) Allegheny General Hospital, Pittsburgh, PA, (4) Elekta, Inc., Atlanta, GA, (5) Allegheny Health Network, Pittsburgh, PA |
| PO-GeP-I-209 | The Effect of Reconstruction Filters On Dual Energy CT Images From a Single-Source Sequential System C Olguin*, I Barreto, S Leon, C Schaeffer, A Heshmat, M Arreola, University of Florida College of Medicine, Gainesville, FL |
| PO-GeP-I-211 | The Relationship Between Beam Width,Scan Length, and Dosimeter Dimension in CT Equilibrium Dose Measurement V Weir1, J Zhang2*, (1) Baylor University Medical Center, Plano, TX, (2) University of Kentucky, Lexington, KY |
| PO-GeP-I-216 | Toward Developing a Practical Approach for Evaluating CT Scanner Tube Current Modulation Performance S Zhang*, J Provencher, R Subramaniam, Mount Sinai Medical Center, New York, NY |
| PO-GeP-I-222 | Understanding the Interdependent Relationship Between Radiation Dose and Iterative Reconstruction Strength in Abdominal CT Using a Live Animal Model H Ganesh*, F Raslau, C Adams, E Escott, J Zhang, University of Kentucky, Lexington, KY |
| PO-GeP-I-223 | Upright Dedicated Cone-Beam Breast CT: Short-Scan, Non-Uniform, Sparse-View Angular Sampling for Radiation Dose Reduction H Tseng, S Vedantham*, A Karellas, University of Arizona, Tucson, AZ |
| PO-GeP-I-227 | Utilizing a Radiology-Based Informatics System to Impact Clinical Practice: A Study to Improve and Track Patient Alignment in Computed Tomography A Moody1*, L DeWeese2, T Griglock3, (1) UT Health San Antonio, San Antonio, TX, (2) Oregon Health & Science Univ, Portland, OR, (3) Oregon Health & Science Univ, Portland, OR |
| PO-GeP-I-228 | Validation of a Deformable Image Registration Method to Assess Lung Ventilation From 4DCT C Laplante*, S Bedwani, J Carrier, Hopital Notre-Dame du CHUM, Montreal, QCCA, |
| PO-GeP-I-230 | Validation of Quantitative Activity Measurements From Clinical SPECT/CT Systems J Halama*, J Bian, R Wagner, Loyola Univ Medical Center, Maywood, IL |
| PO-GeP-I-233 | Variations in Image Artifacts at Ultra-Low Radiation Dose Levels Due to Differences in Scanner Make and Model: Implications for CT Screening Applications J Browne, M Bruesewitz, Z Long*, T Vrieze, C McCollough, L Yu, Mayo Clinic, Rochester, MN |
| PO-GeP-M-3 | 4DCT Quality Assurance with Multi-Vendors Respiratory Signals Input T Lin*, A Galuza, J Liu, C Ma, Fox Chase Cancer Center, Philadelphia, PA |
| PO-GeP-M-5 | A Comparison of Internal Target Volumes (ITVs) Between 4DCT Simulation Images and 4DCBCT Pre-Treatment Images Using a Respiratory Phantom at Two Different Simulated Respiratory Rates S Parker*, C Whiddon, D Nelson, J Foster, Wake Forest Baptist Health High Point Medical Center, High Point, NC |
| PO-GeP-M-30 | A Novel Semi-Supervised Learning Method Using Soft-Label for Lung Segmentation On CT J Zhou1*, Z Yan2, Y Zhang1, N Yue1, (1) Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, (2) SenseBrain Technology Limited LLC, Princeton, NJ |
| PO-GeP-M-45 | Accurate Tracking of Position and Dose During VMAT Based On VMAT-CT X Zhao1*, R Zhang2, (1) Louisiana State University, Baton Rouge, LA, (2) Mary Bird Perkins Cancer Center, Baton Rouge, LA |
| PO-GeP-M-53 | An Automated Contouring Workflow for Increased Standardization and Efficiency D Hoffman1*, J Meyers2, R Manger1, D Hoopes1, I Dragojevic1, (1) UC San Diego, La Jolla, CA, (2) MIM Software Inc., Cleveland, OH |
| PO-GeP-M-54 | An Automatic Tumor Motion Determination Towards Daily ITV Margin Verification in Lung SBRT Using Markerless 4D-CBCT T Fuangrod1*, B Sakchatchawan2,3, Y Vichianin3, and K Srungboonmee4 (1) Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhron Royal Academy, Bangkok, TH (2) Chulabhorn Hospital, HRH Princess Chulabhorn College of Medical Science, Chulabhron Royal Academy, Bangkok, TH (3) Department of Radiological Technology, Faculty of Medical Technology, Mahidol University, Bangkok, TH (4) Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, TH |
| PO-GeP-M-79 | Automatic Prostate Bed Target Segmentation On Daily Cone-Beam CT Image Using a Multi-Path 3D Dense-UNet J Fu1*, S Yoon1, A Kishan1, K Singhrao1, Z Wang1, J Lewis2, D Ruan1, (1) Department Of Radiation Oncology, UCLA, Los Angeles, CA, (2) Cedars-Sinai Medical Center, Beverly Hills, CA. |
| PO-GeP-M-80 | Automatic Segmentation of Prostate Bed in Post-Prostatectomy CT Images X Xu*, J Lian, Univ North Carolina, Chapel Hill, NC |
| PO-GeP-M-81 | Automatic Segmentation of the Prostate On CT Images Using a Bi-Directional Convolutional LSTM U-Net with Novel Loss Function X Li1*, H Bagher-Ebadian2, C Li1, E Mohamed2, F Siddiqui2, B Movsas2, D Zhu1, I Chetty2, (1) Wayne State University (2) Henry Ford Health System, Detroit, MI |
| PO-GeP-M-87 | Calculation of Rotational Patient Positional Error Corrected Setup Margin in Frameless Stereotactic Radiosurgery and Radiotherapy B Sarkar1*, (1) Apollo Gleneagles Hospitals Limited (2) GLA University Mathura |
| PO-GeP-M-91 | CAPULET: A Novel Coded Aperture Prompt-Gamma Ultra-Light Imaging DETector A Vella*, F Van den Heuvel, CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford, GB |
