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Keywords: Image Processing
| 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-B-TRACK 6-2 | Localized GBM Recurrence Prediction On Post-Operative Multi-Modal MRIs Through Stem Cell Niches Proximity Estimation Coupled SVM Y Lao1*, V Yu2, A Pham3, T Wang3, D Ruan1, E Chang3, K Sheng1, W Yang3, (1) UCLA School of Medicine, Los Angeles, CA, (2) Memorial Sloan Kettering Cancer Center, New York, NY, (3) University of Southern California, Los Angeles, CA |
| MO-EF-TRACK 4-7 | Latent Space Arc Therapy Optimization N Bice1*, N Kirby1, D Nguyen2, C Kabat1, P Myers1, N Papanikolaou1, M Fakhreddine1, (1) UT Health San Antonio MD Anderson Cancer Center, San Antonio, Texas, (2) UT Southwestern Medical Center, Dallas, TX. |
| MO-IePD-TRACK 1-4 | Adaptive Dual-Energy X-Ray Imaging with Pre-Calibrated Patient-Specific Weighting Factors I Romadanov1, M Sattarivand2*, (1) Nova Scotia Health Authority, Halifax, NS, CA, (2) Nova Scotia Health Authority, Halifax, NS, CA |
| MO-IePD-TRACK 1-5 | Estimating Bone Densities Using a Cadmium Zinc Telluride Photon Counting Detector J Nguyen1*, D Richtsmeier1, K Iniewski2, M Bazalova-Carter1 (1) University of Victoria, Victoria, BC, CA (2) Redlen Technologies, Saanichton, BC, CA |
| MO-IePD-TRACK 1-7 | Impact of Flexible Noise Control (FNC) Processing Parameters On Image Quality: A Chest Phantom Study L Ren*, T Daly, K Loewen, B Schueler, Z Long, Mayo Clinic, Rochester, MN |
| 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 4-6 | Using Automatic Segmentation to Improve Deformable Image Registration K Shah1*, J Shackleford1, N Kandasamy1, G Sharp2, (1) Drexel University, Philadelphia, PA, (2) Massachusetts General Hospital, Boston, MA |
| MO-IePD-TRACK 4-7 | Using Neural Networks to Extend Cropped Medical Images for Deformable Registration Among Images with Differing Scan Extents E McKenzie1*, N Tong2, D Ruan1, M Cao1, R Chin1, K Sheng1, (1) UCLA, Los Angeles, CA, (2) Xidian University, Xi'an CN |
| PO-GePV-M-15 | Limitations of the Structural Similarity Index in Medical Image Synthesis Evaluation D Gourdeau1,2,3*, S Duchesne1,2, L Archambault2,3, (1) Universite Laval (2) CERVO Brain research center (3) CHUQ Hotel-Dieu de Quebec |
| PO-GePV-M-119 | Improvement of MVCT Image Quality for Adaptive Helical Tomotherapy Using CycleGAN-Based Image Synthesis with Small Datasets D Lee*1,3, H Cho1, Y Han2,3, (1) Yonsei University, Wonju, KR, (2) Sungkyunkwan University, Seoul, KR (3) Samsung Medical Center, Seoul, kr |
| PO-GePV-M-145 | A Method to Consider Dose Distribution and Dose Gradient in Evaluating Image Registration for Dose Accumulation X Wu*, J Williamson, H Gach, H Li, D Yang, Washington University in St. Louis, St. Louis, MO |
| PO-GePV-M-154 | Impact of Various Noise Reduction Techniques On Markerless Tumor Tracking in Dual Energy Imaging M Kaur1*, P Wagstaff1, H Mostafavi2, M Lehmann2, D Morf2, L Cortesi2, L Zhu2, H Kang1, M Harkenrider1, J Roeske1, (1) Loyola University, Chicago, IL, (2) Varian Medical Systems, Palo Alto, CA |
| PO-GePV-M-248 | A Medical Image Challenge Infrastructure K Farahani1*, J Kalpathy-Cramer2, B Bearce3, U Wagner4, (1) National Cancer Institute, Bethesda, MD, (2) Boston, MA, (3) No institution provided (4) Frederick National Lab, Frederick, MD |
| SU-D-TRACK 4-5 | Reconstruction of DRR-Like KV-DR Using CycleGAN-Based Image Synthesis for Intra- and Extracranial SRT/SRS D Lee*1,2, S LEE3, H Cho1, (1) Yonsei University, Wonju, KR, (2) Samsung Medical Center, Seoul, KR (3) Boston Medical Center, Boston, MA |
