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Results for "multi modal": 118 found

23na Magnetic Resonance Imaging k-Space Denoising

Authors: Lorenzo Arsini, Andrea Ciardiello, Fabio Massimo D'Amore, Stefano Giagu, Federico Giove, Carlo Mancini-Terracciano, Cecilia Voena

Affiliation: Istituto Superiore di SanitĂ , Sapienza University of Rome, UniversitĂ  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 ...

3D Renal Microdosimetry: Evaluating Radiopharmaceuticals Using Histology-Based 3D Models

Authors: John P. Aris, Wesley E. Bolch, Madison Bushloper, Lauren E Ellis, Adam Grey Haneberg, Elizabeth Martin, Bonnie N. C. President, Andrew Robert Sforza, Alexander Zorrilla

Affiliation: Johns Hopkins University, Biomedical Engineering, University of Florida

Abstract Preview: Purpose: To develop six additional high-fidelity 3D models of kidney renal cortical labyrinth from H&E-stained histology slides to support radiation dosimetry in radiopharmaceutical therapies (RPTs).<...

A Combination of Radiomics and Dosiomics for Gross Tumor Volume Regression in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR)

Authors: Hao Peng, Yajun Yu

Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center

Abstract Preview: Purpose: Personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is a novel ablative radiation dosing scheme developed by our institution. This study aims to establish a regression...

A Dynamic Reconstruction and Motion Estimation Framework for Cardiorespiratory Motion-Resolved Real-Time Volumetric MR Imaging (DREME-MR)

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...

A Ground Truth Label-Mediated Method for Improved Bone and Gas Cavity Definition for MRI-Guided Online Adaptive Radiotherapy Workflows Using Synthetic CT Images.

Authors: Benito De Celis Alonso, Braian Adair Maldonado Luna, Gerardo Uriel Perez Rojas, René Eduardo Rodríguez-Pérez, Kamal Singhrao

Affiliation: Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute, Harvard Medical School, Faculty of Physics and Mathematics, Benemérita Universidad Autónoma de Puebla

Abstract Preview: Purpose: Artificial Intelligence (AI)-generated synthetic CT (sCT) images can be used to provide electron densities for dose calculation for online adaptive MRI-guided stereotactic body radiotherapy (...

A Method to Expedite Quality Assurance for Head and Neck Ctvs with Lymph Node Level Auto-Autocontouring and Identification

Authors: Beth M. Beadle, Adrian Celaya, Laurence Edward Court, David Fuentes, Anna Lee, Tze Yee Lim, Dragan Mirkovic, Amy Moreno, Raymond Mumme, Tucker J. Netherton, Callistus M. Nguyen, Jaganathan A Parameshwaran, Jack Phan, Carlos Sjogreen, Sara L. Thrower, Congjun Wang, He C. Wang, Xin Wang

Affiliation: Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Department of Radiation Oncology, Stanford University, The University of Texas MD Anderson Cancer Center, MD Anderson Cancer Center, MD Anderson, Rice University, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose: Quality assurance of target volumes from radiotherapy clinical trials is a labor and resource intensive task. The purpose of this work is to quantify the accuracy of a tool that automatically...

A Multi-Omics Approach for Predicting Acute Hematologic Toxicity in Patients with Cervical Cancer Undergoing External-Beam Radiotherapy

Authors: Sijuan Huang, Yongbao Li

Affiliation: Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Sun-Yat sen University Cancer Center

Abstract Preview: Purpose: Hematologic toxicity (HT) is one of the most prevalent treatment-related toxicities experienced by locally advanced cervical cancer (LACC) patients receiving radiotherapy (RT). This study aim...

A Multi-Regional and Multi-Omics Approach to Predict Penumonitis in Patients with Locally Advanced Non-Small Cell Lung Cancer in Nrg Oncology Trial RTOG 0617

Authors: Katelyn M. Atkins, Indrin J. Chetty, Elizabeth M. McKenzie, Taman Upadhaya, Samuel C. Zhang

Affiliation: Department of Radiation Oncology,Cedars-Sinai Medical Center, Cedars-Sinai Medical Center

Abstract Preview: Purpose:
We explored a multi-regional and multi-omics approach to extract CT-based radiomics and 3D dosiomics features to predict radiation pneumonitis (RP) in patients with locally advanced Non-Sm...

A Semi-Automated Landmark Identification Framework for Liver MR-CT Image Pairs: Towards a Multi-Modality DIR Benchmark Dataset

Authors: Deshan Yang, Zhendong Zhang

Affiliation: Duke University, Department of Radiation Oncology, Duke University

Abstract Preview: Purpose:
The evaluation of deformable image registration (DIR) algorithms is crucial for improving accuracy and clinical adoption. However, reliable benchmarks, especially for inter-modality regist...

A Two-Layer, Two-Task Prediction Model Based on 3D Imaging and Residual Networks for Mid-Chemoradiation Tumor Response Prediction on FDG PET for La-NSCLC

Authors: Stephen R. Bowen, Chunyan Duan, Daniel S. Hippe, Qiantuo Liu, Jing Sun, Jiajie Wang, Shouyi Wang, Faisal Yaseen, Xiaojing Zhu

Affiliation: Tongji University, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Shanghai University of Electric Power, Fred Hutchinson Cancer Center, University of Texas at Arlington

Abstract Preview: Purpose: Accurate prediction of patient response to radiotherapy plays a crucial role in monitoring disease progression and assessing treatment efficacy, enabling clinicians to develop personalized th...

A Vision-Language Model for T1-Contrast Enhanced MRI Generation for Glioma Patients

Authors: Zachary Buchwald, Zach Eidex, Richard L.J. Qiu, Justin R. Roper, Mojtaba Safari, Hui-Kuo Shu, Xiaofeng Yang, David Yu

Affiliation: Emory University and Winship Cancer Institute, Emory University, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose: Gadolinium-based contrast agents (GBCA) are commonly used for patients with gliomas to delineate and characterize the brain tumors using T1-weighted (T1W) MRI. However, there is a rising conc...

AI Auto-Contouring for CT-Based High-Dose-Rate Interstitial Brachytherapy of Cervical Cancer: Implications for Organ-at-Risk (OAR) Contouring and Dosimetric Analysis

Authors: Indrin J. Chetty, Jing Cui, Mitchell Kamrava, Tiffany M. Phillips, Jennifer M. Steers, Brad Stiehl

Affiliation: Department of Radiation Oncology,Cedars-Sinai Medical Center, Cedars-Sinai Medical Center

Abstract Preview: Purpose: Auto-contouring for HDR interstitial brachytherapy can be confounded by large deformation in anatomy and image quality. Here we evaluated the performance of an AI-based auto-contouring softwa...

AI-Based SBRT Dose Prediction Directly from Diagnostic PET/CT: Applications for Multi-Disciplinary Lung Cancer Care

Authors: Pradeep Bhetwal, Wookjin Choi, Adam Dicker, Rupesh Ghimire, Yingcui Jia, Lauren Nkwonta, Yevgeniy Vinogradskiy, Wentao Wang, Maria Werner-Wasik

Affiliation: Thomas Jefferson University

Abstract Preview: Purpose: Multi-disciplinary clinics are becoming standard of care for patients with lung cancer treated with SBRT. To improve clinical decision support in a multi-disciplinary clinic, it would be bene...

Abdomen CT Multi-Organ Segmentation Using Multi-Granularity Feature Extraction

Authors: Zilei Fu, Yi Guo, Wanli Huo, Hongdong Liu, Laishui Lyu, Zhao Peng, Yaping Qi, Senting Wang

Affiliation: Department of Radiotherapy, cancer center, The First Affiliated Hospital of Fujian Medical University, the Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University, Division of lonizing Radiation Metrology, National Institute of Metrology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, China Jiliang University, Department of Oncology, Xiangya Hospital, Central South University

Abstract Preview: Purpose: Medical image boundaries are commonly characterized by smooth gray-level transitions, resulting in pixel-level segmentation errors near these blurred boundaries. To address this, we developed...

Addressing Missing MRI Sequences: A DL-Based Region-Focused Multi-Sequence Framework for Synthetic Image Generation

Authors: Amir Abdollahi, Oliver JĂ€kel, Maxmillian Knoll, Rakshana Murugan, Adithya Raman, Patrick Salome

Affiliation: UKHD & DKFZ, Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), German Cancer Research Centre(DKFZ), DKFZ, MGH

Abstract Preview: Purpose:
Missing MRI sequences, due to technical issues in data handling or clinical constraints like contrast agent intolerance, limit the use of medical imaging datasets in computational analysis...