| PO-GeP-M-99 | Clinical Evaluation of Deep Learning and Atlas Based Auto-Contouring of Bladder and Rectum for Prostate Radiotherapy J Zabel1,2, J Conway2,3, A Gladwish2,3, J Skliarenko2,3, G Didiodato1,2, L Goorts-matthews2, A Michalak2, S Reistetter2, J King2, K Malkoske2, K Nakonechny2, M Tran2, N McVicar2*, (1) McMaster University, Hamilton, ON, Canada, (2) Simcoe Muskoka Regional Cancer Program, Barrie, ON, Canada, (3) Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada |
| PO-GeP-M-106 | Comparing Delivered Dose Estimations with and Without Dose Recalculation for Head-And-Neck Adaptive Radiotherapy Y Hu*, M Aristophanous, L Cervino, A Caringi, N Allgood, M Hunt, Memorial Sloan Kettering Cancer Center, New York, NY |
| PO-GeP-M-112 | Comparison of Contour-Based, Image-Based, and Contour-Image-Based Deformable Image Registration for Adaptive Re-Planning K Kainz*, H Zhong, A Tai, E Ahunbay, X Li, Medical College of Wisconsin, Milwaukee, WI |
| PO-GeP-M-120 | Cone-Beam CT Radiomics for Patients with Liver Tumors Treated by Stereotactic Body Radiation Therapy: A Pilot Study P Yang1*, J Shan2, Q Zhou2, L Xu1, Z Cao1, T Niu3, M Huang4, X Sun2, (1) Zhejiang University, Hangzhou, ,CN, (2) Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang Univ., Hangzhou, ,CN,(3) Georgia Institute Of Technology,Woodruff School of Mechanical Engineering,Atlanta,GA(4) Duke University, Department of Radiation Oncology, Durham, NC, |
| PO-GeP-M-126 | CT Image Parameter Estimation Using PCA-Based Deep Learning in Chronic Obstructive Pulmonary Disease A Moslemi1*, W Tan2, J Bourbeau3, J Hogg2, H Coxson2, M Kirby1, (1) Ryerson University, Toronto, Ontario, CA, (2) Centre For Heart Lung Innovation, University Of British Columbia, (3) McGill University |
| PO-GeP-M-130 | Dark Field Proton Radiography: A Proof of Principle M S Freeman*, J C Allison, E F Aulwes, P E Magnelind, F G Mariam, J J Medina, W Z Meijer, F E Merrill, L P Neukirch, T Schurman, R B Sidebottom, Z Tang, F Trouw, D Tupa, J Tybo, M Espy, Los Alamos National Laboratory, Los Alamos, NM |
| PO-GeP-M-135 | Deep Learning for 3D Automated Delineation of Primary Gross Tumor Volume for Nasopharyngeal Carcinoma by CT Combining Contrast-Enhanced CT Z Dai1*, X Wang2, H Jin3, C Cai4, S Zhao5, Y Zhu6, Y Chen7, (1) The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, CN, (2) The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, CN, (3) The Second Affiliated Hospital Of Guangzhou University Of Chinese Medicine, Guangzhou, Guangdong, CN, (4) The Second Affiliated Hospital Of Guangzhou University Of Chinese Medicine, Guangzhou, Guangdong, AF, (5) The Second Affiliated Hospital Of Guangzhou University Of Chinese Medicine, Guangzhou, Guangdong, CN, (6) The Second Affiliated Hospital Of Guangzhou University Of Chinese Medicine, Guangzhou, Guangdong, CN, (7) The Second Affiliated Hospital Of Guangzhou University Of Chinese Medicine, Guangzhou, Guangdong, CN |
| PO-GeP-M-147 | Determination of Planning Target Volume Margin for Gastric Lymphoma Radiotherapy Using Daily Four-Dimensional Cone-Beam Computed Tomography Y Shimohigashi1*, Y Doi1, Y Kono1, M Maruyama1, Y Kai1, R Toya2, (1) Department of Radiological Technology, Kumamoto University Hospital, Kumamoto, Japan, (2) Department of Radiation Oncology, Kumamoto University Hospital, Kumamoto, Japan |
| PO-GeP-M-156 | Differentiating the Pathological Subtypes of Primary Lung Cancer for Patients with Brain Metastases Based On Radiomics Features From Brain CT Images X Jin1*, Z Ji2, C Xie3, (1) Wenzhou Medical University First Hospital, Wenzhou, ,CN, (2) ,,,(3) ,Wenzhou, ,CN |
| PO-GeP-M-159 | Direct Dose Calculation On CBCTs for Various Treatment Sites in Adaptive Radiotherapy C Wessels*, S Scheib, Varian Medical System, Daettwil, AGCH, |
| PO-GeP-M-167 | Dose Verification of Four-Dimensional Cone-Beam Computed Tomography for Target Localization of Planned and Unplanned Target Motion C Baley*, S Stathakis, P Myers, N Kirby, K Rasmussen, N Papanikolaou, D Saenz, Mays Cancer Center - MD Anderson Cancer Center, San Antonio, TX |
| PO-GeP-M-169 | Dosimetric Analysis of OARnet Auto-Delineations for Head and Neck Organs-At-Risk M H Soomro*, H Nourzadeh, V Leandro Alves, W Choi, J Siebers, University of Virginia, Charlottesville, VA |
| PO-GeP-M-177 | Dosimetric Variation Between Manual Contouring and Auto-Segmentation for Normal Structures in Intensity Modulated Radiotherapy C Alekchander1*, V Kaliyaperumal2, S Chawla3, A Agarwal4, S Goel5, A Sharma6, (1) Patel Hospital Pvt. Ltd, Jalandhar, PB, IN, (2) Medanta The Medicity, Gurgaon, HR, IN, (3) Patel Hospital Pvt Ltd, ,,(4) ,,,(5) Patel Hospital, ,,(6) Patel Hospital Pvt Limited, |
| PO-GeP-M-188 | Establishment and Validation of a Multi-Omics Nomogram to Predict Lymph Node Metastasis of Esophageal Squamous Cell Carcinoma Z Li1, B Li2*, (1)University of Jinan,Jinan, Shandong Province, CN, (2)Shandong Cancer Hospital and Institute, Jinan,Shandong Province,CN; Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong Province,CN; |
| PO-GeP-M-192 | Evaluating ICBCT Image Quality at Halcyon Linac for Patient Set Up Verification M Gei1*, J Visak2, D Pokhrel3, (1) University of Kentucky, Georgetown, KY, (2) University of Kentucky, New Lenox, IL, (3) University of Kentucky, Lexington, KY |
