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Keywords: reconstruction
BReP-SNAP-I-11Comparison 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-12Cone-Beam CT Image Reconstruction with Spherical Harmonics
T Shimomura*, A Haga, Tokushima UniversityTokushimaJP
BReP-SNAP-I-35Orthogonal 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-43Reducing 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-58Variations 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-59Reconstructing 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-T-55Electromagnetic Tracking as a Quality Assurance Tool for Complex Intracavitary and Interstitial Cervix Brachytherapy Implants
I Kolkman-Deurloo1*, M Doorn Van1, L Heerden Van1, J Schiphof-godart1, D Schaart2, M Hoogeman1, (1) Erasmus MC - Cancer Institute, Rotterdam, NL, (2) Delft Univ of Technology, Delft, NL
BReP-SNAP-T-61Evaluation of Electromagnetic Catheter Tracking in HDR Prostate Brachytherapy for Different Imaging and Treatment Environments
C P Joshi1,2*, C Pinter3, A Lasso3, E Lugez3, T Vaughan3, C Shenfield1,2, G Fichtinger3, (1) Cancer Center of Southeastern Ontario, Kingston Health Sciences Centre, Kingston, ON, Canada, (2) Departments of Oncology and Physics, Queens University, Kingston, ON, Canada (3) School of Computing, Queens University, Kingston, ON, Canada
PO-GeP-I-6A Deep Learning-Based End-To-End CT Reconstruction Method
K Lu*, L Ren, F Yin, Duke University, Durham, NC
PO-GeP-I-18A Robust Real-Time Acceleration and Reconstruction Scheme for Rapid MRI Using Principal Component Analysis
M Wright*, B Dietz, E Yip, J Yun, B G Fallone, K Wachowicz, Medical Physics, University of Alberta, Cross Cancer Institute, Edmonton, AB, CA
PO-GeP-I-20A Sparse Sampling RF Pulse Sequence for 3D 13C Metabolic MR Chemical Shift Imaging
J-X Wang1*, (1) University of Alabama at Birmingham, Birmingham, AL
PO-GeP-I-24Accurate 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-120Fast DTI Using Deep Learning Based On Cartesian and Non-Cartesian Undersampling Schemes
Z Gao1*, T Arsenault2, Z Chang3, (1) Duke Kunshan University, Suzhou, Jiangsu, China (2) Duke University, Durham, NC (3) Duke University Medical Center, Durham, NC
PO-GeP-I-150Metal 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-169Positron Emission Tomography Scatter Image Reconstruction with CNN Machine Learning
G Fontaine*, S Pistorius, University of Manitoba, Winnipeg, MB
PO-GeP-I-178Quantitative 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-185Real Time Image Reconstruction Technique for Digital Breast Tomosynthesis-Guided Needle Biopsy
P Singh1*, C Choi2, T Vent2, A Maidment3, (1) Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, (2) Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, (3) Department of Radiology, University of Pennsylvania, Philadelphia, PA
PO-GeP-I-196Stochastic 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-201Super Resolution Reconstruction of T2-Weighted Knee MRI From Single-Plane Low Resolution Interleaved Acquisitions
R Nosrati1,2, O Afacan1,2*, A Gholipour1,2, A Tsai1,2, S Warfield1,2, (1) Harvard Medical School, Boston, MA, (2) Boston Children's Hospital, Boston, MA
PO-GeP-I-209The 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-M-243Impact 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-364Reconstruction 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-T-408Evaluation of New Markers for the Interstitial Plastic Component in MR-Based Cervix Brachytherapy
J Chimeno Hernandez1, F Blazquez1, G Montoliu2, J Bosque2, G Jose1, J Ruiz Rodriguez1, V Carmona Meseguer1, F Lliso-Valverde1, J Perez-Calatayud1*, (1) Radiotherapy Department,Hospital Universitari i Politecnic La Fe, Valencia,Spain , (2) Radiodiagnostic Department, Hospital Universitari I Politecnic La Fe, Valencia, Spain
SU-C-TRACK 1-1Optimization 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-E-TRACK 1-1BEST 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
TH-C-TRACK 1-3Evaluation 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
TU-C-TRACK 1-1BEST 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 1-5Quick 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-EF-TRACK 2-2BEST IN PHYSICS (MULTI-DISCIPLINARY): Accelerating Image Reconstruction On An MRI-Linac with AUTOMAP
D Waddington1*, N Hindley1, N Koonjoo2, T Reynolds1, B Zhu2, C Paganelli3, M Rosen2, P Keall1, (1) ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, NSW, AU, (2) A. A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA, (3) Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, IT.
WE-D-TRACK 3-4Groundwork for Potential Organ Dosimetry System Revisions in the Japanese Atomic Bomb Survivor Cohorts
K Griffin1*, C Paulbeck2, T Sato3, S Funamoto4, H Cullings4, S Egbert5, S Domal2, A Endo3, N Hertel6, W Bolch2, C Lee1, (1) National Cancer Institute, Rockville, MD, (2) University of Florida, Gainesville, FL, (3) Japan Atomic Energy Agency, Tokai-mura, JP, (4) Radiation Effects Research Foundation, Hiroshima, JP, (5) Consultant, (6) Georgia Institute of Technology, Atlanta, GA
WE-F-TRACK 1-3Accelerating MRI Acquisition Using Cascaded Attention UNet with Prior Information
V Agarwal*, J Balter, Y Cao, Univ Michigan, Ann Arbor, MI