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Results for "similarity coefficient": 89 found

4DCT Vs 5DCT: How to Generate an Accurate Target Volume

Authors: Ryan Andosca, Rojine T. Ariani, Peter Boyle, Minji Victoria Kim, Michael Vincent Lauria, Daniel A. Low, Claudia R. Miller, Drew Moghanaki, Louise Naumann, Dylan P. O'Connell, Ricky R Savjani

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

Abstract Preview: Purpose: To demonstrate that 5DCT can provide an accurate internal tumor volume (ITV) while 4DCT cannot.
Methods: The 5DCT imaging protocol uses a motion model and 25 deformably registered free-bre...

A Clinical Evaluation of Two Commercially Available Deep-Learning Algorithms for Automated Organs at Risk Contouring

Authors: Steven DiBiase, Gurtej S. Gill, Haohua Billy Huang, Nicholas J. Lavini, Luxshan Shanmugarajah, Salar Souri, Samantha Wong

Affiliation: Stony Brook University, Northwell Health, Cornell University, NewYork-Presbyterian, New York-Presbyterian

Abstract Preview: Purpose: Clinical applications of deep learning-based algorithms have come to the radiation oncology field as organ at risk (OAR) auto contouring programs. We evaluated two of these algorithms’ (Radfo...

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 Dual Energy CT-Guided Intelligent Radiation Therapy Platform

Authors: Jiayi Chen, Manju Liu, Ning Wen, Haoran Zhang, Yibin Zhang

Affiliation: Department of Radiation Oncology, Ruijin Hospital, Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Duke Kunshan University, Department of Radiation Oncology,Ruijin Hospital, Shanghai Jiao Tong University School of Medicine

Abstract Preview: Purpose: This study introduces a novel Dual Energy CT (DECT)-Guided Intelligent Radiation Therapy (DEIT) platform designed to streamline and optimize the radiotherapy process. The DEIT system combines...

A Hybrid Transformer-CNN for Tracking-Free 3D Ultrasound Volume Reconstruction from 2D Freehand Scans

Authors: Wenfeng He, Tian Liu, Pretesh Patel, Richard L.J. Qiu, Keyur Shah, Tonghe Wang, Xiaofeng Yang, Chulong Zhang

Affiliation: Icahn School of Medicine at Mount Sinai, Emory University, Medical Physics Graduate Program, Duke Kunshan University, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose: This study introduces a tracking-free approach to reconstruct 3D ultrasound (US) volumes from 2D freehand US scans. By eliminating the reliance on external tracking systems, this method aims ...

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 New Voxel-Based Similarity Approach for Assessing Contour Similarity and Clinical Dosimetric Effect

Authors: Shari Damast, Svetlana Kuznetsova, Christopher J. Tien

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

Abstract Preview: Purpose: Current contour similarity evaluation approaches (Dice Similarity Coefficient, Mean Distance to Agreement) are limited to geometric agreement without assessment of ultimate dosimetric impact....

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 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 Tumor Tracking Method in Surface-Guided Radiotherapy

Authors: Penghao Gao, Zejun Jiang

Affiliation: Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Artificial Intelligence Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences

Abstract Preview: Purpose: Real-time tumor tracking can effectively compensate for the impact of respiratory motion on dose distribution. We propose a patient-specific external-internal correlation model driven by opti...

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...

Advancing Cardiac Sparing with Upright Patient Geometry and Deep Learning

Authors: Shae Gans, Carri K. Glide-Hurst, Mark Pankuch, Chase Ruff, Niek Schreuder, Nicholas R. Summerfield, Yuhao Yan

Affiliation: Departments of Human Oncology and Medical Physics, University of Wisconsin-Madison, Northwestern Medicine Proton Center, Northwestern Medicine Chicago Proton Center, Leo Cancer Care

Abstract Preview: Purpose: Novel upright patient positioners coupled with diagnostic-quality vertical CT at treatment isocenter introduce a significant opportunity for improved image-guided particle therapy. Treating p...