| SU-E-TRACK 6-7 | Spatial Reconstruction of Statistically Significant Radiomics Signatures Using 3D Wavelet Decomposition in Tumors of Oropharyngeal Cancer H Bagher-Ebadian*, F Siddiqui, A Ghanem, S Zhu, M Lu, B Movsas, I Chetty, Henry Ford Health System, Detroit, MI |
| 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 1-4 | Recurrence Prediction for Head and Neck Squamous Cell Cancer Patients Using Local Binary Pattern-Based Dosiomics H Kamezawa1*, H Arimura2, (1) Teikyo University, Omuta, JP, (2) Kyushu University, Fukuoka, JP |
| SU-IePD-TRACK 1-5 | Multi-Frequency-Based CXR Data Normalization for Deep-Neural-Network Classifier H Kim*, KAIST, Daejon, KR, W Sim, Radisen, Seoul, KR, D Cho, Radisen, Seoul, KR, S Cho, KAIST, Daejeon, KR |
| SU-IePD-TRACK 2-3 | Breast Thickness Map Estimation and Its Associated Correction in DBT Imaging S Lee1*, H Kim2, H Lee3, S Cho4, (1) KAIST, Daejeon, 44, KR, (2) KAIST, Daejeon, ,KR, (3) Massachusetts General Hospital, Boston, MA, (4) KAIST, Daejon, ,KR |
| SU-IePD-TRACK 3-1 | A Realistic DRR Rendering Method Based On Cycle-GAN R Wei1*, B Liu2,3, B Liang1, J Dai1, (1) National Cencer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing,100021, CN, (2) Image Processing Center,Beihang University, Beijing,CN, (3) Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, CN |
| SU-IePD-TRACK 3-2 | Multi-Organ Segmentation On An Inference-Combined Dataset T Marschall1*, X Li2, K Yang3, B Liu4, (1) Massachusetts General Hospital, Cambridge, MA, (2) Massachusetts General Hospital, Belmont, MA, (3) Harvard Medical School, Massachusetts General Hospital, Boston, MA, (4) Massachusetts General Hospital, Boston, MA |
| SU-IePD-TRACK 3-4 | A Deep Learning Approach for Contour Interpolation D Yang2*,C Zhao1, Y Duan1, H Li2, H Kim2, L Henke2, D Yang2, (1) University of Missouri, (2) Washington University School of Medicine, St. Louis, MO |
| TH-IePD-TRACK 2-7 | Automatic Stratification of Prostate Cancer Patients Into Low- and High-Grade Groups Using a Support Vector Machine Model with Multiparametric Magnetic Resonance Image Features A Urakami*1, H Arimura2, Y Takayama3, F Kinoshita1, K Ninomiya1, K Imada3, S Watanabe2, A Nishie3, Y Oda3, K Ishigami3, (1) Kyushu University Graduate School of Medical Sciences, Fukuoka, JP, (2) Kyushu University Faculty of Medical Sciences, Fukuoka, JP, (3) Kyushu University Hospital, Fukuoka, JP |
| TU-B-TRACK 6-3 | Neural Network Based Event Classification to Improve Compton Imaging for Proton Beam Range Verification C Barajas1, G Kroiz1, J Polf2*, S Peterson3, D Mackin4, S Beddar4, M Gobbert1, (1) University Of Maryland, Baltimore County, (2) University of Maryland School of Medicine, Baltimore, MD, (3) University of Cape Town, Rondebosch, ,ZA, (4) UT MD Anderson Cancer Center, Houston, TX |
| 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-D-TRACK 3-2 | Low-Dose CT Image Enhancement Through a Texture Transformer S Zhou1*, L Yu2, M Jin1, (1) University of Texas at Arlington, Arlington, TX, (2) Mayo Clinic, Rochester, MN |
| TU-F-TRACK 6-1 | 3D Dense U-Net for Fully Automated Multi-Organ Segmentation in Female Pelvic Magnetic Resonance Imaging F Zabihollahy*, E Schmidt, A Viswanathan, J Lee, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD |
| TU-IePD-TRACK 4-1 | A Deep Reinforcement Learning Based Pipeline for Prostate Segmentation On MRI with Low Variance Performance L Xu1*, W Shi1, N Wen2, (1) Wayne State University, Detroit, MI, (2) Henry Ford Health System, Detroit, MI |
| TU-IePD-TRACK 4-6 | Deploying Deep Learning-Based Image Segmentation Models Via CERR A Iyer*, E LoCastro, J Deasy, A Apte, Memorial Sloan-Kettering Cancer Center, New York, NY |