Advancing Magnetic Resonance Imaging (MRI) Safety Clearance: Utilizing Dual-Energy CT (DECT) Material Decomposition for Foreign Object Assessment

Authors: Anzi Zhao

Affiliation: Northwestern Medicine

Abstract Preview: Purpose: This study investigates the utility of Dual-Energy Computed Tomography (DECT) material decomposition in resolving Magnetic Resonance Imaging (MRI) safety concerns for patients with unidentifi...

Adversarial Diffusion-Based Self-Supervised Learning for High-Resolution MR Imaging

Authors: Zachary Buchwald, Chih-Wei Chang, Zach Eidex, Richard L.J. Qiu, Mojtaba Safari, Shansong Wang, Xiaofeng Yang, David Yu

Affiliation: Emory University and Winship Cancer Institute, Emory University, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose: MRI offers excellent soft tissue contrast for diagnosis and treatment but suffers from long acquisition times, causing patient discomfort and motion artifacts. To accelerate MRI, supervised d...

An Efficient Deep Learning Model with Multi-Scale Integration for Automated Pancreas Segmentation on MR Images

Authors: Jingyun Chen, Yading Yuan

Affiliation: Columbia University Irving Medical Center, Department of Radiation Oncology

Abstract Preview: Purpose: To develop and evaluate the Scale-attention network (SANet) for automated pancreas segmentation on MR images.
Methods: To develop SANet, we extended the classic U-Net design with a dynamic...

Automated Full-Body Tumor Segmentation from PET/CT Images

Authors: Austin Castelo, Xinru Chen, Caroline Chung, Laurence Edward Court, Jaganathan A Parameshwaran, Zhan Xu, Jinzhong Yang, Yao Zhao

Affiliation: The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose:
To develop a deep learning-based segmentation model to automatically delineate tumors from full-body PET/CT images.
Methods:
PET/CT image pairs of 91 patients were collected for this...

Automated Multimodal Image Registration for Prostate Bed Radiation Treatment

Authors: Quan Chen, Xue Feng, Chunhui Han, Gaofeng Huang, Trevor Ketcherside, Yi Lao, Yun Rose Li, An Liu, Bo Liu, Kun Qing, William T. Watkins

Affiliation: Graduate Program in Bioengineering, University of California San Francisco-UC Berkeley, Department of Radiation Oncology, City of Hope National Medical Center, Mayo Clinic Arizona, Carina Medical LLC

Abstract Preview: Purpose: New treatment platforms such as Ethos (Varian Medical Systems) allow the introduction of multi-modal imaging into adaptive radiotherapy workflow to facilitate an up-to-date view of patients’ ...

Automated Treatment Planning for Linac-Based Stereotactic Radiosurgery of Intraocular Malignancies Via Hyperarc Knowledge-Based Planning

Authors: Chase Cochran, Shane McCarthy, Damodar Pokhrel, William St Clair

Affiliation: University of Kentucky, Department of Radiation Medicine, University of Kentucky, Radiation Medicine

Abstract Preview: Purpose: Manually generating intraocular stereotactic radiosurgery (SRS) plans involves significant challenges, including lengthy planning times and inter-planner variability. Knowledge-based SRS plan...

Automatic Tumor Segmentation and Catheter Detection from MRI for Cervical Cancer Brachytherapy Using Uncertainty-Aware Dual Convolution-Transformer Unet

Authors: Majd Antaki, Rohini Bhatia, Gayoung Kim, Yosef Landman, Junghoon Lee, Akila N. Viswanathan

Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Physics and Advanced Development Elekta

Abstract Preview: Purpose: Brachytherapy is a standard radiation therapy approach for cervical cancer, which directly delivers radiation source to the tumor using catheters. Treatment planning requires identification o...

BEST IN PHYSICS MULTI-DISCIPLINARY: Building a Cross-Modality Model to Integrate Bio-Clinical Features, Anatomical MRI, and White-Matter Pathlength Mapping for Personalized Glioblastoma RT Planning

Authors: Steve Braunstein, Angela Jakary, Hui Lin, Bo Liu, Janine Lupo, Tiffany Ngan, Ke Sheng, Nate Tran

Affiliation: Radiation Oncology, University of California San Francisco, Graduate Program in Bioengineering, University of California San Francisco-UC Berkeley, Department of Radiation Oncology, University of California San Francisco, Department of Radiology and Biomedical Imaging, University of California San Francisco, Department of Radiation Oncology, University of California, San Francisco

Abstract Preview: Purpose: Current RT clinical target volumes (CTVs) for Glioblastoma (GBM) employ 2cm isotropic expansions of gross tumor volumes. However, studies showed patients still experience progression beyond t...

BEST IN PHYSICS MULTI-DISCIPLINARY: Foundation Model-Empowered Unsupervised 3D Deformable Medical Image Registration

Authors: Xianjin Dai, PhD, Zhuoran Jiang, Lei Ren, Lei Xing, Zhendong Zhang

Affiliation: University of Maryland School of Medicine, Department of Radiation Oncology, Stanford University, Duke University, Stanford University

Abstract Preview: Purpose: Unsupervised deep learning has shown great promise in deformable image registration (DIR). These methods update model weights to optimize image similarity without necessitating ground truth d...

BEST IN PHYSICS MULTI-DISCIPLINARY: Motion-Resolved Dynamic CBCT Reconstruction Using Prior-Model-Free Spatiotemporal Gaussian Representation (PMF-STGR)

Authors: Hua-Chieh Shao, Chenyang Shen, Jiacheng Xie, Shunyu Yan, 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, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, The University of Texas at Dallas

Abstract Preview: Purpose: Motion-resolved CBCT imaging, which reconstructs a dynamic sequence of CBCTs reflecting intra-scan motion (one CBCT per x-ray projection), is highly desired for regular/irregular motion chara...

Beyond Correlation: An Ultra-Large Physics-Driven Vascularized Tumor Model to Bridge Feature Formation with Underlying Biology

Authors: Jiayi Du, Lihua Jin, Ke Sheng, Yu Zhou

Affiliation: Harvard University, University of California, San Francisco, UCLA, Department of Radiation Oncology, University of California, San Francisco

Abstract Preview: Purpose: Radiomics enables powerful insights into tumor biology through non-invasive imaging, excelling in diagnostic and prognostic predictions. However, due to a lack of mechanistic connections, que...

Biomechanically Guided Deep Learning for Deformable Multimodality Liver Registration Framework

Authors: Yunfei Dong, Dongyang Guo, Zhenyu Yang, Fang-Fang Yin, Zeyu Zhang

Affiliation: Duke University, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose:
To develop a Biomechanically Guided Deep Learning Registration Network (BG-DRNet) that improves both accuracy and physiological plausibility in liver image registration. While cone-beam CT...

Brain Tumor Segmentation from Multi-Parametric MRI with Integrated Evidential Uncertainty Estimation

Authors: Sahaja Acharya, Matthew Ladra, Junghoon Lee, Lina Mekki

Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Department of Biomedical Engineering, Johns Hopkins University

Abstract Preview: Purpose: Multi-parametric MRI (mpMRI) is widely used for deep learning (DL)-based automatic segmentation of brain tumors. While multi-contrast images concatenated as channels are typically input to ne...

Commissioning a Fixed Beamline Ultra-High Dose Rate Proton Therapy System

Authors: Ahmet S. Ayan, Austin M. Faught, Eunsin Lee, Julia Pakela

Affiliation: Department of Radiation Oncology, The Ohio State University Wexner Medical Center, Ohio State University, The Ohio State University

Abstract Preview: Purpose: To present commissioning of a fixed beamline ultra-high dose rate proton pencil beam scanning system, the world’s first multi-room Varian ProBeam360⁰
Methods: The fixed beamline pencil bea...

Compact Representation of External Beam Photon Phase Space Data Via Implicit Neural Representation Learning

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 ...

Cross Characterization of a 3D Gamma Camera and Ring Brachytherapy Applicator

Authors: Zachary R. Grelewicz, Kevin C. Jones, Yixiang Liao, Andrew Ogilvy, Julius V. Turian

Affiliation: Rush University Medical Center

Abstract Preview: Purpose: To demonstrate the accuracy and QA application of a time-resolved 3D gamma camera, we measured the path traversed by the Ir-192 High-Dose-Rate (HDR) brachytherapy source passing through a rin...

