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Results for "ground truth": 117 found

A Comparison of Non-Adaptive Versus Online Adaptive Radiotherapy for Prostate Cancer Using FLOW-RT-- Fast, AI-Driven but Learning-Enabled, Online Adaptive Workflow for Radiotherapy

Authors: Theodore Higgins Arsenault, Kenneth W. Gregg, Beatriz Guevara, Lauren E Henke, Angela Jia, Rojano Kashani, Kyle O'Carroll, Alex T. Price, Adithya Reddy, Atefeh Rezaei, Daniel E Spratt, Runyon C. Woods

Affiliation: University Hospitals Seidman Cancer Center

Abstract Preview: Purpose: To evaluate the effect of unedited AI-generated contours used for online adaptive radiotherapy (FLOW-ART) on the plan quality of prostate treatments as compared to non-adaptive (non-ART) proc...

A Framework for the Standardization of Radiomics Classes in the Presence of Blur and Noise

Authors: Huay Din, Grace Jianan Gang, Grace Hyun Kim, Michael F. McNitt-Gray, Joseph W. Stayman, Yijie Yuan

Affiliation: Johns Hopkins University, John Hopkins University, University of Pennsylvania, David Geffen School of Medicine at UCLA

Abstract Preview: Purpose:
Radiomics rely on quantitative features to discern underlying biological signatures. However, feature dependence on the imaging systems themselves hampers the creation of reproducible and ...

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 for Automatic Working Angle Prediction during Intracranial Aneurysms Embolization

Authors: Tina Ehtiati, Grace Jianan Gang, Limei Ma, Oleg Shekhtman, Visish M. Srinivasan

Affiliation: Siemens Medical Solutions USA, Inc., University of Pennsylvania

Abstract Preview: Purpose: Saccular aneurysms are the most common type of intracranial aneurysm and are typically treated by endovascular embolization. The procedure requires approximately orthogonal fluoroscopy images...

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-Criteria Optimization Method Based on Reinforcement Learning and Adaptive Boosting in Radiation Therapy

Authors: Liqin HU, Tao He, Jing JIA, Pengcheng LONG, Wei Meng, Yang Yuan

Affiliation: SuperAccuracy Science & Technology Co. Ltd.

Abstract Preview: Purpose: A multi-criteria optimization method based on reinforcement learning and adaptive boosting(RLAB MCO) has been developed to enhance radiotherapy plan quality by offering reasonable and effecti...

A Novel Group-Wise Non-Rigid Iterative Closest Point Shape Registration Algorithm with Temporal Smoothness Regularizations for Computing the Respiratory Motion of the Heart in Respiratory 4DCTs and Avoiding Random Cardiac Motion Artifacts

Authors: Hongyu An, Phillip Cuculich, H Michael Gach, Yao Hao, Trevor McKeown, Clifford Robinson, Yuhao Wang, Deshan Yang

Affiliation: Washington University School of Medicine in St. Louis, Washington University School of Medicine, Duke University, Department of Radiation Oncology, Duke University, Washington University in St. Louis, Department of Radiation Oncology, Washington University School of Medicine in St. Louis

Abstract Preview: Purpose: Cardiorespiratory motion management is crucial in stereotactic arrhythmia radiotherapy to define target margins and minimize cardiac toxicity. While respiratory 4DCT (r4DCT) images can infer ...

A Novel Groupwise Shape Deformable Registration Algorithm to Quantify the Respiratory Motion of the Heart in Respiratory 4DCTs While Accounting for Random Cardiac Motion Artifacts in the Respiratory 4DCTs

Authors: Anna E. Rodrigues, Yuhao Wang, Deshan Yang

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

Abstract Preview: Purpose: Accurate motion margin definition in StereoTactic Arrhythmia Radiotherapy (STAR) requires accurate cardiorespiratory motion assessment. However, respiratory 4DCT (r4DCT) images are affected b...

A Novel Margin-Based Focal Distance Loss for Lesion Segmentation in Medical Imaging

Authors: Weiguo Lu, 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

Abstract Preview: Purpose:
Neural network-based lesion segmentation remains a significant challenge due to the low contrast between lesions and surrounding tissues (high ambiguity) and the variability of lesion shap...

A Real-Time 6 Degrees-of-Freedom Image Guided Radiation Therapy Technology Using Internal-External Motion Correlation Modelling

Authors: Jeremy T. Booth, Nicholas Hardcastle, Freeman Jin, Alicja Kaczynska, Paul J. Keall, Chandrima Sengupta

Affiliation: Northern Sydney Cancer Centre, Royal North Shore Hospital, Physical Sciences, Peter MacCallum Cancer Centre, Image X Institute, Faculty of Medicine and Health, University of Sydney, Image X Institute, Faculty of Medicine and Health, The University of Sydney

Abstract Preview: Purpose: An ESTRO survey reported that 71% of radiotherapy centers across 41 countries want to implement real-time image guided radiation therapy (IGRT) to improve patient outcomes. To address this gl...

A Tool to Quantitatively Assess Dose after Patient Motion

Authors: Asma Amjad, Renae Conlin, Beth A. Erickson, William Hall, Eric S. Paulson, Christina M. Sarosiek

Affiliation: Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose: The adapt-to-shape (ATS) workflow on the MR-Linac involves manual contour edits followed by treatment plan reoptimization on daily pre-beam MRIs. A verification image is acquired after plan o...

A Two-Marker Method for Calculating the Radiological Magnification of the Hip in Standing Pelvic Radiography

Authors: Walaa Abdelfadeel, Reagan Thomas Dugan, Tiffany Kinsey, Cameron Kofler, Zheng Feng Lu, Ingrid S. Reiser, Gregory Stacy, Sara Wallace

Affiliation: University of Chicago Medicine, University of Chicago

Abstract Preview: Purpose: To develop an accurate, vendor-neutral method for estimating geometric magnification at the plane of the hip joint on standing anterior-posterior (AP) radiographs for digital templating of to...

A Virtual 4DCT Generator Based on a Digital Phantom with Joint Cardiac and Respiratory Motions

Authors: Phillip Cuculich, Geoffrey D. Hugo, Xiwen Li, Michael T. Prusator, Clifford Robinson, Pamela Samson, Xue Wu

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

Abstract Preview: Purpose:
For development of novel motion management methods it is useful to have a digital phantom capable of realistic simulation of respiratory 4DCT acquisition of the thorax, including cycle-to-...

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...

A Vqvae-Based Framework with Embedded Kullback-Leibler Divergence for Stochastic and Diverse Dose Prediction

Authors: Weigang Hu

Affiliation: Fudan University Shanghai Cancer Center

Abstract Preview: Purpose: The purpose of this study is to introduce a VQVAE-based framework that addresses the limitations of conventional dose prediction methods, which rely on fixed deep learning models that produce...

