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Taxonomy: IM- Optical : Image Reconstruction
| 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-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-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 |
| 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-18 | A 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-41 | Building 3D Dynamic Keyhole Library with Compressive Sensing and Parallel Imaging Reconstruction D Lam*, B Lewis, H Gach, S Mutic, T Kim, Washington University in St. Louis, St. Louis, MO |
| PO-GeP-I-120 | Fast 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-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-169 | Positron Emission Tomography Scatter Image Reconstruction with CNN Machine Learning G Fontaine*, S Pistorius, University of Manitoba, Winnipeg, MB |
| 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-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-201 | Super 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-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-234 | Visualization of Meniscus with 3D Axial Reconstructions E Lavdas1*, M Papaioannou2, A Tsikrika3, E Pappas2, A Tsagkalis2, D Paridis2, V Roka4, S Stathakis5, P Mavroidis6, (1) University of West Attica, Athens, GR, (2) Animus Kyanoys Stavros, Larissa, GR, (3) General University Hospital of Larissa, Larissa, GR, (4) Health Center of Farkadona, Trikala, GR, (5) Mays Cancer Center - MD Anderson Cancer Center, San Antonio, TX, (6) Univ North Carolina, Chapel Hill, NC |
| 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-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 |
| 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 |
| 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 |
| 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 1-4 | Resolution Enhancement for Cone Beam CT Using Focal Spot Deconvolution L Shi*, A Wang, Stanford University, Stanford, CA |
| WE-F-TRACK 1-2 | Deep Learning Based Image Reconstruction Applied to An Accelerated Brain Protocol K Hwang1*, X Wang2, M Lebel2, E Bayram2, S Banerjee2, JM Johnson1, (1) UT MD Anderson Cancer Center, Houston, TX, (2) GE Healthcare, Waukesha, WI |