Cycle-Consistent Multi-Task Automated Segmentation and Synthetic CT Generation Model for Adaptive Proton Therapy

Authors: Derek Tang, Susu Yan

Affiliation: Massachusetts General Hospital

Abstract Preview: Purpose: To evaluate the performance of a multi-task automated-segmentation and synthetic CT generation model (sCT) and investigate its application in an adaptive proton therapy workflow.
Methods: ...

Deep Learning Based Automatic Cerebrovascular Segmentation in Multi-Center TOF-MRA Datasets

Authors: Gayoung Kim, Junghoon Lee

Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University

Abstract Preview: Purpose: 3D time-of-flight magnetic resonance angiography (TOF-MRA) is widely used for visualizing cerebrovascular structures. Accurate segmentation of cerebrovascular structures is critical for relia...

Deep Learning-Based Auto-Segmentation in Cervical High-Dose-Rate Brachytherapy with Clinical Considerations

Authors: Benjamin Haibe-Kains, Ruiyan Ni, Alexandra Rink

Affiliation: Department of Medical Biophysics, University of Toronto, University Health Network

Abstract Preview: Purpose: Accurate auto-segmentation for targets and organs-at-risk (OARs) using deep learning reduces the delineating time in radiotherapy. In high-dose-rate brachytherapy, specific clinical criteria ...

Demystifying Magnetic Resonance Imaging: Targeted Educational Initiatives for Medical Physicists in TĂŒrkiye and Preclinical Medical Students in the United States

Authors: Samuel A. Einstein, Jesutofunmi Fajemisin, Evren O. Göksel, Görkem O. GĂŒngör, Marthony Robins, Travis C. Salzillo, Charles R. Thomas, Turgay Toksay, Joseph Weygand, Yue Yan

Affiliation: Acibadem MAA University, Department of Radiation Oncology and Applied Science, Dartmouth Health, Dartmouth College, Moffitt Cancer Center, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Penn State College of Medicine, Bursa Ali Osman Sönmez Oncology Hospital

Abstract Preview: Purpose: Magnetic resonance imaging (MRI) is an indispensable clinical tool, offering unparalleled soft tissue contrast critical for diagnosing and managing a wide range of conditions. However, its co...

Denoising Diffusion-Weighted Images of Rectal Cancer Acquired on a 0.35 T Magnetic Resonance Imaging-Guided Linear Accelerator Using Singular Value Decomposition

Authors: Jacqueline M. Andreozzi, Tess Armstrong, Shiva Bhandari, John M Bryant, Jessica M Frakes, David J. Gladstone, Sarah E Hoffe, Kujtim Latifi, Arash Naghavi, Steven Nichols, Ibrahim M. Oraiqat, Russell Palm, Gage H. Redler, Stephen A Rosenberg, Joseph Weygand

Affiliation: Department of Radiation Oncology and Applied Science, Dartmouth Health, Thayer School of Engineering, Dartmouth College, Department of Radiation Oncology, Ohio State University, Moffitt Cancer Center, Department of Radiation Oncology, Moffitt Cancer Center

Abstract Preview: Purpose: The MR-linac provides unique opportunities for integrating advanced imaging into radiotherapy workflows, but the lower sensitivity of systems like the 0.35T model can pose challenges for diff...

Developing a Comprehensive Multi-Modal Framework for Population-Scale Liver Volumetry: Insights and Predictive Models

Authors: Mustafa Bashir, Diana Kadi, Kyle J. Lafata, Jacob A. Macdonald, Mark Martin, Yuqi Wang, Marilyn Yamamoto

Affiliation: Duke University, Department of Radiation Oncology, Duke University, Department of Electrical and Computer Engineering, Duke University, Department of Radiology, Duke Unversity

Abstract Preview: Purpose: To develop a high-throughput, automated-data-interrogation pipeline for integrating imaging and clinical information to identify key determinants of liver volume (LV), enabling population-sca...

Development and Validation of Novel Two-Stage Vascular Segmentation Model for Interventional Angiography

Authors: Abid Khan, Chad Klochko, Michael J Kovalchick, Hyeok Jun Lee, Hani Nasr, Krishnan Shyamkumar, Kundan S Thind

Affiliation: Henry Ford Radiology, Wayne State University, Henry Ford Health, HFHS

Abstract Preview: Purpose: Automated vascular segmentation in interventional angiography is challenged by contrast kinetics, vessel variations, and 2D projections, limiting the effectiveness of single-model approaches....

Development of a Beam Profile Variation Observation Model for Intensity Modulated Radiotherapy

Authors: Kaile Li

Affiliation: Alexander T. Augusta Miliary Medical Center

Abstract Preview: Purpose: To develop a experimental method in observation of beam profile transfer model in intensity modulated radiotherapy (IMRT).
Methods: A picket fence plan was generated in Eclipse treatment p...

Development of a Deep Learning Model for Accurate Brain Dose Prediction in Multi-Target Stereotactic Radiosurgery Plan Evaluation

Authors: Wenchao Cao, Yingxuan Chen, Haisong Liu, Wenyin Shi, Wentao Wang, Lydia J. Wilson, Zhenghao Xiao

Affiliation: Thomas Jefferson University

Abstract Preview: Purpose: Multi-target stereotactic radiosurgery (SRS) planning poses challenges due to complex geometries, small target volumes, and steep dose gradients. Achieving a balance between target coverage a...

Development of a Knowledge-Based Planning Model for Optimal Trade-Off Guidance in Locally Advanced Non-Small Cell Lung Cancer

Authors: Ming Chao, Hao Guo, Tenzin Kunkyab, Yang Lei, Tian Liu, Kenneth Rosenzweig, Robert Samstein, James Tam, Junyi Xia, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai

Abstract Preview: Purpose:
The aim of the study is to develop a trade-off prediction model to efficiently guide the treatment planning process for patients with stage III non-small cell lung cancer (NSCLC).
Metho...

Development of a Method to Standardize Multi-Institutional Quality Assurance Data through an AI Based Language Model Ontology.

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...

Development of a Method to Standardize Multi-Instiutional Quality Assurance Data through an AI Based Language Model Ontology.

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...

Diffusion-Based PET Image Enhancement in Bgrt

Authors: David J. Carlson, Huixiao Chen, Tianqi Chen, Jun Hou, Chi Liu, Qiong Liu, Henry S. Park, Huidong Xie

Affiliation: Yale University, Department of Therapeutic Radiology, Yale University School of Medicine

Abstract Preview: Purpose:
The RefleXionÂź X1 Biology-guided radiotherapy (BgRT) system consists of dual PET detectors, a 6MV linear accelerator (linac), a 64-leaf collimator, an MVD detector, and a CT scanner mounte...

Dosimetric Assessment of Simultaneous Multi-Energy and Fluence Optimization for IMRT and VMAT

Authors: Aliasghar Rohani, Rui Zhang

Affiliation: Louisiana State University, Baton Rouge, Louisiana, Department of Radiation Oncology, Mary Bird Perkins Cancer Center

Abstract Preview: Purpose: This study aimed to evaluate the impact of simultaneous optimization of multi-photon beam energy and fluence on IMRT and VMAT treatment planning.
Methods: An Elekta linear accelerator (lin...

Dynamic Modeling of Patients, Modalities and Tasks via Multi-modal Multi-task Mixture of Experts

Authors: Liyue Shen

Affiliation: University of Michigan

Abstract Preview: N/A...

Effects of Enhanced Leaf Modeling on Single-Isocenter Multi-Target Stereotactic Radiosurgery Dose Accuracy

Authors: Thomas R. Mazur, Kevin Renick, Matthew C. Schmidt, Xiaodong Neo Zhao

Affiliation: Washington University in St. Louis School of Medicine, Washington University School of Medicine

Abstract Preview: Purpose: To compare dose calculation accuracy for single-isocenter multi-target stereotactic radiosurgery plans (SIMT-SRS) in Eclipse V18.0 with enhanced leaf model (ELM) using the anisotropic analyti...

Effects of Plan Optimization Techniques on Relative Biological Effective Dose in a Thin Intracranial Target Volume

Authors: Sean P. Boyer, Shae Gans, Mingcheng Gao, Draik B. Hecksel, Mark Pankuch

Affiliation: Northwestern Medicine Proton Center, Northwestern Medicine Chicago Proton Center

Abstract Preview: Purpose: Protons deposit a large portion of their energy at the end of their range in a region known as the Bragg peak, which may increase the relative biological effective (RBE) dose in this region. ...

