Authors: Jenghwa Chang, Kuan Huang, Lyu Huang, Jason Lima, Jian Liu, Farzin Motamedi
Affiliation: Northwell, Department of Computer Science and Technology, Kean University, Physics and Astronomy, Hofstra University, Hofstra University Medical Physics Program
Abstract Preview: Title: A Deep Learning Approach to the Prediction of Gamma Passing Rates in VMAT Radiotherapy Plans for Adaptive Treatment.
Purpose: This study aims to develop a deep learning algorithm to predict ...
Authors: Yu Chang, Mei Chen
Affiliation: Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine
Abstract Preview: Purpose: Spot weights optimization, as a critical step in the proton therapy, is often time-consuming and labor-intensive. Deep learning, with its powerful learning and computational efficiency, can e...
Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Gregory Szalkowski, 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: Respiratory motion management is crucial for accurate radiation delivery to moving targets while protecting healthy tissue, relying on time-resolved volumetric imaging and continuous deformab...
Authors: Maria Jose Almada, Bruno Forti, Andres Lima, Carlos Daniel Venencia
Affiliation: Instituto Zunino - Fundacion Marie Curie
Abstract Preview: Purpose:
To automate the planning of radiotherapy treatments for bone metastases using a script in the ECLIPSE planning system version 15.6 with a graphical interface.
Methods:
A script was d...
Authors: Hongyi Jiang, Fang-Fang Yin
Affiliation: Duke University, Medical Physics Graduate Program, Duke Kunshan University
Abstract Preview: Purpose:
Imaging moving tissues using PET-CT can be difficult. Separating signal into phases during construction reduces signal count and increases influence of noise. Algorithms that use signal fr...
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: Serdar Charyyev, Cynthia Fu-Yu Chuang, Veng Jean Heng, Lianli Liu, Lei Xing, Yong Yang
Affiliation: Department of Radiation Oncology, Stanford University
Abstract Preview: Purpose: To replace large finite-size photon phase space files with a compact neural network capable of generating an infinite number of particles.
Methods: Three separate models were developed to ...
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: Ahssan Balawi, Peter Jermain, Timothy Kearney, Sonali Rudra, Michael H. Shang, Markus Wells, Mohammad Zarenia
Affiliation: Department of Radiation Medicine, MedStar Georgetown University Hospital
Abstract Preview: Purpose: To investigate the applicability and accuracy of a deep learning (DL) model in predicting radiation dose distribution for breast cancer patients treated with pencil-beam-scanning proton radio...
Authors: Anke Henning, Mahrshi Jani, Tianyu Wang, Andrew Wright, Xinyu Zhang
Affiliation: Advanced Imaging Research Center (AIRC), UT Southwestern Medical Center
Abstract Preview: Purpose: Proton MRSI offers critical metabolic insights into diseased brain processes but is prone to artifacts, and current post-processing methods are often insufficient, resulting in low-quality da...
Authors: Dirk Grunwald, Hans Herzog, Hidehiro Iida, N. Jon Shah, Usman Khalid, Manfred Lennartz, Philipp Lohmann, Ceren Memis, Tobias Meurer, Claudia Regio Brambilla, Jürgen Scheins, Lutz Tellmann, Christoph W. Lerche, Martin Wiesmann, Karl Ziemons
Affiliation: FH Aachen University of Applied Sciences, Department of Chemistry and Biotechnology, Clinic for Diagnostic and Interventional Neuroradiology, Uniklinik Aachen,, Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH,, Central Institute for Engineering, Electronics and Analytics (ZEA-1), Forschungszentrum, Turku PET Center, Institute of Biomedicine, Faculty of Medicine, University of Turku,
Abstract Preview: Purpose: Quantitative brain studies with positron emission tomography (PET) often require an arterial input function (AIF), which traditionally requires arterial cannulation. However, this is invasive...
Authors: Rafe A. McBeth, Ayoola Okuribido, Rodney D. Wiersma
Affiliation: Department of Radiation Oncology, University of Pennsylvania, University of Pennsylvania, UCLA
Abstract Preview: Purpose: To develop a method for standardizing data collected during quality assurance checks across institutions using language models.
Background: QA procedures and data management can vary widel...
Authors: Rafe A. McBeth, Ayoola Okuribido, Rodney D. Wiersma
Affiliation: Department of Radiation Oncology, University of Pennsylvania, University of Pennsylvania, UCLA
Abstract Preview: Purpose: To develop a method for standardizing data collected during quality assurance checks across institutions using language models.
Background: QA procedures and data management can vary widel...