| PO-GeP-M-193 | Evaluating the Accuracy of Atlas-Based Auto-Segmentation for Pediatric Craniospinal Irradiation S Al-ward*, O Ates, M Gargone, T E. Merchant, L Zhao, St. Jude Children's Research Hospital, Memphis, TN |
| PO-GeP-M-199 | Evaluation of Deep Learning-Based Auto-Segmentation of Target Volume and Normal Organs in Breast Cancer Patients SY Chung1*, JS Chang1, Y Chang2, BS Choi1, J Chun1, JS Kim1, YB Kim1, (1) Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, KR, (2) CorelineSoft, Co., Ltd, KR |
| PO-GeP-M-212 | Evaluation of the Direct Density Algorithm for Energy-Independent Radiotherapy Planning A Shutler1*, A Sarkar1, G Grousset2, J Shah2, F Mourtada1, (1) Christiana Care Health System, Newark, DE, (2) Siemens Healthineers USA, Malvern, PA |
| PO-GeP-M-231 | Generalizability Issue of Deep Learning Models in Medicine and Its Potential Solutions: Illustrated with CBCT to CT Image Conversion X Liang*, D Nguyen, S Jiang, UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-M-237 | Higher Ventilation Induced Radiation Pneumonitis for Non-Small Cell Lung Cancer Patients T Lin*, S Kumar, A Dayal, C Ma, Fox Chase Cancer Center, Philadelphia, PA |
| PO-GeP-M-238 | How Low Can You Go? A CBCT Dose Reduction Study A Olch1*, P Alaei2, (1) University of Southern California, and Children's Hospital of Los Angeles, Los Angeles, CA, (2) University of Minnesota, Minneapolis, MN, AF |
| PO-GeP-M-241 | Impact of Deep Learning Based Image Quality Augmentation On CBCT Based Radiomics Analysis M Huang*, Z Zhang, J Lee, Z Jiang, T Niu, F Yin, L Ren, Duke University Medical Center, Cary, NC |
| PO-GeP-M-243 | Impact of ICBCT Reconstruction On Cone Beam Acquisitions G Stinnett*, G Nelson, R Price, H Zhao, A Paxton, V Sarkar, B Salter, University of Utah, Salt Lake City, UT |
| PO-GeP-M-245 | Impact of Imaging Frequency On the Accuracy of Patient Treatment Dose: An Evaluation Based On Halcyon MVCBCT H Wang*, Q Hu, Y Huang, Y Zhang#, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, BeijingCN,#corresponding author: Yibao Zhang; email: zhangyibao@pku.edu.cn |
| PO-GeP-M-258 | Individualized Prediction of Local Recurrence After Radical Surgery for Esophageal Squamous Cell Carcinoma: Development and Validation of Radiomics Nomogram Z Li1, B Li2*, (1) University of Jinan, Jinan, Shandong, CN, (2) Shandong Cancer Hospital and Institute,Jinan, Shandong,CN;Shandong First Medical University and Shandong Academy of Medical Sciences,Jinan, Shandong,CN. |
| PO-GeP-M-273 | Investigation of Partial-Arc CBCT Protocols for Imaging Extremities P Watson*, E Poon, McGill University Health Centre, Montreal, QC, CA |
| PO-GeP-M-274 | Investigation of Radiomics Modelling Discriminability of Tumor Subvolumes in Predicting Distant Metastasis After Radiotherapy in Advanced Nasopharyngeal Carcinoma T Yu1*, X Teng2, S Lam3, J Zhang4, F Lee5, K Au6, W Yip7, J Cai8, (1) Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, (2) Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, (3) Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, (4) The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, (5) Queen Elizabeth Hospital, Hong Kong SAR, (6) Hong Kong Queen Elizabeth Hospital,Hong Kong SAR(7) Queen Elizabeth Hospital, Hong Kong,Hong Kong SAR, (8) Hong Kong Polytechnic University, Hong Kong SAR |
| PO-GeP-M-278 | KV-Energy Fan-Beam CT Imaging Performance of a Novel Biology-Guided Radiotherapy (BgRT) Machine Z Sun1*, H Gao2, S Xu3, J Ye4, C Huntzinger5, S Shirvani6, S Mazin7, T Laurence8, (1) Reflexion Medical, Hayward, CA, (2) Tsinghua University, Haidian Dist, 11, CN, (3) Reflexion Medical, Hayward, CA, (4) Reflexion Medical, Hayward, CA, (5) RefleXion Medical, Hayward, CA, (6) Reflexion Medical, Hayward, CA, (7) Reflexion Medical, Hayward, CA, (8) Reflexion Medical, Hayward, CA |
| PO-GeP-M-285 | Long Term Stability of KV Cone Beam CT (CBCT) Image Quality Assurance for IBA Proteus PLUS Proton Therapy System J George*, J Bennouna, S Rana, J Yu, L Coutinho, M Hobson, L Perles, S George, A Gutierrez, Miami Cancer Institute, Miami, FLORIDA, |
| PO-GeP-M-298 | Monte Carlo Simulation Framework for Scatter Correction of KV and MV CBCT Images of the Varian TrueBeam STx Linac B Zapien Campos*, Instituto de Fisica,UNAM,Mexico City,MX A Martinez Davalos*, Instituto de Fisica,UNAM,Mexico City,MX H Alva Sanchez*, Instituto de Fisica,UNAM,Mexico City,MX M Rodriguez Villafuerte*, Instituto de Fisica,UNAM,Mexico City,MX |
| PO-GeP-M-303 | MRI-Only Brain Radiotherapy: Assessing the Dosimetric Accuracy of Synthetic CT Images Generated Using Cycle GAN S Kazemifar1*, A Barragan Montero2, T Bai1, R Timmerman1, Y Park1, M Lin1, S Jiang1, A Owrangi1, (1) UT Southwestern Medical Center, Dalllas, TX, (2) Universite Catholique de Louvain, Brussels, VBR, BE |
| PO-GeP-M-308 | Multiple Resolution Residual Network for Automatic Thoracic Organs-At-Risk Segmentation in CT H Um*, J Jiang, M Thor, A Rimner, L Luo, J Deasy, H Veeraraghavan, Memorial Sloan Kettering Cancer Center, New York, NY |
