Authors: Lorenzo Arsini, Andrea Ciardiello, Fabio Massimo D'Amore, Stefano Giagu, Federico Giove, Carlo Mancini-Terracciano, Cecilia Voena
Affiliation: Istituto Superiore di Sanità, Sapienza University of Rome, Università Sapienza Roma, Magnetic Resonance for Brain Investigation Laboratory, Museo Storico della Fisica e Centro di Studi e Ricerche Enrico Fermi
Abstract Preview: Purpose: To leverage newly developed heteronuclear magnetic resonance imaging (MRI) techniques, particularly sodium (23Na) imaging, for identifying potential biomarkers of Alzheimer's disease—such as ...
Authors: Kyle Cuneo, Issam M. El Naqa, Dale W. Litzenberg, Yiming Liu, Xueding Wang, Lise Wei, Wei Zhang, Jiaren Zou
Affiliation: University of Michigan, H. Lee Moffitt Cancer Center
Abstract Preview: Purpose: To quantitatively map 3D dose deposition during radiotherapy, empowering real-time adaptive radiation treatment.
Methods: The research features reconstructing dose deposition from acou...
Authors: Petr Bruza, Jeremy Eric Hallett, Brian W Pogue, Yucheng Tang, Shiru Wang
Affiliation: NVIDIA Corp, Dartmouth College, Thayer School of Engineering, Dartmouth College, University of Wisconsin-Madison, University of Wisconsin - Madison
Abstract Preview: Purpose: Cherenkov imaging allows for real-time visualization of megavoltage X-ray or electron beam delivery during radiation therapy. By using a time-gated intensified CMOS camera synchronized with a...
Authors: Weikang Ai, Xiaoyu Hu, Xun Jia, Kai Yang, Yuncheng Zhong
Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Massachusetts General Hospital, Department of Biomedical Engineering, Johns Hopkins University, Johns Hopkins University
Abstract Preview: Purpose: Real-time tumor tracking is critically important for respiratory motion management for lung cancer radiotherapy. A previously proposed application of a photon counting detector involves measu...
Authors: Beth Bradshaw Ghavidel, Chih-Wei Chang, Yuan Gao, Priyanka Kapoor, Shaoyan Pan, Junbo Peng, Richard L.J. Qiu, Jill Remick, Justin R. Roper, Zhen Tian, Xiaofeng Yang
Affiliation: Whinship Cancer Institute, Emory University, Emory University, University of Chicago, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: Current cone-beam computed tomography (CBCT) typically requires no less than 200 degrees of angular projections, which prolongs scanning time and increases radiation exposure. To address thes...
Authors: Alexander Bookbinder, Matthew Tivnan, Xiangyi Wu, Wei Zhao
Affiliation: Stony Brook Medicine, Massachusetts General Hospital
Abstract Preview: Purpose: To investigate and benchmark a system-adaptive diffusion-based digital breast tomosynthesis (DBT) denoising model for a direct-indirect dual-layer flat panel detector (DI-DLFPD) with a k-edge...
Authors: Xinhui Duan, Roderick W. McColl, Mi-Ae Park, Liqiang Ren, Gary Xu, Kuan Zhang, Yue Zhang
Affiliation: UT Southwestern Medical Center, Department of Radiology, UT Southwestern Medical Center, Imaging Services, UT Southwestern Medical Center
Abstract Preview: Purpose:
Image-based deep-learning noise-reduction techniques have been developed for photon-counting CT (PCCT) to improve image quality with reduced radiation dose. The denoising strength is typic...
Authors: Edward Robert Criscuolo, Chenlu Qin, Deshan Yang, Zhendong Zhang
Affiliation: Duke University, Department of Radiation Oncology, Duke University
Abstract Preview: Purpose:
Low-dose CT (LDCT) imaging minimizes radiation exposure but introduces significant noise, compromising image quality. While deep learning-based denoising models such as HFormer achieve sta...
Authors: Xiangli Cui, Chunyan Fu, Man Hu, Wanli Huo, Jingyu Liu, Jianguang Zhang, Yingying Zhang, Shanyang Zhao
Affiliation: Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, the Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University, Departments of Radiation Oncology, Zibo Wanjie Cancer Hospital, Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Department of Oncology, Xiangya Hospital, Central South University, College of Information Engineering, China Jiliang University, Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences
Abstract Preview: Purpose: To quantify the impact of fine-tuning strategies for pre-trained AI image generation models on glioma MRI image quality and observer performance, and to determine the optimal fine-tuning conf...
