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: Ila Farhang, Ryan D. Foster, Devin Heitz, Jordan B Lunsford, Ashkan Shafiee
Affiliation: Atrium Health Wake Forest Baptist, Atrium Health/LCI Cabarrus
Abstract Preview: Purpose: American Association of Physicists in Medicine (AAPM) Task Group (TG) -51 is a widely utilized protocol for the absolute calibration of linear accelerators. The purpose of this project was fo...
Authors: Weiguo Lu, Jax Luo, Xiaoxue Qian, Hua-Chieh Shao, Guoping Xu, 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, Harvard Medical School
Abstract Preview: Purpose:
Semi-supervised segmentation leverages sparse annotation information to learn rich representations from combined labeled and label-less data for segmentation tasks. This study leverages th...
Authors: Yunxiang Li, Xinlong Zhang, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Acquiring high-resolution (HR) proton density (PD) images is time-consuming, while lower-resolution (LR) PD scans are faster but can lack sufficient details. We propose CycleHR, a T2-contrast...
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 ‘big data’ radiotherapy planning databases of 100,000 patients or more. Anatomic site of treatment is an important stratification for almo...
Authors: Kyle J. Lafata, Xiang Li, Megan K. Russ, Zion Sheng
Affiliation: Duke University, Department of Radiation Oncology, Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System
Abstract Preview: Purpose: To adapt Vision-Language Foundational Models (VLFM) to perform HNSCC tumor grading on H&E whole slide images (WSI) via attention-based multiple instance learning (ABMIL).
Methods: We utili...
Authors: Xuelian Chen, John Ginn, Zhuhong Li, Kaizhong Shi, Chunhao Wang, Jianliang Wang, Chuan Wu, Zhenyu Yang, Fang-Fang Yin, Jingtong Zhao
Affiliation: The First People's Hospital of Kunshan, Duke University, Medical Physics Graduate Program, Duke Kunshan University, Duke Kunshan University, Department of Radiation Oncology, Duke Kunshan University
Abstract Preview: Purpose: Developing deep learning-based models for accurate automated breast cancer diagnosis from mammography presents significant challenges due to the small size and subtle nature of breast lesions...
Authors: Kenneth A. Fetterly
Affiliation: Mayo Clinic
Abstract Preview: Purpose: Among the limitations of channelized Hotelling T2 type model observers (CHO) applied to medical imaging systems is that they reduce 2D image detail to a singular value and are only applicable...
Authors: Diana Cardona, Casey C. Heirman, William Jeck, Kyle J. Lafata, Lauren M. Neldner, Jeffrey S. Nelson, Megan K. Russ, Ehsan Samei
Affiliation: Duke University, Department of Radiation Oncology, Duke University, Department of Pathology, Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System
Abstract Preview: Purpose: Display image accuracy is critical for digital diagnostic fields, such as radiology and digital pathology. While the AAPM TG-18 test patterns are established for grayscale radiology monitor Q...
Authors: Edwin Quashie, Yun Wang
Affiliation: Indiana University Medical School, Indiana University School of Medicine, Department of Radiation Oncology
Abstract Preview: Purpose: To provide a formal evaluation for the TrueBeam linac matching process before, during and after the commission of the newly installed TrueBeam linac which we want to match to an existing True...
Authors: Resat Aydin, Joseph Barbiere, Brett Lewis, Roland Teboh
Affiliation: HUMC, Hackensack University Medical Center
Abstract Preview: Purpose:
Accurately compensating for respiratory-induced tumor motion is critical in BgRT, where precise delineation of volumes ensures effective dose delivery. We propose an integrated approach th...
Authors: Pouya Azarbar, Nima Kasraie, Peyman Sheikhzadeh
Affiliation: UT Southwestern Medical Center, Shahid Beheshti University of Medical science, Imam Khomeini Hospital Complex,Tehran University of Medical Sciences
Abstract Preview: Purpose: Positron Emission Tomography (PET) is crucial for diagnosing and monitoring diseases due to its functional imaging capabilities. However, its high cost, significant radiation exposure, and li...