| PO-GeP-M-309 | Multi-Section CT Radiomics Can Accurately Predict Postoperative Recurrence in Esophageal Squamous Cell Cancer Patients Achieving PCR After Neoadjuvant Chemoradiotherapy Followed by Surgery Q Qiu1*, J Duan1, H Deng2, Z Han3, J Gu1, Y Yin1, (1) Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, CN, (2) Second Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu, CN (3) Yantai Yuhuangding Hospital, Yantai, Shandong, CN |
| PO-GeP-M-315 | On the Relationship Between Hounsfield Unit and Electron Density: Learning to Be More Accurate A Sudhyadhom1*, J Scholey2, R Marants1, V Kearney2, M Descovich2, L Vinas3, (1) BWH/DFCI/HMS, Boston, MA, (2) University of California San Francisco, San Francisco, CA, (3) University Of California, Berkeley |
| PO-GeP-M-320 | Organ Segmentation From CT Images Using Super Perception Convolutional Neural Networks for Cervical Cancer Brachytherapy Z Zhang1*, S Wang1, Y He2, R Zhou1, Z Jin1, P Xie2, J Wei2, (1) Xiangya Hospital Central South of University, Changsha, Hunan,CN, (2) Perception Vision Medical Technology, Guangzhou, Guangdong,CN, |
| PO-GeP-M-350 | Quantify the Difference in Target Margin Sharpness Demonstrated On 4DCT and 4DCBCT Images Y Tseng1*, M Zhang2, M Hunt2, Y Song2, (1) Columbia University, New York, NY, (2) Memorial Sloan Kettering Cancer Center, New York, NY |
| PO-GeP-M-351 | Quantifying Radiation-Induced Changes to Pulmonary Anatomy Through Dose-Binned Hounsfield Unit Analysis Pre- and Post-RT A Wuschner1*, E Wallat1, M Flakus1, D Shanmuganayagam1, J Meudt1, G Christensen2, J Reinhardt2, J Bayouth1, (1)University of Wisconsin, Madison, WI (2) Univ Iowa, Iowa City, IA |
| PO-GeP-M-352 | Quantifying the Effects of Radiation Therapy Fractionation Scheme On Dose Response Modeling E.M. Wallat1*, A.E. Wuschner1, M.J. Flakus1, W. Shao2, J.M. Reinhardt2, G.E. Christensen2, J.E. Bayouth1, (1)University of Wisconsin-Madison, Madison, WI (2) University of Iowa, Iowa City, IA |
| PO-GeP-M-358 | Radiomics Feature Robustness Under Different Image Perturbation Combinations and Intensities: A Study On Nasopharyngeal Carcinoma CT Images J Zhang1, X Teng1*, Z Ma1, T Yu1, S Lam1, F Lee2, K Au2, W Yip2, J Cai1, (1) The Hong Kong Polytechnic University, Hung Hom, Kowloon, HKSAR, (2) Queen Elizabeth Hospital, HKSAR |
| PO-GeP-M-364 | Reconstruction of Intrafractional 3D Images From Real-Time 2D KV Radiograph and 4DCT J Kim*, G Chen, A Tai, S Lim, T Keiper, X Li, H Zhong, Medical College of Wisconsin, Milwaukee, WI |
| PO-GeP-M-381 | Single Imager Proton Radiography with a Pencil-Beam Scanning System J Harms1, L Maloney2, Y Lin3, T Liu4, A Erickson5, R Zhang6*, (1) Emory University, Atlanta, GA, (2) ,Gainesville, GA, (3) Duke University Medical Center, Atlanta, GA, (4) Emory Univ, Atlanta, GA, (5) Gerogia Institute of Technology, Atlanta, ,(6) Dartmouth College, Lebanon, NH, AF |
| PO-GeP-M-390 | Synthetic Contrast Enhancement of Cone Beam Computed Tomography (CBCT) for Adaptive Radiotherapy O Dona*, Y Wang, D Horowitz, A Xu, J Rickman, C Wuu, Columbia Univ, New York, NY |
| PO-GeP-M-400 | The Impact of CT Reconstruction Kernels On Atlas Based Automatic Segmentation M Reyhan*, B Swann, I Vergalasova, N Yue, K Nie, R Singh, M McKenna, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ |
| PO-GeP-M-403 | To Distinguish Peripheral Lung Cancer and Pulmonary Inflammatory Pseudotumor Using CT-Radiomics Features Extracted From PET/CT Images C Ma, Y Yin*, Shandong Cancer Hospital and Institute,Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, ShandongCN, |
| PO-GeP-M-405 | TOPAS Model for Simulating Contrast in High-Energy Proton Radiography B Broder1*, M Freeman2, (1) Univ Chicago, Chicago, IL, (2) Los Alamos National Laboratory, Los Alamos, NM |
| PO-GeP-M-419 | Validation and Clinical Application of DL-Based Automatic Target and OAR Segmentation Software, DeepViewer Z Peng1*, Y Chang2, Y Song3, H Wu4, A Wu5, X Pei6, X Xu7, (1) University of Science and Technology of China, Hefei, (2) University of Science and Technology of China, Hefei, (3) University of Science and Technology of China, Hefei, (4) Anhui Wisdom Technology Company Limited, Hefei, (5) The First Affiliated Hospital of University of Science and Technology of China, Hefei, (6) University of Science and Technology of China, Hefei, (7) Rensselaer Polytechnic Institute, Troy, NY |
| PO-GeP-M-432 | Weakly-Supervised Deep Learning Based Automatic Image Segmentation Via Deformable Image Registration W Chi123*, W Lu1, L Ma1, J Wu1, H Chen4,, M Tan23, X Gu1, (1) UT Southwestern Medical Center, Dallas, TX, (2) South China University Of Technology, Guangzhou, China, (3) Guangzhou Laboratory, Guangzhou, China, (4) Sun Yat-sen University, Guangzhou, China |
| PO-GeP-M-436 | Why MAE Alone Is Not Enough for SCT Model Comparisons P Klages*, N Tyagi, H Veeraraghavan, Memorial Sloan-Kettering Cancer Center, New York, NY |
| PO-GeP-M-438 | The Effect of Dose Calculation Accuracy for HU to Electron Density Calibration For Iterative CBCT Reconstruction J Zavala Bojorquez1*, E Flores-Martinez2, T Atwood1, C Bojechko1, (1) University of California San Diego, La Jolla, CA, (2) University of Chicago Hospitals, Chicago, IL |
| PO-GeP-P-15 | Verification Measurements On Single Section Or in Air On ICRU/AAPM Long CT Phantom: Application of AAPM Report 200 D Bakalyar*, J Steiner, Henry Ford Health System, Detroit, MI |