Efficient Denoising of Low-Statistic Influence Matrices Using a Diffusion Transformer-Based Framework for Adaptive Proton Therapy

Authors: Yuzhen Ding, Hongying Feng, Jason Michael Holmes, Baoxin Li, Wei Liu, Daniel Ma, Lisa McGee, Samir H. Patel, Jean Claude M. Rwigema, Sujay A. Vora

Affiliation: Arizona State University, Department of Radiation Oncology, Mayo Clinic, Mayo Clinic Arizona, Mayo Clinic

Abstract Preview: Purpose:
Intensity-modulated proton therapy (IMPT) is a preferred treatment modality for head and neck (H&N) cancer patients, offering precise tumor targeting while sparing surrounding organs at ri...

Enhanced Pelvic Organ Segmentation Using LLM-Driven Prompts for Prostate Cancer Low-Dose-Rate Brachytherapy

Authors: Yang Lei, Tian Liu, Ren-Dih Sheu, Meysam Tavakoli, Jing Wang, Kaida Yang, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai, Department of Radiation Oncology, Emory University

Abstract Preview: Purpose:
The study aimed to improve target and organ at risk (OAR) segmentation in low-dose-rate brachytherapy (LDR-BT) for prostate cancer treatment, by integrating clinical guidelines into deep l...

Enhanced Prognostic Modeling for Clear Cell Renal Cell Carcinoma Via Multi-Omics Model and Computational Pathology Foundation Model Integration

Authors: James Brugarolas, Meixu Chen, Raquibul Hannan, Payal Kapur, Jing Wang, Kai Wang

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, University of Maryland Medical Center

Abstract Preview: Purpose: Accurate prognosis of clear cell renal cell carcinoma (ccRCC) is essential for guiding personalized treatment planning. In this study, we present a multi-modal ensemble model (MMEM) that inte...

Evaluating the Capabilities of Hypersight CBCT for Advanced Dual-Energy CBCT Imaging in Online Adaptive Radiotherapy

Authors: Yi-Fang Wang, Yading Yuan

Affiliation: Columbia University Irving Medical Center, Department of Radiation Oncology, Columbia University Irving Medical Center

Abstract Preview: Purpose: HyperSight, the latest CBCT technology from Varian Medical Systems, integrates rapid 6-second data acquisition with advanced iterative reconstruction and upgraded hardware. Previous studies h...

From Noisy Signals to Accurate Maps: Transforming Look-Locker MRI with an Intelligent T₁ Estimation

Authors: Prabhu C. Acharya, Hassan Bagher-Ebadian, Stephen L. Brown, James R. Ewing, Mohammad M. Ghassemi, Benjamin Movsas, Farzan Siddiqui, Kundan S Thind

Affiliation: Michigan State University, Oakland University, Henry Ford Health

Abstract Preview: Purpose: Accurate T1 quantification using T One by Multiple Read Out Pulse (TOMROP) sequences is essential for physiological assessments in dynamic-contrast-enhanced (DCE) MRI and T1 mapping studies. ...

Fully Automated Zero-Shot Organ Segmentation in Male Pelvic MR Images for MR-Guided Radiation Therapy

Authors: Jihun Kim, Jin Sung Kim, Jun Won Kim, Yong Tae Kim, Chanwoong Lee, Jihyn Pyo, Young Hun Yoon

Affiliation: Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine

Abstract Preview: Purpose: Although segmentation foundation models have recently demonstrated promising zero-shot performance on natural images, its clinical application to magnetic resonance (MR) images still requires...

Generalized 2D Cine Multi-Modal MRI-Based Dynamic Volumetric Reconstruction Using Motion-Aligned Implicit Neural Network with Spatial Prior Embedding

Authors: Ming Chao, Karyn A Goodman, Yang Lei, Tian Liu, Jing Wang, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai

Abstract Preview: Purpose: Real-time volumetric MRI is essential for motion management in MRI-guided radiotherapy (MRIgRT), yet acquiring high-quality 3D images remains challenging due to time constraints and motion ar...

Graph Neural Network with Long Short-Term Memory for CT-Based Macrotrabecular-Massive Hepatocellular Carcinoma Diagnosis

Authors: Enhui Chang, Yunfei Dong, Yifei Hao, Chengliang Jin, Shengsheng Lai, Yi Long, Mengni Wu, Yulu Wu, Ruimeng Yang, Zhenyu Yang, Yue Yuan, Lei Zhang, Wanli Zhang, Yaogong Zhang

Affiliation: Duke Kunshan University, Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: Macrotrabecular-Massive Hepatocellular Carcinoma (MTM-HCC) is one type of liver cancer showed minimum image signature for accurate non-invasive diagnosis. This study aims to develop and evalu...

High-Temporal Dynamic CBCT Imaging Via Gaussian Neural Representation

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...

Hydrated Electron Yield Under Electron Irradiation at Ultra-High Dose Rates with Varying Physical Beam Parameters

Authors: Xu Cao, Wesley S. Culberson, Aubrey Parks, Brian W Pogue, Matthew Reed, William Scott Thomas

Affiliation: University of Wisconsin: Madison, Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin - Madison, University of Wisconsin-Madison, University of Wisconsin - Madison, UW-Madison

Abstract Preview: Purpose: Under Ultra-High Dose Rate (UHDR) irradiation, radical-radical interactions are likely enhanced due higher density production in time and space and this alters the cascade of reactions that s...

Image Quality-Based Clinical CT Cohort Selection from Midrc Using a Multi-Institutional Phantom Dataset

Authors: John M. Boone, Andrew M. Hernandez, Paul E. Kinahan, Michael F. McNitt-Gray, Jeffrey H. Siewerdsen, Ali Uneri

Affiliation: University of California, Johns Hopkins Univ, UT MD Anderson Cancer Center, David Geffen School of Medicine at UCLA, University of Washington, UC Davis Health

Abstract Preview: Purpose: Measuring image quality (IQ) in large clinical databases, such as the Medical Imaging and Data Resource Center (MIDRC), is challenging due to the inherent complexity of image content and the ...

Impact analysis of effective dose conversion factors from CT scanner models

Authors: Max Chen, Sean Marquardt, Ben Yang, Kai Yang

Affiliation: Cary Academy, Massachusetts General Hospital, Winchester High School

Abstract Preview: Purpose: To analyze the impact of scanner model variation on the effective dose conversion factor (“k-factor”), which is most commonly used for CT effective dose calculation.

Methods: The stand...

Implementing a Learning-to-Optimize Machine Learning Framework to Accelerate VMAT Treatment Planning Optimization for Prostate Cancer

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...

Improvement of Spine Phantom for MR Imaging of the Spine

Authors: Richard Dortch, Thammathida Ketsiri, Zhiqiang Li, Shiv P. Srivastava

Affiliation: Barrow Neurological Institute, Dignity Health Cancer Institute, St. Joseph's Hospital & Medical Center

Abstract Preview: Purpose: Imaging the spinal cord post-surgery is challenging due to metal surgical implants, which induce signal loss and geometric distortions. Together, this hinders the visualization of the spinal ...

Improving Mammography Diagnosis Accuracy through Global Context and Local Lesion Integration

Authors: Minbin Chen, Xiaoyi Dai, Xiaoyu Duan, Chunhao Wang, Fan Xia, Zhenyu Yang, Fang-Fang Yin, Chulong Zhang, Rihui Zhang

Affiliation: Duke University, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University, The First People's Hospital of Kunshan

Abstract Preview: Purpose: Deep learning (DL)-based mammography diagnosis presents unique challenges, as accurate interpretation requires both global breast condition analysis and local lesion structural information. E...

Improving the Robustness of AI-Based Detection and Segmentation for Brain Metastasis By Optimizing Loss Function and Multi-Dataset Training

Authors: Omar Awad, Alfredo Enrique Echeverria, Issam M. El Naqa, Daniel Allan Hamstra, Yiding Han, Ryan Lafratta, Abdallah Sherif Radwan Mohamed, Piyush Pathak, Zaid Ali Siddiqui, Baozhou Sun, Vincent Ugarte

Affiliation: H. Lee Moffitt Cancer Center, Harris Health, Baylor College of Medicine

Abstract Preview: Purpose:
Accurate detection and segmentation of brain metastases are critical for diagnosis, treatment planning, and follow-up imaging but are challenging due to labor-intensive manual assessments ...

Interpretable Deep Learning Predicts Metastasis-Free Survival (MFS) from Conventional Imaging for Oligometastatic Castration-Sensitive Prostate Cancer (omCSPC) Using Multi-Modality PSMA PET and CT Imaging.