Advancing Deep Segmentation Accuracy in CBCT for Radiotherapy Via Robust Scatter Mitigation: First Results from a Pilot Trial

Authors: Cem Altunbas, Farhang Bayat, Roy Bliley, Rupesh Dotel, Brian Kavanagh, Uttam Pyakurel, Tyler Robin, Ryan Sabounchi

Affiliation: Department of Radiation Oncology, University of Colorado School of Medicine, Taussig Cancer Center, Cleveland Clinic, University of Colorado Anschutz Medical Campus

Abstract Preview: Purpose: Automatic segmentation of anatomical structures in CBCT images is key to enabling dose delivery monitoring and online plan modifications in radiotherapy. However, poor image quality can degra...

Advancing Thoracic Synthetic CT Images with Enhanced Cyclegan for Adaptive Radiotherapy Applications

Authors: Silambarasan Anbumani, Nicolette O'Connell, Eenas A. Omari, Amanda Pan, Eric S. Paulson, Lindsay Puckett, Monica E. Shukla, Dan Thill, Jiaofeng Xu

Affiliation: Elekta Inc, Elekta Limited, Linac House, Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose: Accurate electron density information from on-board imaging is essential for direct dose calculations in adaptive radiotherapy (ART). This study evaluates a deep learning model for thoracic s...

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...

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-...

Applicability of Online Dose Measurement in Trilogy Linear Accelerators: Using Target Current Pulse Counting Method

Authors: Daming LI, Jinsen Xie, Zhe Zhang

Affiliation: Peking University Shenzhen Hospital Radiotherapy Department, School of Nuclear Science and Technology, University of South China

Abstract Preview: Purpose:
This study aims to investigate the feasibility of online dose measurement of the Trilogy linear accelerator output based on target current pulse signals.
Methods:
A data acquisition ...

Artificial Intelligence Based Auto-Contouring for Organs at Risk in Head and Neck

Authors: Mylinh Dang, Laila A Gharzai, Xinlei Mi, Poonam Yadav

Affiliation: Northwestern Feinberg School of Medicine, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Northwestern Medicine

Abstract Preview: Purpose: Delineation of organs at risk (OAR) in the head/neck region requires substantial physician time. Many artificial intelligence (AI) based auto-contouring software are commercially available. T...

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...

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: 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...

Box-Prompt Zero-Shot Smart Segmentation in Radiation Oncology Using a SAM-Based Model: Smartsam

Authors: Kristen A. Duke, Samer Jabor, Neil A. Kirby, Parker New, Niko Papanikolaou, Arkajyoti Roy, Yuqing Xia

Affiliation: St. Mary's University, The University of Texas San Antonio, UT Health San Antonio

Abstract Preview: Purpose:
The Segment Anything Model (SAM) is a foundational box-prompt-based model for natural image segmentation. However, its applicability to zero-shot 3D medical image segmentation, particularl...

Brain Structural Covariance Networks in Nicotine-Dependent Users: A Graph Analysis

Authors: Humberto Monsivais, Brian A. Taylor, Francesco Versace

Affiliation: Purdue University, Department of Behavioral Science, Division of Cancer Prevention and Population Sciences, The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose: To identify possible signatures of altered brain morphometry in nicotine-dependency via a structural covariance network approach.

Methods: Fifty-one healthy controls (HC:27M, mean age=...

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...

Clinical Validation of AI-Driven Segmentation Model for Pediatric Craniospinal Irradiation: Marked Reduction in Contouring Time and Enhanced Workflow Efficiency

Authors: Alexander Choi, William Ross Green, Christine Hill-Kayser, Gary D. Kao, Michael LaRiviere, Rafe A. McBeth, Steven Philbrook

Affiliation: Department of Radiation Oncology, University of Pennsylvania

Abstract Preview: Purpose: To validate the potential of clinical deployment of an in-house AI-driven auto-segmentation tool for pediatric craniospinal irradiation (CSI) in proton therapy, with goals of reducing manual ...

Comparative Analysis of Soft Tissues and 3D Printing Materials Using Euclidean Distance for Linear Attenuation Coefficient

Authors: Gisell Ruiz Boiset, Paulo ROBERTO Costa, Elsa Bifano Pimenta

Affiliation: University of SĆ£o Paulo (USP), Institute of Physics

Abstract Preview: Purpose: This study evaluates the similarity between the linear attenuation coefficients of soft tissues and 3D printing materials in diagnostic imaging range using the Euclidean distance approach.

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, ...