Authors: Naruomi Akino, Kirsten Lee Boedeker, Ilmar Hein, Dylan Mather, Akira Nishikori, Daniel W Shin
Affiliation: Canon Medical Systems Corporation, Canon Medical Research USA
Abstract Preview: Purpose: To validate the performance a deep learning reconstruction (DLR) algorithm in an anatomical background compared to a uniform phantom background.
Methods: An analytic forward projection mod...
Authors: Shusen Jing, Qihui Lyu, Dan Ruan, Ke Sheng, Qifan Xu
Affiliation: Department of Radiation Oncology, University of California, Los Angeles, University of California San Francisco, Department of Radiation Oncology, University of California, San Francisco
Abstract Preview: Purpose: Metallic implants can significantly distort sinograms, leading to severe artifacts in computed tomography (CT) reconstructions. Reconstructing CT images containing metal is fundamentally an i...
Authors: Osama R. Mawlawi, Yiran Sun
Affiliation: RICE University, UT MD Anderson Cancer Center
Abstract Preview: Purpose: Conventional PET reconstruction methods often produce noisy images with artifacts due to data/model mismatches and inconsistencies. Recently, deep learning-based conditional denoising diffusi...
Authors: Hania A. Al-Hallaq, Chih-Wei Chang, Anees H. Dhabaan, Yuan Gao, Shaoyan Pan, Junbo Peng, Richard L.J. Qiu, Keyur Shah, Sibo Tian, Zhen Tian, Xiaofeng Yang, David Yu, Jun Zhou
Affiliation: Emory University, Whinship Cancer Institute, Emory University, University of Chicago, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: Traditional cone-beam computed tomography (CBCT) often requires multiple angular projections, increasing radiation exposure and extending scanning times, which may lead to heightened patient ...
Authors: Ruiyan Du, He Huang, Mingzhu Li, Ying Li, Hongyu Lin, Wei Liu, Shihuan Qin, Yiming Ren, Hui Xu, Lian Zhang, Xiao Zhang, Zunhao Zhang
Affiliation: Department of Radiation Oncology, Mayo Clinic, Medical AI Lab, The First Hospital of Hebei Medical University, Hebei Provincial Engineering Research Center for AI-Based Cancer Treatment Decision-Making, The First Hospital of Hebei Medical University, Department of Oncology, The First Hospital of Hebei Medical University
Abstract Preview: Purpose: Monte Carlo (MC) dose calculation is the gold standard in clinical CyberKnife radiation therapy (RT), considering its steep dose gradients and high-freedom non-coplanar beam angles, but extre...
Authors: Yankui Chang, Shijun Li, Xi Pei, Ripeng Wang, Xuanhe Wang, X. George Xu, Qing Zhang, Jingfang Zhao
Affiliation: University of Science and Technology of China, Shanghai proton and heavy ion center, School of Nuclear Science and Technology, University of Science and Technology of China, Anhui Wisdom Technology Co., Ltd.
Abstract Preview: Purpose:
This paper describes disruptive methods using both GPU-based MC simulation and deep-learning (DL)-based MC denoising algorithms, as well as clinical tests involving more than 560 patient p...
Authors: Matthew Brown, Yushi Chang, Jinhyuk Choi, William Silva Mendes, Lei Ren, Aman Sangal, William Paul Segars, Phuoc Tran, Hualiang Zhong
Affiliation: University of Maryland School of Medicine, Department of Radiation Oncology, University of Maryland School of Medicine, Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Duke University Medical Center, Department of Radiation Oncology, Medical College of Wisconsin
Abstract Preview: Purpose: Digital phantoms like XCAT are essential for imaging and treatment optimization in radiology and radiation oncology. However, the lack of realistic textures (HU distribution) in XCAT limits i...
Authors: Jing Qian, Brandon Reber, David M. Routman, Satomi Shiraishi
Affiliation: Mayo Clinic
Abstract Preview: Purpose: The dose distribution in proton radiotherapy (PRT) is characterized by sharp gradients, posing a challenge for machine learning-based dose prediction. While denoising with diffusion processes...