| PO-GeP-P-93 | OBI Image Quality Assurance Using Process Capability Indices S Alani*, Y Tova |
| PO-GeP-T-237 | Comprehensive Calibration and Evaluation of a Cone-Beam CT On a Pre-Clinical Small Animal Radiation Research Platform Y Huang1*, Y Zhong1, C Wang1, Y Gonzalez1, C Shen1, X Jia1, (1) The University of Texas Southwestern Medical Ctr, Dallas, TX |
| PO-GeP-T-299 | Development of Patient Height-Specific 3D Age-Scaling Factors to Generate DICOM Computational Phantoms for Retrospective Late-Effects Studies A Gupta1,2*, C Owens1,2, S Shrestha1,2, S Smith1, R Weathers1, R Howell1,2, (1) Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, (2) The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, USA |
| PO-GeP-T-428 | Evaluation On Dosimetric Effects of Irregular Motion During 4DCT Scanning Using a Self-Developed Lung Phantom R Zhao*1, H Wei1, X He2, (1) Shanghai Pulmonary Hospital and Tongji University School of Medicine,Shanghai, CN,(2)Ruijin Hospital and Shanghai Jiaotong University School of Medicine,Shanghai, CN |
| PO-GeP-T-484 | Image-Guided Radiotherapy with Titanium Dioxide Nanoparticles Investigated in Animal Models of Pancreatic Cancer M Moreau*, W Ngwa, Brigham and Womens Hospital, Boston, MA, Dana Farber Cancer Institute, Boston, MA, University of Massachusetts Lowell, Lowell, MA |
| PO-GeP-T-507 | Implications of Metallic Spine Hardware On Dosimetry and Image Verification in Spine SBRT E Tchistiakova*, H Morrison, K Thind, N Ploquin, Tom Baker Cancer Center, Calgary, AB,CA |
| PO-GeP-T-549 | Investigation On the Use of Our 3D Age-Scaling Functions (ASF) to Scale Whole-Body Regions to Any Arbitrary Age A Gupta1,2*, C Owens1,2, S Shrestha1,2, C Lee3, P Balter1, S Smith1, R Weathers1, S Kry1,2, D Followill1,2, J Long1,4, R Howell1,2, (1) Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, (2) The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, USA, (3) Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Bethesda, MD, USA, (4) Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, |
| PO-GeP-T-675 | Quantification of the Correlation Between Outcome with Dose Computed Using Rigid and Deformable Registration H Sharifi*, A Ghanem, Q Wu, I J Chetty, Henry Ford Health System, Detroit, MI |
| PO-GeP-T-682 | Quantitative Versus Qualitative and Dosimetric Evaluation of Automated Segmentations J Pursley*, G Maquilan, G Sharp, Massachusetts General Hospital and Harvard Medical School, Boston, MA |
| PO-GeP-T-742 | Stopping Power Estimation for Carbon Ion Beam Therapy Using Pseudo-Triple Energy CT Y Kim*, J Kim, S Cho, KAISTDaejonKR |
| PO-GeP-T-746 | Study On the Improved Internal Feedback System of RapidPlan Model C Ma*, Shandong Cancer Hospital and Institute,Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, ShandongCN, |
| PO-GeP-T-790 | The Use of Artificial Intelligence to Auto-Segment Organs-At-Risk in Total Marrow Irradiation Treatment A Liu*, R Li, C Han, J Liang, D Du, A Shinde, S Dandapani, A Amini, S Glaser, J Wong, City of Hope Medical Center, Duarte, CA |
| SU-B-TRACK 2-4 | 5DCT Reconstruction Accuracy and Elasticity Estimation Performance for Low Dose Fast-Helical Free Breathing CT M Lauria1*, B Stiehl1, D O'Connell1, S Hsieh2, I Barjaktarevic1, A Santhanam1, D Low1, (1) UCLA, Los Angeles, CA, (2) Mayo Clinic, Rochester, MN |
| SU-B-TRACK 2-5 | A Quantitative Analysis of Lung Elastography Using Free Breathing Fast Helical CT Scans B Stiehl*, M Lauria, I Barjaktarevic, D Low, A Santhanam, UCLA, Los Angeles, CA |
| SU-C-TRACK 1-1 | Optimization Gap: Iterative Reconstruction Does Not Deliver Actual CT Dose Reduction in a Large, Diverse Fleet of Clinical CT Scanners J Bell1*, C Smitherman2, T Petrone2, W Moloney3, D Jordan1, (1) University Hospitals, Cleveland, OH, (2) Petrone Associates, LLC, Staten Island, NY, (3) Bio-Med Associates, Inc, Yardley, PA |
| SU-C-TRACK 1-2 | A Practical Model for Equilibrium Dose Measurement K Grizzard*, D Vergara, J Moroz, M Hoerner, Yale New Haven Hospital and Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT |
| SU-C-TRACK 1-3 | Experimental Validation of a Linear Boltzmann Transport Equation Solver for Rapid CT Dose Map Generation S Principi1*, Y Liu2, Y Lu2, D K Ragan2, A Wang3, A Maslowski4, T Wareing4, T G Schmidt1, (1) BME department, Medical College of Wisconsin and Marquette University, Milwaukee, WI, (2) Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, (3) Department of Radiology, Stanford University, Stanford, CA, (4) Varian Medical Systems, Palo Alto, CA |
| SU-C-TRACK 1-6 | Implementation of Tube Current Modulation Into An Organ Dose Calculator for CT Patients C Lee1*, Y Yeom2, J Atkinson3, L Folio4, (1) National Cancer Institute, Rockville, MD, (2) National Cancer Institute, Rockville, MD, (3) Georgia Institute of Technology, Atlanta, GA,(4) National Institutes of Health, Bethesda, MD |