Authors: Marissa Brown, Geoffrey D. Clarke, Luke Norton
Affiliation: University of Texas Health Science Center at San Antonio
Abstract Preview: Purpose: To evaluate how different learning strategies affect convolutional neural network (CNN) estimates of the liver's intravoxel incoherent motion (IVIM) parameters.
Methods: A 3-stage U-Net wa...
Authors: Ara Alexandrian, Sadiki Daniel
Affiliation: Louisiana State University, Mary Bird Perkins Cancer Center
Abstract Preview: Purpose: To develop a learning-to-optimize machine learning model that accelerates optimization in VMAT treatment planning by training on prostate patient data.
Methods: A treatment plan dataset of...
Authors: Logan M. Bateman, Xu Cao, Jonathan T. Elliott, Lillian A. Fisher, Ida Leah Gitajn, Xinyue Han, Eric R. Henderson, Shudong Jiang, Jessica M. Sin, Yue Tang
Affiliation: Dartmouth College, Dartmouth-Hitchcock Medical Center
Abstract Preview: Purpose: Adequate tissue perfusion is essential for fracture healing and infection prevention, as it supplies oxygen, nutrients and antibiotics to the injury area. However, current methods of assessin...
Authors: Giavanna Luisa Jadick, Patrick J La Riviere
Affiliation: University of Chicago
Abstract Preview: Purpose: We assess two multi-measurement acquisition schemes for material decomposition with x-ray phase-contrast imaging (XPCI); demonstrating for the first time that multi-distance imaging can match...
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: Yang Sheng, Qingrong Jackie Wu, Qiuwen Wu, Xin Wu, Dongrong Yang
Affiliation: Duke University Medical Center
Abstract Preview: Purpose: Gradient-based optimization is the standard approach for IMRT fluence map optimization (FMO). Recently, a machine learning (ML)-assisted framework using a one-layer neural network was propose...
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:
This work demonstrates how existing software, when creatively adapted, can address a wide range of clinical challenges. By focusing on data exploration and application-specific modificatio...
Authors: Michael Dingfelder
Affiliation: East Carolina University
Abstract Preview: Purpose:
To study and accurately evaluate the influence of the Fermi Density effect on stopping powers and cross sections of charged particles in liquid water used in Monte Carlo radiation transpor...
Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Zi Yang, 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: Dose prediction (DP) is essential in guiding radiotherapy planning. However, current DP models for intensity-modulated radiation therapy (IMRT) primarily rely on fixed-beam orientations and a...
Authors: Ming Dong, Carri K. Glide-Hurst, Behzad Hejrati, Joshua Pan, Yuhao Yan
Affiliation: Department of Computer Science, Wayne State University, Departments of Human Oncology and Medical Physics, University of Wisconsin-Madison, Department of Human Oncology, University of Wisconsin-Madison
Abstract Preview: Purpose: Upright patient positioners and vertical CT reduce tumor motion and stabilize internal anatomy during treatment delivery. Yet, to fully exploit the advantages of upright, translation of stand...
Authors: Ilias Gatos, Stavros Grigoriadis, George C. Kagadis, Maria Karamesini, Paraskevi Katsakiori, Dimitris N. Mihailidis, Stavros Spiliopoulos, Efstratios Syrmas, Ioannis Theotokas, Stavros Tsantis, Pavlos Zoumpoulis
Affiliation: Diagnostic Echotomography, University of Pennsylvania, University of Athens, University of Patras
Abstract Preview: Purpose: To detect prostate lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images which is a particularly difficult task due to the heterogeneous and inconsistent representa...
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...
Authors: Bryan Bednarz, Larry A. DeWerd, Sean Jollota, Ahtesham Ullah Khan, Ohyun Kwon, Jeff Radtke
Affiliation: Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin - Madison
Abstract Preview: Purpose: The accurate quantification of absorbed dose is essential for alpha-emitting radionuclides used in radiopharmaceutical therapy (RPT). This study presents the first direct comparison of physic...
Authors: Mark Anastasio, Zong Fan, Hua Li, Changjie Lu, Lulu Sun, Xiaowei Wang, Zhimin Wang, Michael Wu
Affiliation: University of Illinois at Urbana-Champaign, University of Illinois at Chicago, Washington University School of Medicine, University of Illinois Urbana-Champaign, Washington University in St. Louis, University Laboratory High School
Abstract Preview: Purpose: Histological whole slide images (WSIs) are vital in clinical diagnosis. Although deep learning (DL) methods have achieved great success in this task, they often lack interpretability. Traditi...