AI-Powered Real-Time x-Ray Guided Tracking to Improve Stereotactic Arrythmia Radioablation: Proof of Principle

Authors: Vicky Chin, Mark Gardner, Nicholas Hindley, Paul J. Keall, Adam Mylonas

Affiliation: Image X Institute, Faculty of Medicine and Health, The University of Sydney

Abstract Preview: Purpose: Stereotactic Arrhythmia Radioablation (STAR) is a non-invasive method to treat cardiac arrhythmias by targeting aberrant electrical conduction regions in the heart. Targeting is challenging g...

Advanced Daily Imaging Technology Improves Tracking of Soft Tissue Decomposition for Head and Neck Cancer Patients Receiving Radiotherapy

Authors: Robert K Chin, Klea Hoxha, Erika Jank, Jesus Juarez, Eulanca Yuka Liu, Dylan P. O'Connell, X. Sharon Qi, Ricky R Savjani

Affiliation: Department of Radiation Oncology, University of California, Los Angeles, University of California, Los Angeles, UCLA Radiation Oncology

Abstract Preview: Purpose:
Daily cone beam CT (CBCT) images are acquired prior to each radiotherapy treatment for verifying patient positioning. HyperSight is an advanced imaging solution that replaces standard CBCT...

An Automated Tool for the Categorization of a Clinical Database By Anatomic Region for Big Data Applications

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...

An Image Registration-Based Motion Correction Procedure to Recover Joint Cardiac and Respiratory Motion from Respiratory 4DCT

Authors: Phillip Cuculich, Geoffrey D. Hugo, Xiwen Li, Michael T. Prusator, Clifford Robinson, Pamela Samson, Xue Wu

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

Abstract Preview: Purpose: Stereotactic arrhythmia radiotherapy (STAR) requires compensation for both respiratory and cardiac motions of the heart. Respiratory 4DCT scans implicitly include cardiac motion and cycle-to-...

An Open-Access Toolkit to Generate Realistic CT and Low-Field MR Images Based on an Xcat Phantom

Authors: Debora de Souza Antonio, Romy Guthier, Konrad Pawel Nesteruk, Erno Sajo, William Paul Segars, Gregory C. Sharp, Atchar Sudhyadhom, Hengyong Yu

Affiliation: Massachusetts General Hospital, Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Duke University Medical Center, Brigham and Women’s Hospital and Dana Farber Cancer Institute, Harvard Medical School,, Massachusetts General Hospital and Harvard Medical School, University of Massachusetts Lowell

Abstract Preview: Purpose: To develop an open-access toolkit for rapidly generating simultaneously realistic CT scans and low-field MR images of the abdominal region, based on patient data, while employing an XCAT phan...

Analysis of the Accuracy of Avatar-Based Patient Positioning Technique for Radiation Therapy

Authors: Danna Gurari, Moyed Miften, Sarah Milgrom, Atharva Rajesh Peshkar, Willem Schreuder, David H. Thomas

Affiliation: University of Colorado Boulder, University of Colorado School of Medicine, University of Colorado Anschutz, Thomas Jefferson University

Abstract Preview: Purpose: To evaluate the accuracy of a novel avatar-based patient positioning technique.

Methods: We developed a modified surface-guided radiation therapy (SGRT) technique, 'Avatar-Guided Radia...

Assessing Low Iodine Concentrations in Liver Lesions with Dual Energy CT: Impact of Beam Choices

Authors: Xinhua Li, Vu Nguyen, Yifang (Jimmy) Zhou

Affiliation: Cedars-Sinai Medical Center

Abstract Preview: Purpose: Assessing iodine concentration in liver lesions is essential for evaluating contrast enhancement in multi-phase liver CT and for accurate disease diagnosis. This study aims to evaluate the as...

Assessment of Deep Learning Models for 3D Dose Prediction in Prostate Cancer SIB-IMRT Using MR-Linac

Authors: Hao-Wen Cheng, Jonathan G. Li, Chihray Liu, Wen-Chih Tseng, Guanghua Yan

Affiliation: University of Florida

Abstract Preview: Purpose: This study develops and evaluates deep learning (DL) models for predicting 3D dose distributions in simultaneous integrated boost (SIB) prostate cancer treatment using the Elekta Unity MR-Lin...

Assessment of the Impact of CT Respiratory Motion on PET SUV Quantification through Virtual Imaging

Authors: Ehsan Abadi, Darrin Byrd, Paul E. Kinahan, Katie Marie Olivas, Ehsan Samei

Affiliation: Duke University, Center for Virtual Imaging Trials, Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System, University of Washington

Abstract Preview: Purpose: To evaluate the impact of respiratory motion during CT acquisitions on PET image quantification using an integrated PET-CT simulation pipeline.
Methods: A validated CT simulator (DukeSim) ...

Augmenting Histopathology Lymphocyte Detection with Gpt-4 in-Context Visual Reasoning

Authors: Kyle J. Lafata, Casey Y. Lee, 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:
Traditional deep learning-based cell segmentation models face limitations, such as the need for extensive training data and retraining when encountering new cell types or domains. This stu...

Automated Framework for Predicting Tumour Growth in Vestibular Schwannomas Using Contrast-Enhanced T1-Weighted MRI

Authors: Mehdi Amini, Minerva Becker, Simina Chiriac, Alexandre Cusin, Dimitrios Daskalou, Ghasem Hajianfar, Sophie Neveu, Marcella Pucci, Yazdan Salimi, Pascal Senn, Habib Zaidi

Affiliation: Geneva University Hospital, Division of Radiology, Diagnostic Department, Geneva University Hospitals, Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences, Geneva University Hospitals

Abstract Preview: Purpose: Personalized prediction of vestibular schwannoma (VS) tumour growth is crucial for guiding patient management decisions toward observation versus intervention. This study proposes an automate...

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’ ...

Automatic 4D Lung PET-CT Segmentation Using Hybrid Deep Neural Network

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...

Automatic Specific Absorption Rate (SAR) Prediction for Hyperthermia Treatment Planning (HTP) Using Deep Learning Method

Authors: Yankun Lang, Lei Ren, Dario B. Rodrigues

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

Abstract Preview: Purpose:
HTP of microwave (MW) phased-array systems determine MW antenna settings to maximize energy absorption (SAR in W/kg) in tumor. Conventional HTP algorithms calculate SAR based on electromag...

BEST IN PHYSICS IMAGING: Cross-Contrast Diffusion: A Synergistic Approach for Simultaneous Multi-Contrast MRI Super-Resolution

Authors: Yifei Hao, Wenxuan Li, Xiang Li, Tao Peng, Yulu Wu, Fang-Fang Yin, Yue Yuan, Lei Zhang, Yaogong Zhang

Affiliation: Duke University, School of Future Science and Engineering, Soochow University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: Diffusion-based deep-learning frameworks have been recently used in MRI resolution enhancement, or super-resolution. Multi-contrast MRI share common anatomical structures while holding comple...