| SU-CD-TRACK 2-7 | A Random Forest Machine-Enabled Diagnostic Algorithm Combing Quantitative CT Radiomics and Clinical Factors for the Identification of Patients with Corona Virus Disease-19 (COVID-19): A Discovery and Validation Study X Li1,2*, J Li3, X Zhao4, Z Ding1, B Yang1, Q Deng1, S Ma2, Y Kuang5, (1) Hangzhou Cancer Hospital, Hangzhou First People's Hospital Group, Hangzhou, China, (2) Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China, (3) Zhejiang University of Traditional Chinese Medicine, Hangzhou, China, (4) Hangzhou Municipal Health Commission, Hangzhou, China, (5) University of Nevada, Las Vegas, NV |
| SU-D-TRACK 1-4 | Dosimetric Evaluation of Synthetic-CT Generated by Multi-Sequence MR Images for Head and Neck MR-Only Radiotherapy M Qi1*, Y Li2, A Wu1, X Lu1, Y Liu1, L Zhou1, T Song1, (1) Southern Medical University, Guangzhou, Guangdong, CN, (2) Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, CN |
| SU-D-TRACK 1-5 | Development of Realistic Multi-Contrast Textured XCAT (MT-XCAT) Phantoms Using a Dual-Discriminator Conditional-Generative Adversarial Network (D-CGAN) Y Chang1*, K Lafata2, P Segars3, F Yin4, L Ren5, (1) Duke University, Durham, NC, (2) Duke University, Durham, NC, (3) Duke Univ, Durham, NC, (4) Duke University, Durham, NC, (5) Duke University Medical Center, Cary, NC |
| SU-E-TRACK 1-1 | BEST IN PHYSICS (IMAGING): Comparison of Loss Functions in Dual-Domain Convolutional Neural Networks for Low-Dose CT Enhancement KJ Chung1-3*, R Souza4,5, R Frayne4,5, TY Lee1-3, (1) University of Western Ontario, London, ON, CA, (2) Robarts Research Institute, London, ON, CA, (3) Lawson Health Research Institute, London, ON, CA, (4) University Of Calgary, Calgary, AB, CA, (5) Foothills Medical Centre, Calgary, AB, CA |
| SU-E-TRACK 1-2 | Phantom-Based Training Framework for Deep Convolutional Neural Network CT Noise Reduction N Huber*, A Missert, H Gong, S Leng, L Yu, C McCollough, Mayo Clinic, Rochester, MN |
| SU-E-TRACK 1-4 | Modeling and Recovering Gray-Level Co-Occurrence-Based Radiomics in the Presence of Blur and Noise G Gang1*, J Stayman2, (1) Johns Hopkins University, Baltimore, MD, (2) Johns Hopkins University, Baltimore, MD |
| SU-E-TRACK 1-5 | Realistic Lesion Generation Using Generative Adversarial Networks and Radiomics Supervision S Pan*, J Stayman, C Lin, G Gang, Johns Hopkins University, Baltimore, MD |
| SU-E-TRACK 1-7 | An Optimal Timing Protocol for Improved CT Pulmonary Angiography Y Zhao*, L Hubbard, S Malkasian, P Abbona, S Molloi, University of California-Irvine, Irvine, CA |
| SU-F-TRACK 1-1 | Optimization of K-Edge Imaging of Multiple Contrast Agents Using Photon-Counting Computed Tomography (PCCT) D Richtsmeier1*, C Dunning1, K Iniewski2, M Bazalova-Carter1, (1) University of Victoria, Victoria, BC, CA, (2) Redlen Technologies, Saanichton, BC, CA |
| SU-F-TRACK 2-1 | A Generalized Framework for Analytic Regularization of Uniform Cubic B-Spline Deformation Fields K Shah1, J Shackleford1, N Kandasamy1, G Sharp2*, (1) Drexel University, Philadelphia, PA, (2) Massachusetts General Hospital, Boston, MA |
| SU-F-TRACK 2-7 | Synthesizing of Lung Tumors in Computed Tomography Images T O'Briain1*, K Moo Yi1, S Chitsazzadeh2, M Bazalova-Carter1, (1) University of Victoria, Victoria, BC, CA, (2) BC Cancer - Victoria, Victoria, BC, CA |
| TH-AB-TRACK 4-1 | Evaluation of a Novel Sequential 4D Scan Technique for Reduced Motion Artifacts in Respiratory-Gated CT Scans J Cammin1*, R Morris2, (1) Washington University, School of Medicine, St. Louis, MO, (2) Washington University, School of Medicine, St. Louis, MO |
| TH-AB-TRACK 4-2 | Using Virtual Non-Contrast CT From Dual-Energy CT to Eliminate the Need for Pre-Contrast CT for Radiation Therapy Planning of Pancreatic Cancer Patients G Noid1*, A Tai1, D Schott1, J Zhu1, J Shah2, E Paulson1, D Prah1, X Li1, (1) Medical College of Wisconsin, Milwaukee, WI, (2) Siemens Healthineers, Durham, NC |
| TH-AB-TRACK 4-14 | CycleGAN-Based Cone-Beam CT Correction for Adaptive Proton Therapy of Pediatric Patients J Uh*, C Wang, S Acharya, C Hua, St. Jude Childrens Research Hospital, Memphis, TN |
| TH-C-TRACK 1-1 | Adaptive Spectral Inconsistency Modeling for Photon-Counting Detector CT Binxiang Qi, Hewei Gao*, Tsinghua University, Haidian Dist, 11CN, |
| TH-C-TRACK 1-2 | Establishing Quality Control Action Limit for Spectral CT Imaging Using ACR CT Phantom and Longitudinal Measurements X Duan*, Y Zhang, J Anderson, UT Southwestern Medical Center, Dallas, TX |
| TH-C-TRACK 1-3 | Evaluation of the Modulation Transfer Function From a Model-Based and a Statistical-Based Hybrid Iterative Reconstruction Algorithm Using Single-Energy and Dual-Energy CT E Olguin*, S Leon, C Olguin, M Arreola, University of Florida, Gainesville, FL |
| TH-C-TRACK 1-5 | Identifying Active Marrow Using Dual Energy CT Multi-Material Decomposition Q Lyu1*, J Miller2, R Savjani1, M Lawless2, E McKenzie1, K Sheng1, (1) UCLA School of Medicine, Los Angeles, CA, (2) University of Wisconsin-Madison, Madison, WI, |
| TH-C-TRACK 1-6 | Investigation of Spectral Separation, Effective Energy, and Dose Allocation of Split-Filter DECT Using MCNP L Di Maso*, M Lawless, J Huang, J Miller, L DeWerd, University of Wisconsin-Madison, Madison, WI |
| TH-C-TRACK 1-7 | Synthetic Dual Energy CT Images From Single Energy CT Image for Proton Radiotherapy S Charyyev, T Wang*, Y Lei, B Bradshaw Ghavidel, J Beitler, M McDonald, W Curran, T Liu, J Zhou, X Yang, Emory Univ, Atlanta, GA |
