Authors: Benito De Celis Alonso, Braian Adair Maldonado Luna, Gerardo Uriel Perez Rojas, RenĂ© Eduardo RodrĂguez-PĂ©rez, Kamal Singhrao
Affiliation: Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute, Harvard Medical School, Faculty of Physics and Mathematics, Benemérita Universidad Autónoma de Puebla
Abstract Preview: Purpose: Artificial Intelligence (AI)-generated synthetic CT (sCT) images can be used to provide electron densities for dose calculation for online adaptive MRI-guided stereotactic body radiotherapy (...
Authors: Eric Chang, Nguyen Phuong Dang, Andrew Lim, Lauren Lukas, Lijun Ma, Yutaka Natsuaki, Zhengzheng Xu, Hualin Zhang
Affiliation: Radiation Oncology, Keck School of Medicine of USC
Abstract Preview: Purpose: Harnessed the power of AI and Deep Learning (DL), Generalized Neural Network models for medical image transformation are trained to predict target images from reference images, often requirin...
Authors: Christopher Colyer, Leon F Dunn, Jonathan Dunning, Simon Goodall, Andy Schofield
Affiliation: GenesisCare
Abstract Preview: Purpose: Modern radiotherapy is characterized by complex treatment plans utilizing dynamic MLCs, gantry positions and recently, collimator rotations. The purpose of this work was to compare the plan c...
Authors: Yasin Abdulkadir, Justin Hink, James M. Lamb, Jack Neylon
Affiliation: Department of Radiation Oncology, University of California, Los Angeles
Abstract Preview: Purpose: Curation remains a significant barrier to the use of âbig dataâ radiotherapy planning databases of 100,000 patients or more. Anatomic site of treatment is an important stratification for almo...
Authors: Austin Castelo, Xinru Chen, Caroline Chung, Laurence Edward Court, Jaganathan A Parameshwaran, Zhan Xu, Jinzhong Yang, Yao Zhao
Affiliation: The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
Abstract Preview: Purpose:
To develop a deep learning-based segmentation model to automatically delineate tumors from full-body PET/CT images.
Methods:
PET/CT image pairs of 91 patients were collected for this...
Authors: Kristen A. Duke, Samer Jabor, Neil A. Kirby, Parker New, Niko Papanikolaou, Arkajyoti Roy, Yuqing Xia
Affiliation: St. Mary's University, The University of Texas San Antonio, UT Health San Antonio
Abstract Preview: Purpose:
The Segment Anything Model (SAM) is a foundational box-prompt-based model for natural image segmentation. However, its applicability to zero-shot 3D medical image segmentation, particularl...
Authors: Wen C. Hsi, Pouya Sabouri, Zhong Su
Affiliation: University of Arkansas for Medical Sciences, Department of Radiation Oncology, University of Arkansas for Medical Sciences (UAMS)
Abstract Preview: Purpose:
Traditional single-energy CT (SECT) contrast scans cannot be used for proton dose calculations due to significantly higher HU caused by iodine. Dual-layer dual-energy CT (DL-DECT) can dire...
Authors: Gregory T. Armstrong, James E. Bates, Christine V. Chung, Lei Dong, Ralph Ermoian, Jie Fu, Christine Hill-Kayser, Rebecca M. Howell, Meena S. Khan, Sharareh Koufigar, John T. Lucas, Thomas E. Merchant, Taylor Meyers, Tucker J. Netherton, Constance A. Owens, Arnold C. Paulino, Sogand Sadeghi
Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, Department of Epidemiology and Cancer Control, St. Jude Childrenâs Research Hospital, St. Jude Children's Research Hospital, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Department of Radiation Oncology, St. Jude Childrenâs Research Hospital, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, University of Washington/ Fred Hutchinson Cancer Center, Department of Radiation Oncology, University of Pennsylvania, University of Pennsylvania, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology and Winship Cancer Institute, Emory University, The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences
Abstract Preview: Purpose: Clinical workflows often rely on auto-segmentation tools trained on adult data, which may exhibit suboptimal performance in pediatric imaging due to inherent anatomical variations and smaller...
Authors: Max Chen, Sean Marquardt, Ben Yang, Kai Yang
Affiliation: Cary Academy, Massachusetts General Hospital, Winchester High School
Abstract Preview: Purpose: To analyze the impact of scanner model variation on the effective dose conversion factor (âk-factorâ), which is most commonly used for CT effective dose calculation.
Methods: The stand...
Authors: Takahiro Kato, Teiji Nishio, Masataka Oita, Robabeh Rahimi, Yuki Tominaga, Yushi Wakisaka
Affiliation: University of Maryland, Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, Medical Co. Hakuhokai, Osaka Proton Therapy Clinic, Medical Physics Laboratory, Division of Health Science, Osaka University Graduate School of Medicine, Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University
Abstract Preview: Purpose: This study evaluated dose verifications and lateral penumbra improvements for scanned proton therapy plans with and without a multi-leaf collimator (MLC) under various air gaps.
Methods: E...
Authors: Lawrence T. Dauer, Yusuf Emre Erdi, Yiming Gao, Dustin W. Lynch, Usman Mahmood
Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center
Abstract Preview: Purpose: Radiation dose to patients in CT examinations (CTDIvol, DLP) is tracked in dose monitoring solutions. Some solutions push for automatically estimating patient organ doses based on exam images...
Authors: Jeremy S. Bredfeldt, Benito De Celis Alonso, Braian Adair Maldonado Luna, Kevin M. Moerman, Gerardo Uriel Perez Rojas, RenĂ© Eduardo RodrĂguez-PĂ©rez, Kamal Singhrao
Affiliation: Department of Radiation Oncology, Brigham and Women's Hospital, Harvard Medical School, Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute, Harvard Medical School, Department of Mechanical Engineering, University of Galway, Faculty of Physics and Mathematics, Benemérita Universidad Autónoma de Puebla
Abstract Preview: Purpose: Online adaptive radiotherapy replanning for single-isocenter bone cancer metastasis treatment reduces on-table treatment time and patient discomfort compared to the multi-isocenter standard-o...