BEST IN PHYSICS IMAGING: Population-Based Cardio-Respiratory Motion Model to Simulate 4D CT Angiography and 2D+t Fluoroscopy for Percutaneous Coronary Intervention

Authors: Debarghya China, Junghoon Lee, Ali Uneri

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

Abstract Preview: Purpose: This study aims to develop a population-based cardio-respiratory motion model and apply it to patient-specific 3D CTA to simulate 4D CTA and 2D+t fluoroscopy sequences. The developed motion m...

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...

Backscattering of Compton Cameras: A Monte Carlo Simulation Approach

Authors: Jorge Naoki Dominguez Kondo, Qihui Lyu

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

Abstract Preview: Purpose:
Targeted alpha therapy (TAT) has emerged as a highly potent therapeutic method in oncology, but its development is limited by the lack of imaging methods to quantify its dosimetry. Convent...

Backward Photon Monte Carlo Propagator (BPMCP): Proof of Concept of a Fully Physics-Driven SPECT Reconstruction

Authors: Alejandro Bertolet, Carlos Huesa-Berral, Victor V. Onecha

Affiliation: Massachusetts General Hospital and Harvard Medical School

Abstract Preview: Purpose: Radioembolization for liver tumors using 90Y microspheres uses SPECT as confirmatory dosimetry, although 90Y is a pure ÎČ-emitter, and SPECT-imaging is only available through subsequent bremss...

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...

Brain Vessel Segmentation and Tracking in Longitudinal Glioblastoma MRI Scans

Authors: Evan Calabrese, Edward Robert Criscuolo, Deshan Yang

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

Abstract Preview: Purpose: Glioblastoma (GBM) is the most common and aggressive form of brain cancer. Deformable image registration (DIR) is a powerful tool to compute anatomical changes in longitudinal MRI scans, whic...

CBCT-Based Synthetic CT Imaging for Proton and Photon Dose Monitoring and Adaption in Supine Breast Radiotherapy

Authors: Mark E Artz, Julie Bradley, Hardev Singh Grewal, Perry B. Johnson, Christina Klassen, Raymond Mailhot Vega, Nancy Mendenhall, Jiyeon Park, Emma V. Viviers, Yawei Zhang

Affiliation: UF Health Proton Therapy Institute, University of Florida

Abstract Preview: Purpose: Verification CTs (VFCT) are used in radiotherapy to assess patient dose during treatment. However, they are time-consuming and contribute additional radiation exposure to the patient. This st...

Clinically Tenable Lung Dose Estimates in Y-90 Radioembolization from Truncated Maa-SPECT/CT with Unknown Lung Mass

Authors: Dan Giardina, John Karageorgiou, Chris Malone, Naganathan Mani, Allan Thomas

Affiliation: Washington University School of Medicine, Mallinckrodt Institute of Radiology, Washington University School of Medicine

Abstract Preview: Purpose: Relative to planar imaging, MAA-SPECT/CT offers more reliable lung shunt fraction (LSF) and lung mean dose (LMD) estimates in 90Y radioembolization. But lung truncation in SPECT/CT can limit ...

Comparative Analysis of Nine Deep Learning Architectures for Variable Density Grappa 1H Magnetic Resonance Spectroscopy Imaging (MRSI) Reconstruction

Authors: Kimberly Chan, Anke Henning, Mahrshi Jani, Andrew Wright, Xinyu Zhang

Affiliation: Advanced Imaging Research Center (AIRC), UT Southwestern Medical Center

Abstract Preview: Purpose: To evaluate the performance of multiple deep learning architectures for MRSI reconstruction and determine their effectiveness in maintaining high-resolution metabolite mapping while reducing ...

Comparative Evaluation of Nn-Unet Models for Radiotherapy Dose Prediction Using the Head and Neck Cancer Patients

Authors: Theodore Higgins Arsenault, Beatriz Guevara, Rojano Kashani, Raymond F. Muzic, Gisele Castro Pereira, Alex T. Price

Affiliation: University Hospitals Seidman Cancer Center, Case Western Reserve University Department of Biomedical Engineering

Abstract Preview: Purpose: Accurate dose prediction in radiotherapy is essential for treatment planning. This study evaluates four nnUnet-based models using the OpenKBP head and neck dataset: a baseline model (Model 1)...

Comparing SPECT Dose Reconstruction Algorithm Accuracy As a Function of Imaging Parameters

Authors: Srinivas Cheenu Kappadath, Brian Michael Kelley, Benjamin P. Lopez

Affiliation: UT MD Anderson Cancer Center

Abstract Preview: Purpose:
Imaging systems are subject to errors from finite spatial resolution and voxel size. This work demonstrates their effect on dose algorithm {Monte Carlo (MC), dose-volume kernel (DVK), and ...

Comparison of AI-Based and Ants for Longitudinal Deformable Image Registration in Head and Neck Cancer

Authors: Aditya P. Apte, Joseph O. Deasy, Jue Jiang, Nancy Lee, Sudharsan Madhavan, Nishant Nadkarni, Lopamudra Nayak, Harini Veeraraghavan, Wei Zhao

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

Abstract Preview: Purpose: To track early response to radiotherapy using digital twins, it is crucial to quantify tumor volume and mass changes. Traditional tumor detection methods, particularly in image registration, ...

Contrast-Free Full Intracranial Vessel Geometry Estimation from MRI with Metric Learning-Based Inference

Authors: Zhaoyang Fan, Eric Nguyen, Dan Ruan, Jiayu Xiao

Affiliation: Department of Radiation Oncology, University of California, Los Angeles, Department of Radiology, University of Southern California, University of Southern California

Abstract Preview: Purpose: MR vessel wall imaging (VWI) has been shown to be effective for evaluating intracranial atherosclerosis disease. However, VWI typically also requires an MR angiography (MRA) in the same imagi...

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 Denoising for Template Matching in Real-Time Tumor Tracking Using Kv Scattered X-Ray Imaging

Authors: Weikang Ai, Xiaoyu Hu, Xun Jia, Kai Yang, Yuncheng Zhong

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

Abstract Preview: Purpose: Real-time tumor tracking is critically important for respiratory motion management for lung cancer radiotherapy. A previously proposed application of a photon counting detector involves measu...