| TH-D-TRACK 1-3 | Performance Enhancement of An Experimental Benchtop X-Ray Fluorescence Imaging System by Using the Latest Generation Single Crystal Cadmium Telluride Detector H Moktan*, S Jayarathna, S H Cho, The University of Texas MD Anderson Cancer Center, Houston, TX |
| TH-D-TRACK 1-4 | In Vivo Longitudinal Quantification of Radiopacity of a Nanoparticle-Infused Biodegradable Inferior Vena Cava Filter in a Porcine Model J Perez1*, J Damasco1, M Jacobsen2, A Melancon3, M Eggers4, S Huang1, R Layman2, M Melancon1, (1) Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, (2) Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, (3) Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, (4) Adient Medical Inc., 12234 Shadow Creek Parkway, Building 8, Suite 106, Pearland, TX 77584 |
| TH-D-TRACK 4-1 | Intra-Surgical Imaging with Optical Structured Light and Micro-CT to Reduce Positive Margin Rate in Breast Conserving Surgery B Maloney1*, S Streeter1, K Paulsen1,2,3, B Pogue1,2,3, W Wells2,3,4, R Barth2,3,4, (1) Thayer School of Engineering, Dartmouth College, Hanover, NH, (2) Geisel School of Medicine, Dartmouth College, Hanover, NH, (3) Norris Cotton Cancer Center, Lebanon, NH, (4) Dartmouth Hitchcock Medical Center, Lebanon, NH |
| TH-D-TRACK 5-2 | Estimating Target Registration Error for Automated Deformable Image Registration QA J Sage*, D Boukerroui, M Gooding, Mirada Medical, Denver, CO |
| TH-D-TRACK 5-4 | Head and Neck CTV Decision Making and Automatic Contouring Results in More Consistent Radiotherapy Plans C Cardenas1*, B Beadle2, T Lim1, J Yang1, A Olanrewaju1, R Douglas1, T Netherton1, L Zhang1, L Court1, (1) The University of Texas MD Anderson Cancer Center, Houston, TX, (2) Stanford University, Stanford, CA |
| TH-D-TRACK 5-6 | Impact of CT Scanner Acquisition and Reconstruction Methods On Pediatric Organ Autosegmentation Model Generalizability Philip M. Adamson1*, Petr Jordan1, Vrunda Bhattbhatt1, Taly Gilat Schmidt2, (1) Varian Medical Systems, 3120 Hansen Way, Palo Alto, CA, USA, (2) Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI |
| TU-B-TRACK 1-3 | Protocol Optimization for Microcalcification Detectability in Breast CT: A Phantom Study Using Model Observers A Hernandez1*, A Becker2, S Lyu2, C Abbey3, J Boone1,4, (1) Department of Radiology, University of California Davis, (2) Biomedical Engineering Graduate Group, University of California Davis, (3) Department of Psychological & Brain Sciences, University of California Santa Barbara, (4) Department of Biomedical Engineering, University of California Davis |
| TU-C-TRACK 1-1 | BEST IN PHYSICS (IMAGING): A KV-MV CBCT Field of View Enlargement Technique Using a Multi-Layer MV Imager and Regularized Poly-Energetic Correction M Jacobson1*, M Lehmann2, P Huber2, M Shi3, M Myronakis1, D Ferguson1, I Lozano1, T Harris1, P Baturin4, 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 (3) University of Massachusetts, Lowell, Lowell MA (4) Varian Medical Systems, Palo Alto, CA |
| TU-C-TRACK 1-2 | First Experimental Implementation of Adaptive CaRdiac COne BEAm Computed Tomography (ACROBEAT) T Reynolds1*, O Dillon1, J Prinable1, B Whelan2, P Keall1, R O'Brien1, (1) ACRF Image X Institute, University of Sydney,Sydney, NSW, AU, (2) Siemens Healthineers, Forcheim, BY, DE |
| TU-C-TRACK 1-3 | Towards Distortion-Free Intracranial Image Guided Interventional Procedures with ACROBEAT (Adaptive CaRdiac COne BEAm Computed Tomography) T Reynolds1*, J Prinable1, O Dillon1, B Whelan2, R O'Brien1, P Keall1, (1) ACRF Image X Institute, University of Sydney,Sydney, NSW, AU, (2) Siemens Healthineers, Forcheim, BY, DE |
| TU-C-TRACK 1-4 | Resolution Enhancement for Cone Beam CT Using Focal Spot Deconvolution L Shi*, A Wang, Stanford University, Stanford, CA |
| TU-C-TRACK 1-5 | Quick Low Dose 4D CBCT: Real Patient Results of Respiratory Motion Guided Imaging and Motion Compensated Reconstruction O Dillon1*, B Lau1, S Alnaghy1, P Keall1, S Vinod2,3, A Wallis3, S Smith3, A George3, J Sonke4, 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) Netherlands Cancer Institute, Amsterdam, NL |
| TU-C-TRACK 1-6 | Motion Artifact Impact On Dose Calculation Using CBCT S Scheib1*, I Peterlik2, P Paysan3, S Scheib4, Varian Medical Systems Imaging Laboratory, Daettwil Ag, CH, |
| TU-C-TRACK 1-7 | 4D Radiomics: Impact of 4D Image Quality On Radiomic Analysis Z Zhang*, M Huang, Z Jiang, Y Chang, J Torok, F Yin, L Ren, Duke University Medical Center, Cary, NC |
| TU-CD-TRACK 2-3 | Radiomic Prediction of Radiation Pneumonitis On Pretreatment Planning CT Images of Lung Cancer Patients Receiving Stereotactic Body Radiation Therapy T Hirose1*, H Arimura2, K Ninomiya2, T Yoshitake2, J Fukunaga1, Y Shioyama2, (1)Kyushu University Hospital, Fukuoka, FukuokaJP,(2)Kyushu University, Fukuoka, FukuokaJP, |
| TU-CD-TRACK 2-8 | Normalizing Delta Radiomics for Early Prediction of Treatment Response During Chemoradiation Therapy of Pancreatic Cancer H Nasief*, W Hall, B Erickson, X Li, Medical College of Wisconsin, Milwaukee, WI |