Authors: B. Gino Fallone, Alireza Gazor, Andrei D. Ghila, Gawon Han, Patricia A. K. Oliver, Michael W. Reynolds, Keith D. Wachowicz, Tania Rosalia Wood, Shima Y. Tari, Eugene Yip
Affiliation: Medical Physics Division, Department of Oncology, University of Alberta, Nova Scotia Health, Dept. of Medical Physics and Dalhousie University, Dept. of Physics and Atmospheric Science, Dept. of Radiation Oncology, Dept. of Medical Physics, Cross Cancer Institute and Dept. of Oncology, University of Alberta; MagnetTx Oncology Solutions, www.magnetTX.com, Department of Medical Physics, Arthur J. E. Child Comprehensive Cancer Centre, Dept. of Medical Physics, Cross Cancer Institute and Dept. of Oncology, University of Alberta, Department of Medical Physics, BC Cancer, Medical Physics Division, Department of Oncology, University of Alberta and Department of Medical Physics, Cross Cancer Institute
Abstract Preview: Purpose: To develop and validate a high-fidelity Monte-Carlo (MC) model of a 0.5T bi-planar Linac-MR in TOPAS, focusing on accurate Multileaf Collimator (MLC) modelling and positioning for open apertu...
Authors: Ryan Alden, Tithi Biswas, Kaushik Halder, Felix Maria-Joseph, Michael Mix, Rihan Podder, Tarun Kanti Podder
Affiliation: SUNY Upstate Medical University, IIT-Roorkee, University of Florida
Abstract Preview: Purpose: Early-stage NSCLC patients undergoing SBRT often die due to intercurrent illnesses. However, prediction of overall survival (OS) remains crucial due to the risk of disease recurrence. This st...
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: Michael G. Mitch, Csilla I. Szabo-Foster
Affiliation: National Institute of Standards and Technology
Abstract Preview: Purpose: The U.S. air-kerma strength standard for low-dose-rate (LDR) low-energy photon-emitting brachytherapy sources provides measurement traceability for calibrations at the AAPM Accredited Dosimet...
Authors: Sam Beddar, Brett Bocian, David B. Flint, Benjamin Abraham Insley, Patrick James Jensen, Rachael M. Martin Paulpeter, Joshua S. Niedzielski, Luis Augusto Perles, Reza Reiazi, Gabriel O. Sawakuchi
Affiliation: University of Miami, MD Anderson Cancer Center, The University of Texas MD Anderson Cancer Center, Empyrean Medical Systems, UT MD Anderson Cancer Center, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center
Abstract Preview: Purpose: The Harrison-Anderson-Mick (HAM) applicator is an Ir-192 High-Dose-Rate (HDR) intraoperative radiotherapy device. It features a silicone body with embedded catheters that guide the Ir-192 sou...
Authors: Ibtisam Almajnooni, Victor Cobilean, Milos Manic, Harindra Sandun Mavikumbure, Elisabeth Weiss, Lulin Yuan
Affiliation: Virginia Commonwealth University
Abstract Preview: Purpose: This study aims to optimize the 3D U-Net architecture for dose prediction in lung cancer radiation therapy (RT) plans, particularly in scenarios with limited clinical data, as well as to quan...
Authors: Michael Bowers, Patrik Brodin, Madhur Garg, Rafi Kabarriti, William P. Martin, Todd R. McNutt, Julie Shade, Wolfgang A. Tomé, Christian Velten
Affiliation: Johns Hopkins University, Oncospace, Inc., Montefiore Medical Center
Abstract Preview: Purpose: Development of an automated planning tool utilizing AI generated patient-specific dose-volume histogram predictions for rapid H&N plan generation.
Methods: Planning best-practices were dev...
Authors: Izabella L. Barreto, Benjamin Taylor Heggie, Stephanie M. Leon
Affiliation: University of Florida College of Medicine, University of Florida
Abstract Preview: Purpose: Filament deposition modeling (FDM) 3D printers may utilize proprietary calibration methods inadequate for medical imaging. Proper techniques are necessary to achieve imaging uniformity standa...
Authors: Asma Amjad, Slade J. Klawikowski, Natalya V. Morrow, Haidy G. Nasief, Eric S. Paulson, An Tai, Hualiang Zhong
Affiliation: Department of Radiation Oncology, Medical College of Wisconsin
Abstract Preview: Purpose: Accurate and precise linac-based SRS commissioning can be very challenging. Thus, it is important to increase the confidence in the measurement at each step prior to end-to-end testing. The p...
Authors: Avinash Mudireddy, Nathan Shaffer, Joel J. St-Aubin
Affiliation: University of Iowa
Abstract Preview: Purpose: This work demonstrates preliminary results in training a reinforcement learning (RL) network to perform VMAT machine parameter optimization.
Methods: We implemented a policy gradient RL al...