Authors: Yufeng Cao, Luigi Marchionni, William Silva Mendes, Cem Onal, Lei Ren, Amit Sawant, Nicole L Simone, Philip Sutera, Phuoc Tran

Affiliation: University of Maryland School of Medicine, 9Department of Radiation Oncology, Thomas Jefferson University, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, University of Maryland, Baltimore, Baskent University Faculty of Medicine, Department of Radiation Oncology, Department of Radiation Oncology, University of Maryland School of Medicine, Maryland University Baltimore, 8Department of Pathology and Laboratory Medicine, Weill Cornell Medicine

Abstract Preview: Purpose: This study aims to predict 2-yr Metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) patients treated by metastasis-directed therapy (MDT) by devel...

Investigation of the Impact of Dlg Changes on Plan Quality and Patient Specific QA

Authors: Nicole C. Detorie, Steven M. Kirsner, Remy Y. Manigold

Affiliation: Scripps Cancer Center

Abstract Preview: Purpose:
Dosimetric Leaf Gap (DLG) is an important factor in obtaining the proper beam model in the Eclipse treatment planning system. The purpose of this study was to investigate the dosimetric im...

Knowledge-Based Deep Residual U-Net for Synthetic CT Generation Using a Single MR Volume for Frameless Radiosurgery

Authors: Justus Adamson, John Ginn, Yongbok Kim, Ke Lu, Trey Mullikin, Xiwen Shu, Chunhao Wang, Zhenyu Yang, Jingtong Zhao

Affiliation: Duke University, Duke Kunshan University

Abstract Preview: Purpose:
To develop a knowledge-based deep model for synthetic CT (sCT) generation from a single MR volume in frameless radiosurgery (SRS), eliminating the need for CT simulation prior to the SRS d...

LLM-Enhanced Multi-Modal Framework for Predicting Pain Relief of Stereotactic Body Radiotherapy for Spine Metastases Using Clinical Factors and Imaging Reports

Authors: John Byun, Steven D Chang, Mingli Chen, Cynthia Chuang, Xuejun Gu, Melanie Hayden Gephart, Yusuke Hori, Hao Jiang, Mahdieh Kazemimoghadam, Fred Lam, Gordon Li, Lianli Liu, Weiguo Lu, David Park, Erqi Pollom, Elham Rahimy, Deyaaldeen Abu Reesh, Scott Soltys, Gregory Szalkowski, Lei Wang, Qingying Wang, Zi Yang, Xianghua Ye, Kangning Zhang

Affiliation: Department of Radiation Oncology, Stanford University, Department of Neurosurgery, Stanford University, 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: Accurate prediction of pain relief is crucial in determining the clinical effectiveness of Stereotactic body radiotherapy (SBRT) regimen for spine metastases. We propose a deep-learning frame...

Latent Diffusion Model-Driven Semi-Supervised Semantic Segmentation of Cell Nuclei

Authors: Mark Anastasio, Hua Li, Zhuchen Shao

Affiliation: Washington University School of Medicine, University of Illinois Urbana-Champaign

Abstract Preview: Purpose: Automated semantic segmentation of cell nuclei in microscopic images is vital for disease diagnosis and tissue microenvironment analysis. However, obtaining large annotated datasets for train...

Liver Tumor Auto-Contouring Using Recurrent Neural Networks on MRI-Linac for Adaptive Radiation Therapy

Authors: Yan Dai, Jie Deng, Christopher Kabat, Weiguo Lu, Ying Zhang, Hengrui Zhao

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, Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center

Abstract Preview: Purpose:
MRI-guided adaptive radiotherapy (MRgART) using MR-LINAC systems offers significant advantages for liver cancer, enabling superior tumor delineation and online plan adaptation. However, ma...

Lu-PSMA-Dose, a French Retrospective Multicentric Dosimetry Study of 177Lu-PSMA Treatments for Metastatic Castration-Resistant Prostate Cancer

Authors: NadÚge Anizan, Jean-Noël Badel, Thomas Baudier, David Broggio, Désirée Deandreis, Ludovic Ferrer, Didier Franck, Camilo Garcia, Jean Gasteuil, Anne-Laure Giraudet, Olivier Humbert, Laetitia Imbert, Malick Koulibaly, Stéphanie Lamart, Sébastien Leygnac, Alexandre Pignard, Caroline Rousseau, Guido Rovera, David Sarrut, Paul Schwartz, Nicolas Varmenot

Affiliation: Institut de CancĂ©rologie de l’Ouest, Service de MĂ©decine NuclĂ©aire, Gustave Roussy, Service de Physique MĂ©dicale, Institut BergoniĂ©, Service de Physique MĂ©dicale, CHRU de Nancy Brabois, Service de MĂ©decine NuclĂ©aire, Centre de lutte contre le cancer LĂ©on BĂ©rard, UniversitĂ© de Lyon, Centre Antoine Lacassagne, Service de MĂ©decine NuclĂ©aire, UniversitĂ© Nice CĂŽte d’Azur, Centre Antoine Lacassagne, Service de MĂ©decine NuclĂ©aire, Gustave Roussy, Service de MĂ©decine NuclĂ©aire, Institut de CancĂ©rologie de l’Ouest, Service de Physique MĂ©dicale, Service de MĂ©decine NuclĂ©aire, AutoritĂ© de SĂ»retĂ© NuclĂ©aire et de Radioprotection (ASNR), PSE-SANTE/SDOS/LEDI, AutoritĂ© de SĂ»retĂ© NuclĂ©aire et de Radioprotection (ASNR), PSE-SANTE/SDOS, CREATIS CNRS UMR 5220; INSERM U 1044; UniversitĂ© de Lyon; INSA‑Lyon, CREATIS CNRS UMR 5220; INSERM U 1044; UniversitĂ© de Lyon; INSA‑Lyon; Centre de lutte contre le cancer LĂ©on BĂ©rard, Institut BergoniĂ©, Service de MĂ©decine NuclĂ©aire

Abstract Preview: Purpose: The main objective of the Lu-PSMA-Dose study, sponsored by ASNR, is to estimate retrospectively the individual absorbed dose delivered to patients treated with 177Lu-PSMA for metastatic castr...

Mitigating Discrepancies in Radiology Reports: A Robust LLM Approach for Generating Consistent Impressions

Authors: Junwen Liu, Mengzhen Wang, Ning Wen, Jifeng Xiao, Fuhua Yan, Yanzhao Yang, Xuekun Zhang, Zheyu Zhang

Affiliation: Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Shanghai Jiaotong University, The SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Shanghai Jiaotong University Schoo of Medicine

Abstract Preview: Purpose:This study aims to develop and evaluate a large language model (LLM) fine-tuned to generate consistent and accurate impressions from imaging findings. Additionally, the study investigates the ...

Modality-Agnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentation

Authors: Ming Dong, Carri K. Glide-Hurst, Qisheng He, Anudeep Kumar, Alex Singleton Kuo, Joshua Pan, Chase Ruff, Nicholas R. Summerfield

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: Recent evidence highlights the importance of incorporating cardiac substructures (CS) into treatment planning for thoracic cancers, however current segmentation methods are limited to a singl...

Modified Dehazenet for Scatter Correction in Triggered Imaging: Enhancing Visibility and Alignment Precision for Radiation Therapy

Authors: Hyosung Cho, Dae Yup Han, Duhee Jeon, Jiwon Park, Hyesun Yang

Affiliation: Department of Therapeutic Radiology, Yale University School of Medicine, Yonsei University

Abstract Preview: Purpose: Scatter in X-ray imaging degrades image quality, hindering the visibility of critical anatomical features and complicating patient alignment in radiation therapy. This study aims to improve s...

Muilt-Instance Learning Model with 2D and 3D Features Representation and Transformer-Based Prediction for FDG PET Tumor Chemoradiation Response of La-NSCLC

Authors: Stephen R. Bowen, Chunyan Duan, Daniel S. Hippe, Qiantuo Liu, Jiajie Wang, Shouyi Wang, Faisal Yaseen, Han Zhou

Affiliation: Tongji University, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Fred Hutchinson Cancer Center, University of Texas at Arlington

Abstract Preview: Purpose: Predicting the effects of the spatial-temporal tumor response to chemoradiation can assist in adjusting radiation dose and support clinical decision-making in radiotherapy. A multi-instance l...