| WE-AB-TRACK 1-0 | How Low Can CT Dose Go? Future Dose Reduction Technologies J Maier1*, M McNitt-Gray2*, G Gang3*, F Noo4*, M Kachelriess5*, (1) German Cancer Research Center, Heidelberg, DE, (2) David Geffen School of Medicine at UCLA, Los Angeles, CA, (3) Johns Hopkins University, Baltimore, MD, (4) University Utah, Salt Lake City, UT, (5) DKFZ Heidelberg, FS05, Heidelberg, BW, DE |
| WE-B-TRACK 2-2 | Dual-Gated CT for Stereotactic Arrhythmic Radioablation (STAR) N Morton*, P Keall, R O'Brien, University of Sydney, Sydney, NSW, AU, |
| WE-B-TRACK 2-3 | Patient-Specific Deep Learning Model for the Augmentation of Digital Tomosynthesis (DTS) for Image Guided Radiation Therapy Z Jiang1*, Y Chang2, Z Zhang3, F Yin4, L Ren5, (1) Duke Univeristy, Durham, NC, (2) Duke University, Durham, NC, (3) Duke University, Durham, NC, (4) Duke University, Durham, NC, (5) Duke University Medical Center, Cary, NC |
| WE-C-TRACK 1-2 | Impact of Volumetric 4D-CT Motion Artifact Reduction On Ventilation Imaging H Young1,3,4*, T Lee2,3,4, S Gaede1,2, (1) London Regional Cancer Program, London, ON, CA, (2) Lawson Health Research Institute, London, ON, CA, (3) Robarts Research Institute, London, ON, CA, (4) University of Western Ontario, London, ON, CA |
| WE-C-TRACK 1-3 | Contrast Timing Optimization for a Two-Volume Dynamic CT Pulmonary Perfusion Technique Y Zhao, L Hubbard, S Malkasian, P Abbona, S Molloi*, University of California-Irvine, Irvine, CA |
| WE-C-TRACK 1-4 | Validation of a Two-Volume Dynamic CT Renal Perfusion Technique B Flynn*, Y Zhao, L Hubbard, S Malkasian, P Abbona, S Molloi, University of California-Irvine, Irvine, CA |
| WE-C-TRACK 1-5 | Quantitative Evaluation of Metal Artifact Reduction by Threshold-Based Energy-Selective Acquisition in Photon-Counting Computed Tomography S Skornitzke1*, T Do1, S Sawall2, T Reiner1, C Ziener2, M Kachelriess2, H Schlemmer2, H Kauczor1, W Stiller1, (1) Heidelberg University Hospital, Heidelberg, DE, (2) German Cancer Research Center (DKFZ), Heidelberg, DE |
| WE-E-TRACK 2-2 | From CT Scans to 3D Prints: Feasibility of 3D Printing CT Radiomic Phantoms for Standardization and Validation of Quantitative CT Measurements U Mahmood1*, A Apte1, C Kanan2, D Bates1, G Corrias1, L Mannelli1, J Oh1, Y Erdi1, J Deasy1, A Dave1, (1) Memorial Sloan Kettering Cancer Center, (2) Rochester Institute Of Technology |
| WE-E-TRACK 2-5 | Correlation Between Hematocrit and Blood CT Number Changes During Radiation Therapy X Chen*, H Saeed, X Li, Medical College of Wisconsin, Milwaukee, WI |
| WE-E-TRACK 4-0 | Current Status and Issues with Dental and Maxillofacial CBCT: Potential Methodologies for Quality Control and Management of This Technology D Mihailidis1*, E Gingold2*, R Pizzutiello3*, (1) University of Pennsylvania, Philadelphia, PA, (2) Thomas Jefferson University, Philadelphia, PA, (3) Victor, NY |
| WE-E-TRACK 4-1 | Suggestions for testing evaluation of dental and maxillofacial CBCT D Mihailidis1*, E Gingold2*, R Pizzutiello3*, (1) University of Pennsylvania, Philadelphia, PA, (2) Thomas Jefferson University, Philadelphia, PA, (3) Victor, NY |
| WE-E-TRACK 4-2 | Dose metrics and practical dose measurements for dental and maxillofacial CBCT D Mihailidis1*, E Gingold2*, R Pizzutiello3*, (1) University of Pennsylvania, Philadelphia, PA, (2) Thomas Jefferson University, Philadelphia, PA, (3) Victor, NY |
| WE-E-TRACK 4-3 | Regulatory and Accreditation Considerations D Mihailidis1*, E Gingold2*, R Pizzutiello3*, (1) University of Pennsylvania, Philadelphia, PA, (2) Thomas Jefferson University, Philadelphia, PA, (3) Victor, NY |
| WE-F-TRACK 4-0 | Diagnostic Imaging and 100 MSv+ Doses W Sensakovic1*, M Rehani2*, M Lipford3*, T Szczykutowicz4*, (1) Mayo Clinic, Phoenix, AZ, (2) Massachusetts General Hospital, Boston, MA, (3) University of Wisconsin, Madison, WI, (4) University Wisconsin-Madison, Madison, WI |
| WE-F-TRACK 4-1 | Do patient receive 100mSv+ from diagnostic scanning? W Sensakovic1*, M Rehani2*, M Lipford3*, T Szczykutowicz4*, (1) Mayo Clinic, Phoenix, AZ, (2) Massachusetts General Hospital, Boston, MA, (3) University of Wisconsin, Madison, WI, (4) University Wisconsin-Madison, Madison, WI |
| WE-F-TRACK 4-2 | Patients with over 100 mSv doses: Experience from large scale studies to assess the magnitude W Sensakovic1*, M Rehani2*, M Lipford3*, T Szczykutowicz4*, (1) Mayo Clinic, Phoenix, AZ, (2) Massachusetts General Hospital, Boston, MA, (3) University of Wisconsin, Madison, WI, (4) University Wisconsin-Madison, Madison, WI |
| WE-F-TRACK 4-3 | Dose Reduction Opportunities in MDCT W Sensakovic1*, M Rehani2*, M Lipford3*, T Szczykutowicz4*, (1) Mayo Clinic, Phoenix, AZ, (2) Massachusetts General Hospital, Boston, MA, (3) University of Wisconsin, Madison, WI, (4) University Wisconsin-Madison, Madison, WI |
| WE-F-TRACK 4-4 | Round-Table Discussion W Sensakovic1*, M Rehani2*, M Lipford3*, T Szczykutowicz4*, (1) Mayo Clinic, Phoenix, AZ, (2) Massachusetts General Hospital, Boston, MA, (3) University of Wisconsin, Madison, WI, (4) University Wisconsin-Madison, Madison, WI |
| WE-F-TRACK 5-0 | Dual Energy CT Applications in Radiotherapy B Teo1*, J Miller2*, (1) University of Pennsylvania, Philadelphia, PA, (2) University of Wisconsin-Madison, Madison, WI |