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: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Kangning Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose:
PET is used in radiotherapy workflows for accurate target delineation. However, a separate CT scan is typically required for attenuation correction in PET imaging and for registering PET-d...
Authors: Magdalena Bazalova-Carter, James Day, Xinchen Deng
Affiliation: University of Victoria
Abstract Preview: Purpose:
Ring artifacts in Photon-Counting Computed Tomography (PCCT) images can degrade image quality. this study aims to suppress ring artifacts with a novel autoencoder-based framework that leve...
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 Children’s 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 Children’s 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: Bingqi Guo, Ping Xia
Affiliation: Cleveland Clinic
Abstract Preview: Purpose:
Spatially fractionated radiation therapy (SFRT) delivers a “GRID” or “lattice” of high and low doses to tumors to increase tumor control, minimize normal tissue damage, and preserve the im...
Authors: Ling Chen, Alexei V. Trofimov, Yi Wang, Dufan Wu
Affiliation: Massachusetts General Hospital, MGH
Abstract Preview: Purpose:
Selecting gaze angles of the eye is an important part of set-up of proton therapy for ocular tumors, ensuring that the treatment beam could irradiate the tumor while maximally sparing impo...
Authors: Minbin Chen, Xiaoyi Dai, Xiaoyu Duan, Chunhao Wang, Fan Xia, Zhenyu Yang, Fang-Fang Yin, Chulong Zhang, Rihui Zhang
Affiliation: Duke University, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University, The First People's Hospital of Kunshan
Abstract Preview: Purpose: Deep learning (DL)-based mammography diagnosis presents unique challenges, as accurate interpretation requires both global breast condition analysis and local lesion structural information. E...
Authors: Guha Balakrishnan, Osama R. Mawlawi, Yiran Sun, Ashok Veeraraghavan
Affiliation: RICE University, UT MD Anderson Cancer Center
Abstract Preview: Purpose:
Previous deep learning (DL) techniques such as X2CT-GAN [1] has shown great promise in reconstructing realistic CT volume from biplanar X-rays, however they introduce numerous artifacts in...
Authors: Hoyeon Lee
Affiliation: University of Hong Kong
Abstract Preview: Purpose: Deep-learning approaches are widely investigated for Cone-Beam CT (CBCT) scatter correction to improve the quality of the linear-accelerator mounted CBCT. This study aims to optimize the deep...
Authors: Guangjun Li, Ying Song, Huanan Tang, Tianxiong Wu, Qiuyi Ye, Wei Zhang
Affiliation: West China Second Hospital of Sichuan University, United Imaging Healthcare, West China Hospital of Sichuan University
Abstract Preview: Purpose:
To propose a general low-dose reconstruction model for ultra-sparse-view cone-beam CT (CBCT) and evaluate its clinical application in improving image quality and reducing radiation dose fo...