Multi-Center Evaluation of an AI Beam Angle Prediction Model for Liver Treatments Using Pencil Beam Scanning Proton Therapy

Authors: Christopher Ackerman, Chang Chang, Yan-Cheng Huang, Robert Kaderka, Che Lin, Hsin-Chih Lo, Iain MacEwan, Yi-Chin Tu, James Urbanic

Affiliation: University of California San DIego, Taiwan AI Labs, National Taiwan University, California Protons Cancer Therapy Center, University of Miami, Sylvester Comprehensive Cancer Center

Abstract Preview: Purpose: To investigate the performance of an existing AI beam angle prediction model on external patient datasets for liver proton treatments. The AI model was trained on datasets exclusively from on...

Multi-Criteria Optimization in Medical Physics Resource Allocation: Design of an Efficient and Equitable Scheduling System

Authors: Dalton Griner, Kathryn L. Kolsky, Joseph John Lucido, Andrew J. Veres

Affiliation: Mayo Clinic

Abstract Preview: Purpose: This project aimed to automate a complex and time-consuming employee scheduling process. By replacing the traditional manual method with a multi-criteria optimization-based system (MCO), the ...

Multi-Institutional Analysis of CT Dose Index Variability and Radiomics Features

Authors: Caroline Chung, Michael Knopp, Stephen F. Kry, Hunter S. Mehrens, John Rong

Affiliation: The University of Texas MD Anderson Cancer Center, UT MD Anderson Cancer Center, University of Cincinnati

Abstract Preview: Purpose: To evaluate the variability of CT dose index (CTDIvol) and radiomics features across a large cohort of radiotherapy simulation CT scans from multiple institutions.
Methods: Three IROC phan...

Multi-Mechanism CNN and Long Short-Term Memory Fusion Model for Improved CT-Based Thyroid Cancer Diagnosis

Authors: Yunfei Dong, Dongyang Guo, Jiongli Pan, Tao Peng, Caiyin Tang, Zhenyu Yang, Fang-Fang Yin, Lei Zhang, Tianyi Zhang, Yaogong Zhang

Affiliation: Duke Kunshan University, Department of Radiology, Taizhou People’s Hospital Affiliated to Nanjing Medical University, School of Future Science and Engineering, Soochow University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: This study aims to improve the accuracy of CT-based diagnosis of thyroid cancer by developing a hybrid model that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (...

Multi-Modality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma

Authors: Tyler J Bradshaw, Sharon M Castellino, Steve Y Cho, David Hodgson, Bradford S Hoppe, Kara M Kelly, Andrea Lo, Sarah Milgrom, Xin Tie

Affiliation: Department of Radiation Oncology, University of Toronto, Department of Radiology, University of Wisconsin, University of Colorado Anschutz, Department of Medical Physics, University of Wisconsin, Department of Radiation Oncology, Mayo Clinic, Department of Radiation Oncology, BC Cancer, Vancouver Center, Department of Radiology, University of Wisconsin - Madison, Roswell Park Comprehensive Cancer Center, Emory University School of Medicine

Abstract Preview: Purpose: Clinical target volume (CTV) delineation for involved-site radiation therapy (ISRT) in Hodgkin lymphoma (HL) is time-consuming due to the need to analyze multi-time-point PET/CT scans co-regi...

Multi-Omics-Based Prognostic Prediction for Locally Advanced Hypopharyngeal Cancer Treated with Postoperative Chemoradiotherapy: A Dual-Center Study

Authors: Sixue Dong, Chaosu Hu, Weigang Hu, Xiaomin Ou, Jiazhou Wang, Zhen Zhang

Affiliation: Fudan University Shanghai Cancer Center

Abstract Preview: Purpose:
This study aimed to predict the PFS of the patients who were diagnosed with hypopharyngeal cancer and received postoperative chemoradiotherapy by using multi-omics which integrating clinic...

Multi-Organ Segmentation of Pelvic Cone-Beam Computed Tomography (CBCT) with Transformer Models to Enhance Adaptive Radiotherapy for Prostate Cancer

Authors: Ming Chao, Thomas Chum, Tenzin Kunkyab, Yang Lei, Tian Liu, Richard G Stock, Hasan Wazir, Junyi Xia, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai

Abstract Preview: Purpose:
This study aims to develop effective strategies for multi-organ segmentation of pelvic cone-beam computed tomography (CBCT) images based on transformer models to facilitate adaptive radiat...

Multi-Path Deep Learning Model for Predicting Post-Radiotherapy Functional Liver Imaging in Patients with Hepatocellular Carcinoma

Authors: Smith Apisarnthanarax, Stephen R. Bowen, Sunan Cui, Jie Fu, Clemens Grassberger, Yulun He, Yejin Kim, Matthew J. Nyflot, Sharon Pai

Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, Department of Radiation Oncology, University of Washington, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Department of Physics, University of Washington, University of Washington and Fred Hutchinson Cancer Center

Abstract Preview: Purpose: 99mTc-sulfur colloid SPECT imaging enables quantitative assessment of voxel-wise liver function in patients with hepatocellular carcinoma (HCC). Accurately predicting post-radiotherapy (RT) l...

Multi-Region Multiomic Features Improve Random Forest Toxicity Modeling of Radiation Pneumonitis

Authors: Laurence Edward Court, Alexandra Olivia Leone, Zhongxing Liao, Saurabh Shashikumar Nair, Joshua S. Niedzielski, Ramon Maurilio Salazar, Ting Xu

Affiliation: The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose: Radiation Pneumonitis (RP) predictive models often rely on clinical and DVH parameters, but multiomic features from CT imaging and 3D dose distributions from various regions could provide add...

Multi-Scale, Multi-Task Framework with Jacobian Descent for Multi-Plan Dose Prediction in Sequential Boost Radiotherapy

Authors: Steve B. Jiang, Mu-Han Lin, Yu-Chen Lin, Austen Matthew Maniscalco, Dan Nguyen, David Sher, Xinran Zhong

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center, UT Dallas

Abstract Preview: Purpose:
Sequential boost radiotherapy (RT) poses a challenge in allocating dose across multiple plans while protecting organs at risk (OARs). Clinicians must decide whether OAR sparing should occu...

Multi-Variat, Multi-Model, and Multi-Patient: From Pure Feasibility to Generalizability in Machine Learning Outcome Prediction Model-Based Treatment Plan Optimization

Authors: Martin Frank, Oliver JĂ€kel, Niklas Wahl

Affiliation: Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Karlsruhe Institute of Technology (KIT)

Abstract Preview: Purpose: Machine learning (ML) models on normal tissue complication and tumor control probability ((N)TCP) exploiting e.g. dosiomic and radiomic features are playing an increasingly important role in ...

Multi-Vendor Validation of a Deep Learning-Based Synthetic CT Generation Model for MR-Only Radiotherapy Planning in the Pelvis

Authors: Gregory Bolard, Rabten Datsang, Sarah Ghandour, Timo Kiljunen, Pauliina Paavilainen, Sami Suilamo, Katlin Tiigi

Affiliation: Turku University Hospital, Virginia Commonwealth University, MVision AI, North Estonia Medical Centre, Docrates Cancer Center, Hopital Riviera-Chablais

Abstract Preview: Purpose: To verify the performance of a vendor-neutral deep learning model for synthetic CT generation from T2-weighted and balanced steady-state MR sequences to support both MR-only simulation and MR...

Multi-modal AI for Longitudinal Response Assessment

Authors: Harini Veeraraghavan

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center

Abstract Preview: N/A...

Multimodal Attention Fusion Model Leveraging Structured and Unstructured EHR Data for Hospital Readmission Prediction in Head and Neck Cancer

Authors: Shreyas Anil, Jason Chan, Arushi Gulati, Yannet Interian, Hui Lin, Benedict Neo, Andrea Park, Bhumika Srinivas

Affiliation: Department of Otolaryngology Head and Neck Surgery, University of California San Francisco, Department of Data Science, University of San Francisco, Department of Radiation Oncology, University of California San Francisco

Abstract Preview: Purpose: Hospital readmission prediction models often rely on structured Electronic Health Record (EHR) data, overlooking critical insights from unstructured clinical notes. This study presents a mult...

Multimodal Framework for Predicting Radiation-Induced Severe Acute Esophagitis in Esophageal Cancer

Authors: Yeona Cho, Chloe Min Seo Choi, Joseph O. Deasy, Jue Jiang, Jihun Kim, Jin Sung Kim, Nikhil Mankuzhy, Aneesh Rangnekar, Andreas Rimner, Maria Thor, Harini Veeraraghavan, Abraham Wu

Affiliation: University of Freibrug, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Memorial Sloan Kettering Cancer Center, Yonsei University

Abstract Preview: Purpose: We hypothesized that combining clinical, imaging, and radiotherapy dose-distribution features could increase predictive model accuracy in radiation-induced severe acute esophagitis (SAE) in e...

