Authors: Jie Deng, Xiaoxue Qian, Hua-Chieh Shao, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Based on a 3D pre-treatment MRI scan, we developed DREME-MR to jointly reconstruct the reference patient anatomy and a data-driven, patient-specific cardiorespiratory motion model. Via a moti...
Authors: B. Gino Fallone, Keith D. Wachowicz, Mark G. Wright, Jihyun Yun
Affiliation: Medical Physics Division, Department of Oncology, University of Alberta, Dept. of Medical Physics, Cross Cancer Institute and Dept. of Oncology, University of Alberta, Medical Physics Division, Department of Oncology, University of Alberta and Department of Medical Physics, Cross Cancer Institute, Dept. of Medical Physics, Cross Cancer Institute and Dept. of Oncology, University of Alberta; MagnetTx Oncology Solutions, www.magnetTX.com
Abstract Preview: Purpose: This work focusses on a hybrid Principal Component Analysis (PCA) based MR acceleration method for prospectively acquired undersampled data on a hybrid MR-Linac system for real-time target-tr...
Authors: Louis Archambault, Luc Beaulieu, Alexis Horik, Sajjad Ahmad Khan
Affiliation: Département de Physique, de Génie Physique et D'optique, et Centre de Recherche sur le Cancer, Université Laval, Département de physique, de génie physique et d'optique, Université Laval
Abstract Preview: Purpose: This study presents a novel 2D scintillation dosimeter leveraging long scintillating fibers for quality assurance (QA) in radiotherapy. The primary goal is to optimize critical parameters suc...
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: Kimberly Chan, Anke Henning, Mahrshi Jani, Andrew Wright, Xinyu Zhang
Affiliation: Advanced Imaging Research Center (AIRC), UT Southwestern Medical Center
Abstract Preview: Purpose: To evaluate the performance of multiple deep learning architectures for MRSI reconstruction and determine their effectiveness in maintaining high-resolution metabolite mapping while reducing ...
Authors: Chia-Ho Hua, Jirapat Likitlersuang, Jinsoo Uh
Affiliation: St. Jude Children's Research Hospital
Abstract Preview: Purpose: AI-based fast MRI, which reconstructs images from undersampled k-space data, has not yet been tailored for RT planning. This study aims to evaluate the fast MRI performance of our recently pr...
Authors: Oluyemi Bright Aboyewa, KyungPyo Hong, Daniel Kim
Affiliation: Department of Radiology, Northwestern University
Abstract Preview: Purpose: While non-Cartesian MRI is desirable for fast imaging with high spatial resolution and robustness to motion, it requires long post-processing times. Preconditioning with an adequate density c...
Authors: Weixing Cai, Laura I. Cervino, Yabo Fu, Licheng Kuo, Tianfang Li, Xiang Li, Jean M. Moran, Huiqiao Xie, Hao Zhang
Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center
Abstract Preview: Purpose: This study introduces a novel spatiotemporal Gaussian neural representation framework to reconstruct high-temporal dynamic CBCT images from 1-minute acquisition, preserving motion dynamics an...
Authors: Evan Calabrese, Scott R. Floyd, Kyle J. Lafata, Zachary J. Reitman, Eugene Vaios, Chunhao Wang, Lana Wang, Deshan Yang, Zhenyu Yang, Jingtong Zhao
Affiliation: Duke University, Department of Radiation Oncology, Duke University, Duke Kunshan University
Abstract Preview: Purpose:
This study proposes a novel neural ordinary differential equation (NODE) framework to distinguish post-SRS radionecrosis from recurrence in brain metastases (BMs). By integrating imaging f...
Authors: Mark Anastasio, Hua Li, Zhuchen Shao
Affiliation: Washington University School of Medicine, University of Illinois Urbana-Champaign
Abstract Preview: Purpose: Ill-conditioned reconstruction problems in medical imaging, such as those arising from undersampled k-space data in MRI, can result in degraded image quality and clinical task-orientated perf...
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: Kevin Barker, David Jeffrey Contella, Chandima Edirisinghe, Aaron Fenster, Douglas A Hoover, Elizabeth Huynh
Affiliation: Robarts Research Institute, University of Western Ontario, London Health Sciences Center, Department of Radiation Oncology, London Health Sciences Centre
Abstract Preview: Purpose: We aim to develop a system that integrates micro-ultrasound into focal prostate cancer radiotherapy. This requires developing a mechatronic stepper capable of performing motorized rotation of...
Authors: Daniel O Connor, Mary Feng, Hui Lin, Hengjie Liu, Xin Miao, Michael Ohliger, Jess E. Scholey, Ke Sheng, DI Xu, Wensha Yang, Yang Yang
Affiliation: UCSF, University of California, Los Angeles, Department of Radiation Oncology, University of California San Francisco, Department of Radiation Oncology, University of California, San Francisco, Department of Radiation Oncology, University of California at San Francisco, University of San Francisco, Department of Radiology, University of California, San Francisco, University of California San Francisco, Siemens Medical Solutions USA Inc.
Abstract Preview: Purpose: The scanning time for a fully sampled MRI is lengthy. Compressed sensing (CS) has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is...
Authors: Karyn A Goodman, Yang Lei, Tian Liu, Charlotte Elizabeth Read, Jing Wang, Qian Wang, Jiahan Zhang
Affiliation: Icahn School of Medicine at Mount Sinai, Department of Radiation Oncology, Beth Israel Deaconess Medical Center
Abstract Preview: Purpose: Accurate motion management in MRI-guided radiotherapy (MRIgRT) relies on real-time volumetric MRI to track intra-fractional anatomical changes. Dense k-space sampling, while capable of produc...
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: Mary Feng, Yi Lao, Hui Lin, Hengjie Liu, Xin Miao, Michael Ohliger
Affiliation: University of California, Los Angeles, Department of Radiation Oncology, University of California San Francisco, Department of Radiation Oncology, City of Hope National Medical Center, University of California San Francisco, Siemens Medical Solutions USA Inc.
Abstract Preview: Purpose: 4D MRI with high spatiotemporal resolution is vital to characterize the tumor/tumor motion for liver radiotherapy. However, high-quality 4D MRI requires an impractically long scanning time fo...
Authors: Yong Chen, Gilberto Gonzalez, Omprakash Gottam, Liangzhong Xiang
Affiliation: University of California, Irvine, Koneru Lakshmaiah Education Foundation, University of Texas at San Antonio., University of Oklahoma Health Science Center
Abstract Preview: Purpose: Clinics currently lack techniques for real-time, in vivo monitoring of radiation therapy (RT), which is desirable for precise treatment. Radiation-induced acoustic computed tomography (RACT) ...