Deep Learning-Based Segmentation Using Cine Epid Images for Real-Time Tumor Monitoring

Authors: Fumiaki Komatsu, Shunsuke Moriya, Ryosuke Nakamura, Takeji Sakae, Toshiyuki Terunuma, Tetsuya Tomita

Affiliation: Graduate School of Comprehensive Human Sciences, University of Tsukuba, Institute of Medicine, University of Tsukuba, Proton Medical Research Center, University of Tsukuba, Department of Radiology, University of Tsukuba Hospital

Abstract Preview: Purpose: To develop a deep learning (DL) model capable of accurately tracking lung tumors independent of beam angle variations.
Methods: A thoracic dynamic phantom simulating lung motion in the sup...

Deeptuning: A Deep Learning Dose Prediction Framework for Interactive Plan Tuning

Authors: Mingli Chen, Huan Amanda Liu, Weiguo Lu, Lin Ma

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

Abstract Preview: Purpose: To reduce the back-and-forth in planning process between physicians and dosimetrists resulting from trade-off exploration, we proposed a novel deep learning framework called DeepTuning.
Me...

Development and Validation of a Principal Component Analysis Statistical Shape Pediatric/Adolescent Breast Model for Pre-CT Era Breast Dose Reconstruction in Late Effect Studies of Female Childhood Cancer Survivors

Authors: Gregory T. Armstrong, James E. Bates, Kristy K. Brock, Laurence Edward Court, Matt Ehrhardt, Danielle Friedman, Aashish C. Gupta, Donald Hancock, Rebecca M. Howell, Cindy Im, Tera S Jones, Choonsik Lee, Wendy Leisenring, Taylor Meyers, Lindsay Morton, Chaya Moskowitz, Joe Neglia, Vikki Nolan, Caleb O'Connor, Kevin C. Oeffinger, Constance A. Owens, Arnold C. Paulino, Chelsea C. Pinnix, Sander Roberti, Cecile Ronckers, Susan A. Smith, Kumar Srivastava, Lucie Turcotte

Affiliation: Department of Medicine, Duke University School of Medicine, Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, The University of Texas MD Anderson Cancer Center, Department of Oncology, St. Jude Children’s Research Hospital, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, Division of Pediatric Epidemiology and Clinical Research, University of Minnesota, Division of Childhood Cancer Epidemiology, University Medicine at Johannes Gutenberg University Mainz, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Pediatrics, University of Minnesota, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Biostatistics, St. Jude Children’s Research Hospital, Clinical Research Division, Fred Hutchinson Cancer Center, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology and Winship Cancer Institute, Emory University, The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences

Abstract Preview: Purpose: To (1) develop and validate a novel anatomically realistic pediatric/adolescent population-based breast model, (2) incorporate model into an age-scalable female reference phantom, and (3) dem...

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 Robust 3D-Ultrasound (3DUS) Quality Assurance (QA) Framework for a Real-Time Needle-Tracking System in HDR GYN Brachytherapy

Authors: Diandra Ayala-Peacock, Lindsey Hendricks Bloom, Oana I. Craciunescu, Julie A. Raffi, Megan K. Russ, Ryan Sanford, Rajesh Venkarataman

Affiliation: Clinical Imaging Physics Group, Department of Radiology, Duke University Health System, Duke University Medical Center, Duke University, Eigen Health, Duke Univesity

Abstract Preview: Purpose: To establish a robust QA framework to characterize and ensure accuracy, reliability, and reusability of a transrectal ultrasound (TRUS)-based needle tracking system (NTS) for clinical HDR GYN...

Diffusion Model-Based Motion Correction in Portable Computed Tomography for Brain: Human Observer Study

Authors: Rajiv Gupta, Rehab Naeem Khalid, Min Lang, Michael H Lev, Quirin Strotzer, Matthew Tivnan, Maryam Vejdani-Jahromi, Dufan Wu, Siyeop Yoon, Chen Zhennong

Affiliation: Massachusetts General Hospital

Abstract Preview: Purpose: Patient motion is a major source of artifacts in portable brain CT due to the slow scanning speed. A diffusion model was developed to reduce these motion artifacts. This work aims to assess t...

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...

Enhance Four-Dimension Cone-Beam Computed Tomography (4D-CBCT) from Sparse Views Using a Novel Deep Learning Model

Authors: Lei Ren, Jie Zhang

Affiliation: University of Maryland School of Medicine

Abstract Preview: Purpose: 4D-CBCT is valuable for imaging anatomy affected by respiratory motions to guide radiotherapy delivery. However, 4D-CBCT often has undersampled projections acquired in each respiratory phase ...

Enhancing Image Quality in Acoustic Imaging Using the Segment Anything Model (SAM)

Authors: Jadon Buller, Zhuoran Jiang, Yankun Lang, Lei Ren, Leshan Sun, Liangzhong Xiang, Yifei Xu

Affiliation: University of Maryland School of Medicine, University of California, Irvine, University of California, Stanford University

Abstract Preview: Purpose: Electroacoustic tomography (EAT) and Protoacoustic (PA) imaging are novel modalities for treatment verification of electroporation and proton therapy. However, the limited acquisition angle i...

Evaluate a Deep-Learning Auto-Segmentation Software for Liver SIRT

Authors: Wookjin Choi, Jun Li

Affiliation: Thomas Jefferson University

Abstract Preview: Purpose: Resin Yttrium-90 (Y-90) selective internal radiation therapy (SIRT) is a radioembolization procedure which uses Y-90 microspheres to treat metastatic liver cancer. In the procedure, liver vol...

Evaluating Uncertainty Estimation Models for Clinical Integration of AI-Generated Radiotherapy Dose Distributions

Authors: Jacob S. Buatti, Kristen A. Duke, Malena Fassnacht, Neil A. Kirby, Parker New, Niko Papanikolaou, Arkajyoti Roy, Yuqing Xia, Michelle de Oliveira

Affiliation: The University of Texas San Antonio, UT Southwestern Medical Center, UT Health San Antonio

Abstract Preview: Purpose:
Quantifying and visualizing uncertainty is critical for building clinical trust in AI-generated dose distributions. This study evaluates Monte Carlo Dropout (MCD), Snapshot Ensemble (SE), ...

Evaluating the Performance of Using Large Language Models to Automate Summarization of CT Simulation Orders in Radiation Oncology

Authors: Meiyun Cao, Edward L. Clouser, Xiaoning Ding, Jason Michael Holmes, Shaw Hu, Linda L. Lam, Wendy S. Lindholm, Wei Liu, Samir H. Patel, Diego Santos Toesca, Jason Sharp, Sujay A. Vora, Peilong Wang

Affiliation: Department of Radiation Oncology, Mayo Clinic, Mayo Clinic Arizona, George Washington University

Abstract Preview: Purpose: In current clinical workflow of radiation oncology departments, therapists manually summarize CT simulation orders into summaries before the CT simulation for execution. This process signific...