NA-Unetr: A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery

Authors: Hassan Bagher-Ebadian, Ahmed I Ghanem, Joshua P. Kim, Chengyin Li, Rafi Ibn Sultan, Kundan S Thind, Dongxiao Zhu

Affiliation: Wayne State University, Department of Radiation Oncology, Henry Ford Health-Cancer, Detroit, MI and Alexandria Department of Clinical Oncology, Faculty of Medicine, Alexandria University, Henry Ford Health

Abstract Preview: Purpose: Accurate segmentation of the Left Anterior Descending (LAD) artery in free-breathing 3D treatment planning CT is crucial for radiotherapy but remains challenging due to its small size, comple...

Noise Sensitivity of Benchmark Whole-Body CT Segmentation Models: Totalsegmentator and Vista3D Performance on an Independent Dataset

Authors: Samuel L. Brady, Shruti Hegde, Alexander Knapp, Usman Mahmood, Joseph G. Meier, Elanchezhian Somasundaram, Zachary Taylor

Affiliation: Cincinnati Children's Hospital Medical Ctr, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Cincinnati Children's Hospital Medical Center, Cincinnati Childrens Hospital Med Ctr

Abstract Preview: Purpose:
To assess how two benchmark multi-organ CT segmentation models respond to varying image noise levels.
Methods:
This study utilized the pediatric CT dataset from The Cancer Imaging Ar...

Nomogram Based on Interpretable Multiregional Radiomics of Cone-Beam Breast CT and Clinicopathologic Features for Predicting FISH Status in HER2 2+ Breast Cancer to Differentiate HER2-Low from -Positive: A Multi-Center Study

Authors: Keyi Bian, Marco Caballo, Wenxiu Guo, Haijie Li, Jiao Li, Aidi Liu, Yue Ma, Ioannis Sechopoulos, Yafei Wang, Yaopan Wu, Zhaoxiang Ye, Yuwei Zhang, Yueqiang Zhu, Daan van den Oever

Affiliation: Radboud University Medical Center, Tianjin Medical University Cancer Institute & Hospital, Sun Yat-Sen University Cancer Center

Abstract Preview: Purpose: To develop and validate a nomogram integrating intra- and peritumoral radiomics of contrast-enhanced cone-beam breast CT (CE-CBBCT) and clinicopathologic features for predicting fluorescence ...

Non-Contact Electroacoustic Imaging Monitoring Electroporation Therapy

Authors: Zhongping Chen, Yuchen Song, Leshan Sun, Liangzhong Xiang, Yifei Xu

Affiliation: University of California, Irvine, University of California Irvine

Abstract Preview: Purpose: Electroacoustic tomography (EAT) monitors the distribution of electrical energy in tissues by detecting acoustics signals induced by electrical pulses during irreversible electroporation (IRE...

Patient Education through Virtual Reality: Project Development

Authors: Alison Amos, Alan H. Baydush, Shawna Buchanan, Nicole Carone, Sarah Cummings, Corey Henderson, Christy Hickerson, Gretchen Kessler, Sydney Lash, Kelli Reardon, Michelle Rokni, Sarah B. Wisnoskie, Ashlyn Zebrowski

Affiliation: UNC-Chapel Hill, UNC Chapel HIll, Novant Health

Abstract Preview: Purpose: As Medical Physics 3.0 (MP3.0) progresses, many clinics are integrating consultations with physicists to educate cancer patients, reduce stress, and enhance the patient experience. However, d...

Personalized Radiotherapy: A Novel Approach to Multi-Criteria Optimization with Patient-Specific Risk Integration

Authors: Ali Ajdari, Thomas R. Bortfeld, Zhongxing Liao, Mara Schubert, Katrin Teichert

Affiliation: The University of Texas MD Anderson Cancer Center, Department Of Radiation Oncology, Massachusetts General Hospital (MGH), Massachusetts General Hospital & Harvard Medical School, Fraunhofer ITWM

Abstract Preview: Purpose: Radiotherapy (RT) treatment planning often involves solving a multi-criteria optimization (MCO) problem. Conventionally, MCO considers a set of generic (population-wide) dosimetric criteria, ...

Plastic Scintillating Detector Assisted Spot Mapping for Patient Specific Quality Assurance in Proton Beam Therapy

Authors: Saad Bin Saeed Ahmed, Marcos Feijoo, Wazir Muhammad, Charles Shang, Naseem Ud Din

Affiliation: South Florida Proton Therapy Institute, Miami Cancer Institute, Blue Physics LLC, Florida Atlantic University

Abstract Preview: Purpose: This study introduces a novel approach for patient-specific quality assurance in proton beam therapy, utilizing new plastic scintillator detectors with high temporal resolutions to create det...

Precise Measurement of Leaf Open Time in Tomotherapy Using Advanced Plastic Scintillators

Authors: Marcos Feijoo, Mamoru Ichiki, Tatsuya Maeda, Tatsuhiro Makino, Tomonori Megumi, Tadashi Nakabayashi, Yukino Nakata, Shuichi Ozawa, Daishi Takayama

Affiliation: Hiroshima High-Precision Radiotherapy Cancer Center, Blue Physics LLC, Imamura General Hospital, Accuray Inc.

Abstract Preview: Purpose: This study aims to accurately measure the leaf open time (LOT) of the multi-leaf collimator (MLC) in tomotherapy using an advanced plastic scintillator detector (PSD) with high time resolutio...

Precision Radiotherapy Dose Prediction Using Foundation Model-Augmented Learning

Authors: Hilary P Bagshaw, Mark K Buyyounouski, Xianjin Dai, PhD, Praveenbalaji Rajendran, Lei Xing, Yong Yang

Affiliation: Department of Radiation Oncology, Stanford University, Massachusetts General Hospital, Harvard Medical School

Abstract Preview: Purpose: Artificial intelligence (AI)-driven methods have transformed dose prediction, streamlining the automation of radiotherapy treatment planning. However, traditional approaches depend exclusivel...

Predicting and Monitoring Response to Head and Neck Cancer Radiotherapy Using Multi-Modality Imaging and Radiobiological Digital Twin Simulations

Authors: Eric Aliotta, Michalis Aristophanous, Joseph O. Deasy, Bill Diplas, Milan Grkovski, James Han, Vaios Hatzoglou, Jeho Jeong, Nancy Y Lee, Ramesh Paudyal, Nadeem Riaz, Heiko Schoder, Amita Shukla-Dave

Affiliation: Department of Radiology, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: To forecast radiotherapy treatment response for head and neck cancer (HNC) using multimodality imaging and personalized radiobiological modeling.
Methods: Multi-modality imaging data from ...

Real-Time 3D Dose Verification for MR-Guided Online Adaptive Radiotherapy (ART) Via Geometry-Encoded Deep Learning Framework

Authors: Steve B. Jiang, Dan Nguyen, Chenyang Shen, Fan-Chi F. Su, Jiacheng Xie, Shunyu Yan, Daniel Yang, Ying 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) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, The University of Texas at Dallas

Abstract Preview: Purpose: Fast dose verification is essential for the safety and efficiency of MR-guided adaptive radiotherapy (ART) as patients anxiously waiting on the treatment couch. Conventional tools often requi...

Real-Time Automatic Treatment Planning System (RT-AutoTPS) for Volumetric Modulated Arc Radiotherapy (VMAT)

Authors: Steve B. Jiang, Austen Matthew Maniscalco, Dan Nguyen, Chenyang Shen, Jiacheng Xie, Shunyu Yan, Ying 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) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, The University of Texas at Dallas

Abstract Preview: Purpose: Although treatment planning systems (TPSs) can handle dose calculation and plan optimization automatically, planning for radiotherapy still requires extensive efforts and expertise from a mul...

Reducing Variable Rbe in Oars for HN, Breast, and Prostate Proton Therapy Using a Scripted Spot-Delete Technique

Authors: Laura Buchanan, Samantha G. Hedrick, Stephen L. Mahan, Isabella Pfeiffer, Chester R. Ramsey

Affiliation: Thompson Proton Center

Abstract Preview: Purpose: Linear energy transfer (LET) and variable relative biological effectiveness (RBE) evaluation are important steps in the future of proton therapy, moving beyond RBE=1.1 for all treatment plann...

