Výsledky vyhledávání - "Bradley, Jeffrey D"

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Proton Pencil-Beam Scanning Stereotactic Body Radiation Therapy and Hypofractionated Radiation Therapy for Thoracic Malignancies: Patterns of Practice Survey and Recommendations for Future Development from NRG Oncology and PTCOG

Autor: Liu, Wei, Feng, Hongying, Taylor, Paige A., Kang, Minglei, Shen, Jiajian, Saini, Jatinder, Zhou, Jun, Giap, Huan B., Yu, Nathan Y., Sio, Terence S., Mohindra, Pranshu, Chang, Joe Y., Bradley, Jeffrey D., Xiao, Ying, Simone II, Charles B., Lin, Liyong

Stereotactic body radiation therapy (SBRT) and hypofractionation using pencil-beam scanning (PBS) proton therapy (PBSPT) is an attractive option for thoracic malignancies. Combining the advantages of target coverage conformity and critical organ spar

Externí odkaz: http://arxiv.org/abs/2402.00489

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One-step Iterative Estimation of Effective Atomic Number and Electron Density for Dual Energy CT

Autor: Wang, Qian, Xie, Huiqiao, Wang, Tonghe, Roper, Justin, Gao, Hao, Tian, Zhen, Tang, Xiangyang, Bradley, Jeffrey D., liu, Tian, Yang, Xiaofeng

Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification, artifact suppression, etc. For proton therapy treatment planning, be

Externí odkaz: http://arxiv.org/abs/2308.01290

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MRI-based Material Mass Density and Relative Stopping Power Estimation via Deep Learning for Proton Therapy

Autor: Gao, Yuan, Chang, Chih-Wei, Mandava, Sagar, Marants, Raanan, Scholey, Jessica E., Goette, Matthew, Lei, Yang, Mao, Hui, Bradley, Jeffrey D., Liu, Tian, Zhou, Jun, Sudhyadhom, Atchar, Yang, Xiaofeng

Magnetic Resonance Imaging (MRI) is increasingly incorporated into treatment planning, because of its superior soft tissue contrast used for tumor and soft tissue delineation versus computed tomography (CT). However, MRI cannot directly provide mass

Externí odkaz: http://arxiv.org/abs/2210.05804

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Deformable Image Registration using Unsupervised Deep Learning for CBCT-guided Abdominal Radiotherapy

Autor: Xie, Huiqiao, Lei, Yang, Fu, Yabo, Wang, Tonghe, Roper, Justin, Bradley, Jeffrey D., Patel, Pretesh, Liu, Tian, Yang, Xiaofeng

CBCTs in image-guided radiotherapy provide crucial anatomy information for patient setup and plan evaluation. Longitudinal CBCT image registration could quantify the inter-fractional anatomic changes. The purpose of this study is to propose an unsupe

Externí odkaz: http://arxiv.org/abs/2208.13686

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Multimodal Imaging-based Material Mass Density Estimation for Proton Therapy Using Physics-Constrained Deep Learning

Autor: Chang, Chih-Wei, Marants, Raanan, Gao, Yuan, Goette, Matthew, Scholey, Jessica E., Bradley, Jeffrey D., Liu, Tian, Zhou, Jun, Sudhyadhom, Atchar, Yang, Xiaofeng

Mapping computed tomography (CT) number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, magnetic

Externí odkaz: http://arxiv.org/abs/2207.13150

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