Evaluation of Daily Respiratory Pattern from a Single Free-Breathing Cone Beam CT Scan

Authors: Weixing Cai, Laura I. Cervino, Yabo Fu, Xiuxiu He, Tianfang Li, Xiang Li, Hao Zhang

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

Abstract Preview: Purpose:
This work aims to develop an innovative technique to evaluate patients’ daily respiratory pattern using three-dimensional (3D) deformation vector fields (DVF) derived from a free-breathing...

Evaluation of Nodule Volume Accuracy with Deep Learning-Based Reconstructions on Cdznte Photon-Counting and Energy-Integrating CT

Authors: Gisell Ruiz Boiset, Paulo ROBERTO Costa, Luuk J Oostveen, Elsa Bifano Pimenta, Ioannis Sechopoulos, Alessandra Tomal

Affiliation: Radboud University Medical Center, University of SĂŁo Paulo (USP), Institute of Physics, Universidade Estadual de Campinas. Instituto de FĂ­sica Gleb Wataghin

Abstract Preview: Purpose: This study aimed to evaluate the precision and accuracy of volume measurements for solid nodules (SNs) and ground-glass opacities (GGOs) in lung images acquired using energy-integrating CT (E...

Explainable AI with Attention Gates for Transparent and Interpretable Lung Radiotherapy Plan Evaluation

Authors: Jeffrey D. Bradley, Steven J. Feigenberg, Cole Friedes, Yin Gao, Xun Jia, Kevin Teo, Lingshu Yin, Jennifer Wei Zou

Affiliation: Department of Radiation Oncology, University of Pennsylvania, Johns Hopkins University

Abstract Preview: Purpose: Understanding how physicians evaluate plans is critical for automatic planning and ensuring consistent, high-quality care. While deep-learning models excel in complex decision-making, the lac...

Explainable Hybrid CNN-LLM Model to Guide Treatment Planning of Cervical Cancer High Dose Rate Brachytherapy

Authors: Adnan Jafar, Xun Jia, Michael B. Roumeliotis

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

Abstract Preview: Purpose: HDR brachytherapy (HDRBT) treatment planning is challenging due to the need for high-quality plans under time pressure, considering anatomy and applicator geometry. This study proposes an exp...

Fast 3D Scintillation Dosimetry Using Single View Deep Learning Reconstruction

Authors: Louis Archambault, Nicolas Drouin, Alexis Horik, Simon Thibault

Affiliation: Département de Physique, de Génie Physique et D'optique, et Centre de Recherche sur le Cancer, Université Laval, Département de Physique, de Génie Physique et D'optique, et Centre d'optique, photonique et laser, Université Laval

Abstract Preview: Purpose: To develop a novel type of real-time 3D dosimeter for the quality assurance of linear accelerators used in external beam radiotherapy.
Methods: An experimental setup was constructed using ...

Feasibility of Markerless Dynamic Tumor Tracking-VMAT Using Diaphragm Detection and Respiratory Phase-Based Offset Vector

Authors: Noriko Kishi, Takashi Mizowaki, Mitsuhiro Nakamura, Yukine Shimizu

Affiliation: Kyoto University, Kyoto Univercity, Department of Radiation Oncology and Image-applied Therapy, Kyoto University Graduate School of Medicine, Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University

Abstract Preview: Purpose: To predict tumor positions in markerless dynamic tumor tracking (ML-DTT)-VMAT by compensating for the asynchrony between the tumor and the diaphragm.
Methods: Rotational fluoroscopic X-ray...

Feasibility of Using a Convolutional Neural Network to Predict Physician Evaluation of Synthetic Medical Images

Authors: Sofia Beer, Menal Bhandari, Alec Block, Nader Darwish, Joseph Dingillo, Sebastien A. Gros, Hyejoo Kang, Andrew Keeler, Rajkumar Kettimuthu, Jason Patrick Luce, Ha Nguyen, John C. Roeske, George K. Thiruvathukal, Austin Yunker

Affiliation: Data Science and Learning Division, Argonne National Laboratory, Department of Radiation Oncology, Stritch School of Medicine, Loyola University Chicago, Stritch School of Medicine Loyola University Chicago, Cardinal Bernardin Cancer Center, Loyola University Chicago, Department of Computer Science, Loyola University of Chicago

Abstract Preview: Purpose: Artificial intelligence (AI) generated synthetic medical images are seeing increased use in radiology and radiation oncology. Physician observer studies are an ideal way to evaluate the usabi...

Four-Dimensional Computed Tomography Based on 16cm-Long Detectors Vs 4cm-Long Detectors: Dose Validation

Authors: Qianyi Xu, Inhwan Yeo

Affiliation: INOVA Schar Cancer Institute, Thomas Jefferson University

Abstract Preview: Purpose:
Four-dimensional computed tomography(4DCT) is prone to a geometrical error when respiration changes upon patient shift. 4DCT with a longer-length detector(16cm), uninvolving the shift, has...

Fully Automated Review of Prostate Radiotherapy Treatment Plan Quality

Authors: Yasin Abdulkadir, John Charters, Melissa Ghafarian, James M. Lamb, Dishane Chand Luximon, Jack Neylon

Affiliation: Department of Radiation Oncology, University of California, Los Angeles

Abstract Preview: Purpose:
Assessment of radiotherapy treatment quality in large-scale multi-institutional contexts remains an outstanding challenge. Retrospective human review of treatment plans is labor intensive ...

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...

Generating Brain Pseudo-CT from PET-Only Images Using Deep Learning Method

Authors: Pouya Azarbar, Nima Kasraie, Mahsa Shahrbabki Mofrad, Peyman Sheikhzadeh

Affiliation: UT Southwestern Medical Center, Shahid Beheshti University of Medical science, Imam Khomeini Hospital Complex,Tehran University of Medical Sciences, Tehran University of Medical Science

Abstract Preview: Purpose: PET imaging become crucial in diagnosing and managing various diseases, but its key limitation is the lack of detailed anatomical information. Integrating CT-scans with PET images enhances cl...

Generating Synthetic Positron Emission Tomography from Computed Tomography Using Lightweight Diffusion Model for Head and Neck Cancer

Authors: Rashmi Bhaskara, Shravan Bhavsar, Ananth Grama, Oluwaseyi Oderinde, Shourya Verma

Affiliation: Purdue University

Abstract Preview: Generating Synthetic Positron Emission Tomography from Computed Tomography using Lightweight Diffusion Model for Head and Neck Cancer
Purpose: To generate synthetic PET tumor avidity segments direc...

Generation of Patient-Specific Phantom for Head & Neck Proton Therapy Based on Xcat

Authors: Cheng-En Hsieh, Shen-Hao Li, Hsin-Hon Lin, Shu-Wei Wu, An-Ci Yang

Affiliation: Department of Medical Imaging and Radiological Sciences, Chang Gung University, Proton and Radiation Therapy Center, Chang Gung Memorial Hospital, Proton and Radiation Therapy Center, Chang Gung Memorial Hospital Linkou

Abstract Preview: Purpose:
The aim of this study is to develop a framework of generating patient-specific phantom tailored for head and neck proton therapy. From these phantoms, digital reference objects based on th...