Region-Specific Structure-Function Coupling Alterations in Parkinson’s Disease: Insights from Multi-Modal MRI

Authors: Yifei Hao, Ting Huang, Wenxuan Li, Xiang Li, Manju Liu, Rong Liu, Tao Peng, Yulu Wu, Fang-Fang Yin, Lei Zhang, Yaogong Zhang, Jiangtao Zhu

Affiliation: Duke University, Department of Radiology, The Second Affiliated Hospital of Soochow University, School of Future Science and Engineering, Soochow University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: This study investigates the alterations in structure-function coupling (SC-FC) networks in Parkinson’s disease (PD) patients, focusing on region-specific disruptions and compensatory mechanis...

Reinforcement Learning Based Machine Parameter Optimization for Two-Arc Prostate VMAT Planning

Authors: William T. Hrinivich, Junghoon Lee, Lina Mekki

Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Department of Biomedical Engineering, Johns Hopkins University, Johns Hopkins University

Abstract Preview: Purpose: Volumetric modulated arc therapy (VMAT) planning is a computationally expensive process. In this work, we propose a reinforcement learning (RL) framework to automatically optimize dose rate a...

Research on Multi-Organ Segmentation Based on Cross-Domain Transfer Learning

Authors: Jiali Gong, Yi Guo, Chi Han, Wanli Huo, Hongdong Liu, Zhao Peng, Yaping Qi, Zhaojuan Zhang

Affiliation: Department of Radiotherapy, cancer center, The First Affiliated Hospital of Fujian Medical University, Department of Oncology, Xiangya Hospital, Central South University, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, China Jiliang University, Division of lonizing Radiation Metrology, National Institute of Metrology, Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, the Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University

Abstract Preview: Purpose: To address overfitting from limited training data in multi-organ segmentation, an efficient transfer learning framework is proposed. It reduces reliance on training samples, enabling a single...

Robust and Radiobiologically Accurate Dose Summation Strategy for Multiple Courses of Stereotactic Re-Irradiation of Metastatic Brain Cancer Patients with Previous WBRT

Authors: Garrett Russell Hamilton, Joshua Misa, Damodar Pokhrel, William St. Clair

Affiliation: University of Kentucky, Department of Radiation Medicine, University of Kentucky, Radiation Medicine

Abstract Preview: Purpose: Recurrent brain metastasis is very common, and patients who previously received whole-brain radiotherapy (WBRT) may receive an additional course(s) of stereotactic radiosurgery (SRS) to selec...

SPECT/CT Multimodal Segmentation of Bone Marrow for Theranostic Dosimetry

Authors: Tommaso Frigerio, Joshua Genender, John M. Hoffman, Catherine (Caffi) Meyer

Affiliation: UCLA, David Geffen School of Medicine at UCLA

Abstract Preview: Purpose: Accurate bone marrow segmentation is required for bone marrow dosimetry to monitor for dangers in PSMA-Lu177 radioligand therapy. We introduce a hybrid (AI/semantic knowledge) segmentation pi...

Segmentation Regularized Registration Training Improves Multi-Domain Generalization of Deformable Image Registration for MR-Guided Prostate Radiotherapy

Authors: Lando S. Bosma, Victoria Brennan, Nicolas Cote, ChengCheng Gui, Nima Hassan Rezaeian, Jue Jiang, Sudharsan Madhavan, Josiah Simeth, Neelam Tyagi, Harini Veeraraghavan, Michael J Zelefsky

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, NYU Langone Health, University Medical Center Utrecht, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Deep learning-based deformable image registration (DIR) models often lack robustness when applied to datasets with differing imaging characteristics. We aimed to (1) improve registration netw...

Small but Mighty: A Lightweight and Computationally Efficient Model for Deformable Image Registration

Authors: Hengjie Liu, Dan Ruan, Ke Sheng, DI Xu

Affiliation: Physics and Biology in Medicine, University of California, Los Angeles, Department of Radiation Oncology, University of California, San Francisco, Department of Radiation Oncology, University of California at San Francisco, Department of Radiation Oncology, University of California, Los Angeles

Abstract Preview: Purpose:
State-of-the-art deep learning-based deformable image registration often uses large, complex models directly adapted from computer vision tasks but achieves only comparable performance to ...

Spherical Slicing and Convolutions for Accurate Glioma Tumor Segmentation Using Multi-Parametric MRI

Authors: Ke Lu, Chunhao Wang, Ruoxu Xia, Zhenyu Yang, Fang-Fang Yin, Chulong Zhang, Lei Zhang, Rihui Zhang, Jingtong Zhao, Haiming Zhu

Affiliation: Duke University, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University, The First People's Hospital of Kunshan

Abstract Preview: Purpose: The human brain’s spherical geometry offers unique opportunities for improving the segmentation of tiny and irregular anatomical structures. We hypothesize that representing the brain in sphe...

Streamlined Stereotactic Radiosurgery (SRS) Commissioning Experience with a 6FFF Beam for an Elekta Versa: A Clinical Overview for Increased Precision.

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...

Text-Conditioned Latent Diffusion Model for Synthesis of Contrast-Enhanced CT from Non-Contrast CT

Authors: Yizheng Chen, Michael Gensheimer, Mingjie Li, Lei Xing

Affiliation: Department of Radiation Oncology, Stanford University

Abstract Preview: Purpose: Automatically translating non-contrast to contrast-enhanced computed tomography (CT) images is critical for improving clinical workflow, reducing heathcare cost, minimizing radiation exposure...

The Development of a Novel Biomechanical Model for Accurate Contour Deformation during Online Adaptative Metastatic Bone Cancer Radiotherapy Planning.

Authors: Jeremy S. Bredfeldt, Benito De Celis Alonso, Braian Adair Maldonado Luna, Kevin M. Moerman, Gerardo Uriel Perez Rojas, René Eduardo Rodríguez-Pérez, Kamal Singhrao

Affiliation: Department of Radiation Oncology, Brigham and Women's Hospital, Harvard Medical School, Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute, Harvard Medical School, Department of Mechanical Engineering, University of Galway, Faculty of Physics and Mathematics, Benemérita Universidad Autónoma de Puebla

Abstract Preview: Purpose: Online adaptive radiotherapy replanning for single-isocenter bone cancer metastasis treatment reduces on-table treatment time and patient discomfort compared to the multi-isocenter standard-o...

The Topas Monte Carlo Framework – Status and Outlook after 15 Years of Development

Authors: Alejandro Bertolet, Jorge Naoki Dominguez Kondo, Bruce A. Faddegon, Thongchai Masilela, Isaac Meyer, Victor V. Onecha, Harald Paganetti, Jose A. Ramos-Mendez, Jan PO Schuemann, Wook-Geun Shin

Affiliation: University of California San Francisco, Massachusetts General Hospital and Harvard Medical School, Massachusetts General Hospital

Abstract Preview: Purpose: To present the results of 15 years of developments of the TOPAS TOol for PArticle Simulation framework, and to highlight recent and new developments.

Methods: Fundamental understanding...

Treatment Planning System Modelling of a New Commercial Rotational Table

Authors: Michael Armstrong, Courtney R. Buckey, Quan Chen, Suzanne J. Chungbin, Mirek Fatyga, Yi Rong, Jun Tan, Xiang Sheng Yan

Affiliation: Mayo Clinic Arizona, Mayo Clinic

Abstract Preview: Purpose: To model a commercial rotational table in treatment planning system (TPS) for accurate dose optimization and calculation.

Methods: A commercial rotational table (CDR EquilibriumÂź) is r...

Uncertainty-Guided Cross-Domain Adaptation for Unsupervised Medical Image Segmentation

Authors: Yunxiang Li, Weiguo Lu, 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:
Curating high-quality, labeled data for medical image segmentation can be challenging and costly, considering the existence of various image domains with differing modalities/protocols. Cr...

Using Open-Source Reasoning Large Language Models for Radiotherapy Structure Name Harmonization

Authors: Claus Belka, Stefanie Corradini, Christopher Kurz, Guillaume Landry, Matteo Maspero, Adrian Thummerer, Erik van der Bijl

Affiliation: Department of Radiation Oncology, LMU University Hospital, LMU Munich, Radboud University Medical Center, UMC Utrecht

Abstract Preview: Purpose: To automatically harmonize non-standardized organ-at-risk (OAR) structure names from multi-lingual, multi-institutional radiotherapy datasets using state-of-the-art open-source reasoning larg...