Generation of Virtual Lung PET Images from CT Data Via Deep Learning for Accelerated Tumor Detection and Preliminary Diagnosis

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...

Head and Neck Treatment Evaluation with Cone Beam Computed Tomotherapy

Authors: Varsha Jain, Jun Kang, Paul Sulivan, Kate E. Verma, Yevgeniy Vinogradskiy

Affiliation: Thomas Jefferson University

Abstract Preview: Purpose:
Patients undergoing treatment for head and neck(H&N) cancers experience significant body contour changes throughout treatment. Body contour changes are due to tumor shrinkage, weight loss,...

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...

Hyperpolarized 13c Image Superresolution with Deep Learning

Authors: Kofi M. Deh, Tamas Jozsa, Tsang-Wei Tu

Affiliation: Cranfield University, Howard University Hospital, Howard University

Abstract Preview: Purpose: To enhance the quality of hyperpolarized (HP) 13C magnetic resonance images by integrating deep learning with perfusion modeling.
Methods: A convolutional neural network (CNN) and a superr...

Impact of Transfer Learning on Estimation of Intravoxel Incoherent Motion Parameters in the Liver

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...

Improving Head and Neck Radiotherapy Accuracy through Real-Time Volumetric Imaging Using a Kalman Filter Approach

Authors: Youssef Ben Bouchta, Chen Cheng, Owen Thomas Dillon, Mark Gardner, Paul J. Keall, Purnima Sundaresan

Affiliation: Radiation Oncology Network, Western Sydney Local Health District, Image X Institute, Faculty of Medicine and Health, The University of Sydney

Abstract Preview: Purpose: There are three clinical motivations for real time IGRT in head and neck cancer radiation therapy: (1)50% of patients experience anxiety from the patient mask (2)patient motion still occurs d...

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 ...

Integrating Neuroanatomic Knowledge in Clinical Target Volumes for Glioma Patients Using Deep Learning

Authors: Ali Ajdari, Thomas R. Bortfeld, Christopher Bridge, Gregory Buti, Marcela Giovenco, Fredrik Lofman, Gregory C. Sharp, Helen A Shih, Tugba Yilmaz

Affiliation: Massachusetts General Hospital, RaySearch Laboratories, Department Of Radiation Oncology, Massachusetts General Hospital (MGH), Massachusetts General Hospital & Harvard Medical School, Massachusetts General Hospital and Harvard Medical School

Abstract Preview: Purpose: Defining radiation target volumes with accurate integration of the neuroanatomy is one of the major difficulties in designing glioma treatments. We developed a deep learning network for norma...

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...

Is Simplicity Even Better: Deep Learning Algorithms for Breath Motion Phase Prediction in Motion Management

Authors: Amanda J. Deisher, Andrew YK Foong, Witold Matysiak, Jing Qian, Xueyan Tang, Erik J. Tryggestad, Mi Zhou

Affiliation: Mayo Clinic

Abstract Preview: Purpose: Phase gating is commonly employed to mitigate the impact of tumor motion in radiotherapy. Due to the machine-specific time delay between triggering and radiation delivery, the triggering sign...

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...

Latent Diffusion for 3D CT Reconstruction from Biplanar X-Rays

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...

Leveraging Photon Counting Detector CT and Advance Reconstructions to Improve the Accuracy of Vessel Tracing in Prostate Artery Embolization

Authors: Christopher P. Favazza, Andrea Ferrero, Taylor Froelich, Forrest Linch, Scott Thompson

Affiliation: Mayo Clinic

Abstract Preview: Purpose: Photon counting detector (PCD) CT is a promising tool to enhance pre-procedural planning and intra-procedural guidance for prostate artery embolization due to its unsurpassed ultra-high spati...

Lung Nodule Volume Estimation: Performance of Conventional Energy-Integrating and Cdznte-Photon-Counting CT Using Hybrid Reconstruction Method

Authors: Gisell Ruiz Boiset, Paulo ROBERTO Costa, Luuk J Oostveen, Elsa Bifano Pimenta, Ioannis Sechopoulos, Alessandra Tomal

Affiliation: Radboud University Medical Center, University of SĂŁo Paulo (USP), Institute of Physics, Universidade Estadual de Campinas. Instituto de FĂ­sica Gleb Wataghin

Abstract Preview: Purpose: The study evaluated the accuracy and precision of lung nodule volume measurements, specifically solid nodules (SNs) and ground-glass opacities (GGOs) in different imaging settings.
Methods...

Lymph Node Malignancy Prediction in Head and Neck Cancer Using a Graph Neural Network

Authors: Liyuan Chen, Meixu Chen, Bowen Jing, Sepeadeh Radpour, Erich Josef Schmitz, David Sher, Jing Wang

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: Prospective clinical trials have shown that involved nodal radiation therapy (INRT) can substantially improve patients’ quality of life without increasing the risk of elective nodal failure. ...

Mask-Based Synthetic Contrast-Enhanced CT Generation for Advancing Data Limited Segmentation on Cardiac Substructure

Authors: Jin Sung Kim, Chanwoong Lee, Young Hun Yoon

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

Abstract Preview: Purpose: Chest contrast-enhanced CT (CECT) serves as a valuable tool for cardiac imaging, but its lack of detailed anatomical visualization limits its utility in segmentation tasks. While CECT offers ...

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 ...

Motion Correction-Driven Patient-Specific 2D Cine MRI-Based Dynamic Volumetric Reconstruction for MRI-Guided Radiotherapy Intra-Fractional Motion Monitoring

Authors: Karyn A Goodman, Yang Lei, Tian Liu, D. Michael Lovelock, Charlotte Elizabeth Read, Jing Wang, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai

Abstract Preview: Purpose: Real-time volumetric MRI is essential for precise motion management in MRI-guided radiotherapy (MRIgRT). While 2D Cine MRI offers high temporal resolution for motion tracking, it inherently l...

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...

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...

Organ Dose Estimation from Successive CBCT Imaging for Patients Undergoing Radiation Therapy for Prostate Cancer

Authors: Panagiotis Iliopoulos, Marios Myronakis, Kyriaki Theodorou

Affiliation: Medical Physics Department, Medical School, University of Thessaly

Abstract Preview: Purpose: To estimate individualized absorbed dose at Organs at Risk (OARs) from kV Cone Beam CT (CBCT) imaging in the pelvic area, prior radiotherapy, and assess excess risk for secondary malignancies...

Parameterized 4D Deformable Registration (p4Dreg) in Abdominal 4DCT Scans

Authors: Edward Robert Criscuolo, Deshan Yang

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

Abstract Preview: Purpose:
Deformable registration of 4DCT images has many clinical applications, but current methods are unreliable and can produce dangerous errors. Iterative, parametrized image registration does ...

Patient-Specific Imaging Modality Agnostic Virtual Digital Twins Modeling Temporally Varying Digestive Motion

Authors: James M. Balter, Lando S. Bosma, Jorge Tapias Gomez, Nishant Nadkarni, Mert R Sanbuncu, William Paul Segars, Ergys D. Subashi, Neelam Tyagi, Harini Veeraraghavan

Affiliation: University of Michigan, The University of Texas MD Anderson Cancer Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Duke University Medical Center, Cornell University, University Medical Center Utrecht, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Develop patient-specific virtual digital twin (VDT) cohorts modeling physically realistic spatio-temporal gastrointestinal (GI) organs (stomach and duodenum) digestive motion.
Methods: Pat...

Performance Evaluation of Patient Demographics Model-Based Liver Volumetry

Authors: Yasaman Anbari, Srinivas Cheenu Kappadath, Benjamin P. Lopez, Armeen Mahvash, Ali Yousefi

Affiliation: University of Houston, UT MD Anderson Cancer Center

Abstract Preview: Purpose: Patient-demographics-model-based liver volumetry is well-established for determining the future liver remnant following hepatectomy. We used gold-standard CT liver segmentation to validate th...

Physics and Geometry Input-Based Neural Network Dose Engine

Authors: Ricardo Garcia Santiago, Narges Miri, Daryl P. Nazareth, Ankit Pant, Mukund Seshadri

Affiliation: Roswell Park Comprehensive Cancer Center

Abstract Preview: Purpose: To develop a transformer-based deep learning network framework for predicting VMAT dose distributions. This can provide fast and efficient calculations with accuracies potentially comparable ...

Portpy: An Open-Source Python Package to Accelerate Research in Radiotherapy Treatment Planning Optimization

Authors: Qijie Huang, Gourav Jhanwar, Saad Nadeem, Vicki Trier Taasti, Mojtaba Tefagh, Seppo Tuomaala, Masoud Zarepisheh

Affiliation: Varian Medical Systems Inc, Department of Clinical Medicine - Danish Center for Particle Therapy, Aarhus University Hospital, Memorial Sloan Kettering Cancer Center, The University of Edinburgh

Abstract Preview: Purpose:
We have developed PortPy (Planning and Optimization for Radiation Therapy in Python), a first-of-its-kind open-source package designed to accelerate research and development in radiotherap...

Posterior-Mean Diffusion Model for Realistic PET Image Reconstruction

Authors: Osama R. Mawlawi, Yiran Sun

Affiliation: RICE University, UT MD Anderson Cancer Center

Abstract Preview: Purpose: Conventional PET reconstruction methods often produce noisy images with artifacts due to data/model mismatches and inconsistencies. Recently, deep learning-based conditional denoising diffusi...

Predicting Brain V60% in Linac-Based Single-Isocenter-Multiple-Targets (SIMT) Stereotactic Radiosurgery Using Machine Learning

Authors: John Ginn, Zhuoyun Huang, Yongbok Kim, Ke Lu, Chunhao Wang, Yibo Xie, Zhenyu Yang, Jingtong Zhao

Affiliation: Duke University, Duke Kunshan University

Abstract Preview: Purpose: This study aims to develop and validate a machine learning model for predicting V60%, a critical dosimetric metric in LINAC-based Single-Isocenter-Multiple-Targets (SIMT) stereotactic radiosu...

Predicting Elective Pelvic Nodal Volumes with Deep Learning: A Tool to Facilitate Peer Review

Authors: Brian M. Anderson, Shiva K. Das, Meagan Foster, Anirudh Karunaker, Lawrence B. Marks, Lukasz Mazur, Michael Repka

Affiliation: UNC Chapel HIll, University of North Carolina at Chapel Hill, UNC School of Medicine, University of North Carolina

Abstract Preview: Purpose: Development of a peer review segmentation check system to identify deviations in physician contours of standard risk pelvic lymph nodes in patients receiving radiation therapy for prostate an...

Quantitative Fluorescence Molecular Tomography Guide Radiation Therapy

Authors: Jiahao Chen, Yunwen Huang, Yidong Yang, Shanshan Zhang, Ning Zhao, Cheng Zheng

Affiliation: Department of Life Sciences and Medicine, University of Science and Technology of China, Department of Engineering and Applied Physics, University of Science and Technology of China, University of Science and Technology of China

Abstract Preview: Purpose: To develop a molecular image-guided radiotherapy technique for accurate and effective radiation treatment using quantitative fluorescence molecular tomography (FMT).
Methods: X-ray cone be...

Rapid Reconstruction of Extremely Accelerated Liver 4D MRI Via Chained Iterative Refinement

Authors: Mary Feng, Yi Lao, Hui Lin, Hengjie Liu, Xin Miao, Michael Ohliger

Affiliation: University of California, Los Angeles, Department of Radiation Oncology, University of California San Francisco, Department of Radiation Oncology, City of Hope National Medical Center, University of California San Francisco, Siemens Medical Solutions USA Inc.

Abstract Preview: Purpose: 4D MRI with high spatiotemporal resolution is vital to characterize the tumor/tumor motion for liver radiotherapy. However, high-quality 4D MRI requires an impractically long scanning time fo...

Real Time Monte Carlo Dose Calculation for Clinical Cyberknife Radiation Therapy Based on Deep Learning Diffusion Model

Authors: Ruiyan Du, He Huang, Mingzhu Li, Ying Li, Hongyu Lin, Wei Liu, Shihuan Qin, Yiming Ren, Hui Xu, Lian Zhang, Xiao Zhang, Zunhao Zhang

Affiliation: Department of Radiation Oncology, Mayo Clinic, Medical AI Lab, The First Hospital of Hebei Medical University, Hebei Provincial Engineering Research Center for AI-Based Cancer Treatment Decision-Making, The First Hospital of Hebei Medical University, Department of Oncology, The First Hospital of Hebei Medical University

Abstract Preview: Purpose: Monte Carlo (MC) dose calculation is the gold standard in clinical CyberKnife radiation therapy (RT), considering its steep dose gradients and high-freedom non-coplanar beam angles, but extre...

Real-Time Proton and Carbon Ion Monte Carlo Dose Calculation through GPU-Acceleration and DL-Based Denoising Algorithms

Authors: Yankui Chang, Shijun Li, Xi Pei, Ripeng Wang, Xuanhe Wang, X. George Xu, Qing Zhang, Jingfang Zhao

Affiliation: University of Science and Technology of China, Shanghai proton and heavy ion center, School of Nuclear Science and Technology, University of Science and Technology of China, Anhui Wisdom Technology Co., Ltd.

Abstract Preview: Purpose:
This paper describes disruptive methods using both GPU-based MC simulation and deep-learning (DL)-based MC denoising algorithms, as well as clinical tests involving more than 560 patient p...

Reconstructing Radiopharmaceutical Distributions Using Coded Aperture Tomography with Photon Counting Detector

Authors: David P. Adam, Xun Jia, Youfang Lai, Yuncheng Zhong

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

Abstract Preview: Purpose: Radiopharmaceutical therapy is experiencing a resurgence in interest due to its potential of treating widespread metastatic disease in a patient-specific manner. Accurately measuring and desc...

Respiratory Monitoring in Human Subjects Using a Low-Cost Optical Imaging System Prototype

Authors: Marian Axente, Mandeep Kaur

Affiliation: Emory University

Abstract Preview: Purpose: To validate a low-cost optical imaging system for respiratory monitoring by comparing its accuracy and feasibility against the clinical gold standard in human subjects.
Methods: Following ...

Simulating Realistic Digital Phantoms for Virtual Clinical Trials in Radiology and Radiation Oncology Using a Deep-Learning Based Conditional Denoising Diffusion Probabilistic Model (c-DDPM)

Authors: Matthew Brown, Yushi Chang, Jinhyuk Choi, William Silva Mendes, Lei Ren, Aman Sangal, William Paul Segars, Phuoc Tran, Hualiang Zhong

Affiliation: University of Maryland School of Medicine, Department of Radiation Oncology, University of Maryland School of Medicine, Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Duke University Medical Center, Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose: Digital phantoms like XCAT are essential for imaging and treatment optimization in radiology and radiation oncology. However, the lack of realistic textures (HU distribution) in XCAT limits i...

Synthesizing High-Quality Hepatic Vascular Tree Segmentation Datasets to Improve Segmentation Model Performance

Authors: Trevor McKeown, Deshan Yang, Zhendong Zhang

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

Abstract Preview: Purpose: Accurate delineation of liver blood vascular structures is crucial for planning and executing therapeutic interventions in liver-related medical procedures. However, current auto-segmentation...

The Effect of Radiotherapy Structure Shape Features on the Accuracy of Their Digital Representation and Manipulation

Authors: Annie Cooney, Kenneth L. Homann, Somayeh Taghizadehghahremanloo, Adam D. Yock, Hong Zhang

Affiliation: Assistant Professor, Vanderbilt University Medical Center

Abstract Preview: Purpose:
Digital radiotherapy data has been standardized using the DICOM format. However, different radiotherapy software environments interpret identical data differently due to inherent software ...

To Establish Local Diagnostic Reference Levels (DRLs) for Head and Neck Computed Tomography (CT) Exams in Abuja, Nigeria, and to Investigate the Performance of Brain Metastasis (BM) and Brain Lesion (BL) Segmentation Techniques Using U-Net Models.

Authors: Nuraddeen Nasiru Garba, Kalpana M Kanal, Abdullahi Mohammed, Rabiu Nasiru, Muhammad SHAFIU Shehu, Daniel Vergara, Joseph Everett Wishart

Affiliation: AHMADU BELLO UNIVERSITY, ZARIA, University of Washington

Abstract Preview: Purpose: To establish local Diagnostic Reference Levels (DRLs) for head and neck computed tomography (CT) exams in Abuja, Nigeria, and to investigate the performance of brain metastasis (BM) and brain...

Towards Real-Time Marker-Less Prostate Tracking on Standard Radiation Therapy Systems

Authors: Freeman Jin, Paul J. Keall, Alistair MacDonald, Adam Mylonas, Chandrima Sengupta

Affiliation: Image X Institute, Faculty of Medicine and Health, University of Sydney, Image X Institute, Faculty of Medicine and Health, The University of Sydney, Image X Institute, School of Health Sciences, University of Sydney

Abstract Preview: Purpose: During radiation therapy, tumours in the prostate may move from the planned treatment position, leading to significant dose deviations above clinical tolerances Surveys have indicated the nee...

Towards Real-Time Radiotherapy Monitoring By Cherenkov Imaging: Applications of Patient-Specific Bio-Morphological Features Segmented Via Deep Learning

Authors: Petr Bruza, Yao Chen, David J. Gladstone, Lesley A Jarvis, Brian W Pogue, Kimberley S Samkoe, Yucheng Tang, Shiru Wang, Rongxiao Zhang

Affiliation: NVIDIA Corp, Dartmouth College, Thayer School of Engineering, Dartmouth College, Dartmouth Cancer Center, University of Missouri, University of Wisconsin - Madison

Abstract Preview: Purpose: Cherenkov imaging provides real-time visualization of megavoltage radiation beam delivery during radiotherapy. Patient-specific bio-morphological features, such as vasculature, captured in th...

Ukan Architecture for Voxel-Level Dose Prediction in Radiotherapy

Authors: Lu Jiang, Ke Sheng

Affiliation: Department of Radiation Oncology, University of California at San Francisco, Department of Radiation Oncology, University of California, San Francisco

Abstract Preview: Purpose:
Conventional radiotherapy treatment planning is guided by a set of generic objectives that are unspecific to patient anatomy. Treatment planning thus heavily relies on the planner’s experi...

Uncertainties on Synthetic-CT Generation from CBCT: Another Layer of Complexity in Abdominal Adaptive Radiotherapy

Authors: Laura I. Cervino, Wendy B. Harris, Paulo Quintero, Hao Zhang

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

Abstract Preview: Purpose: To evaluate the impact of the prediction uncertainty from CBCT-based synthetic CT (sCT) generation in abdominal adaptive radiotherapy.

Methods: CT and CBCT images from 65 abdominal pat...

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...

Validation of Synthetic CT-Based Online Monitoring for Adaptive Proton Therapy

Authors: Ozgur Ates, Chin-Cheng Chen, Chia-Ho Hua, Matthew J. Krasin, Thomas E. Merchant

Affiliation: St. Jude Children's Research Hospital

Abstract Preview: Purpose: To validate the use of synthetic CTs generated from CBCT images for online monitoring, ensuring accurate and reliable daily plan quality assessments in adaptive proton therapy (APT).
Metho...

“See” through Surface: Transforming Surface Imaging into a Real-Time Three-Dimensional Imaging Solution for Intra-Treatment Image Guidance

Authors: Steve B. Jiang, Ruiqi Li, Hua-Chieh Shao, Kenneth Westover, You Zhang, Tingliang Zhuang

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

Abstract Preview: Purpose:
Respiratory motion is a long-standing challenge for lung SBRT, particularly for centrally-located lung tumors where increased toxicity demands more precise motion management during treatme...