Comprehensive Evaluation of Federated Learning Strategies for Head and Neck Tumor Segmentation on PET/CT Images

Authors: Jingyun Chen, Yading Yuan

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

Abstract Preview: Purpose: To evaluate centralized and decentralized strategies for federated head and neck tumor segmentation on PET/CT.
Methods: We utilized training data from the HEad and neCK TumOR segmentation ...

Contrastive Learning and Hybrid CNN-Transformer Model for Unpaired MR Image Synthesis in Acute Cerebral Infarction

Authors: Kota Hirose, Daisuke Kawahara, Jokichi Kawazoe, Yuji Murakami

Affiliation: Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Graduate School of Biomedical and Health Sciences, Hiroshima University

Abstract Preview: Purpose: Synthesizing medical images can address the lack of or unscanned medical images, reducing scanner time and costs. However, paired image scarcity remains a challenge for image synthesis. We pr...

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 of Oars in Head and Neck Radiation Therapy

Authors: Laila A Gharzai, Bharat B Mittal, Poonam Yadav

Affiliation: Northwestern Feinberg School of Medicine, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Northwestern University Feinberg School of Medicine

Abstract Preview: Purpose: Multiple studies have shown the increasing role of deep learning in segmenting regions of interest. This work presents the feasibility of auto-segmenting the critical structures for head and ...

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 ...

Deep Learning-Based Plan Quality Prediction for Gamma Knife Radiosurgery of Brain Metastases

Authors: Chih-Wei Chang, Runyu Jiang, Mark Korpics, Yuan Shao, Aranee Sivananthan, Zhen Tian, Ralph Weichselbaum, Xiaofeng Yang, Aubrey Zhang, Xiaoman Zhang

Affiliation: Department of Radiation & Cellular Oncology, University of Chicago, University of Chicago, Department of Physics, University of Chicago, Emory University, Department of Radiation Oncology and Winship Cancer Institute, Emory University, School of Public Health, University of Illinois Chicago

Abstract Preview: Purpose: Gamma Knife (GK) plan quality can vary significantly among planners, even for cases handled by the same planner. Although plan quality metrics such as coverage, selectivity, and gradient inde...

Deep Learning-Driven Comparative Analysis of CNN-Based Architectures and High-Order Vision Mamba U-Net (H-vMUNet) for MRI-Based Brain Tumor Segmentation

Authors: Sang Hee Ahn, Nalee Kim, Do Hoon Lim

Affiliation: Samsung Medical Center, Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine

Abstract Preview: Purpose: MRI offers superior soft-tissue contrast, aiding tumor localization and segmentation in radiation therapy, which traditionally relies on oncologists' expertise. This study compares CNN-based ...

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 Brain-like Digital Reference Object of Resting-State Functional MRI

Authors: Henry Szu-Meng Chen, Mu-Lan Jen, Vinodh A. Kumar, Ho-Ling Anthony Liu, Jian Ming Teo

Affiliation: School of Medicine, University of Colorado Denver, Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose: Resting-state (rs-) fMRI detects functional networks by measuring synchronization of low-frequency oscillations in blood-oxygenation-level-dependent (BOLD) signals between brain regions. Stan...

Development of an Orthogonal X-Ray Projections-Guided Cascading Volumetric Reconstruction and Tumor-Tracking Model for Adaptive Radiotherapy

Authors: Penghao Gao, Zejun Jiang, Huazhong Shu, Linlin Wang, Gongsen Zhang, Jian Zhu

Affiliation: Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, Southeast University, Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Artificial Intelligence Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences

Abstract Preview: Purpose: We propose a cascading framework for time-varying anatomical volumetric reconstruction and tumor-tracking, guided by onboard orthogonal-view X-ray projections.
Methods: We employe multiple...

Do We Need Pediatric-Specific Models for Radiotherapy Auto-Contouring? a Comparative Study of Pediatric and Adult-Trained Tools

Authors: Gregory T. Armstrong, James E. Bates, Christine V. Chung, Lei Dong, Ralph Ermoian, Jie Fu, Christine Hill-Kayser, Rebecca M. Howell, Meena S. Khan, Sharareh Koufigar, John T. Lucas, Thomas E. Merchant, Taylor Meyers, Tucker J. Netherton, Constance A. Owens, Arnold C. Paulino, Sogand Sadeghi

Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, St. Jude Children's Research Hospital, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Department of Radiation Oncology, St. Jude Children’s Research Hospital, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, University of Washington/ Fred Hutchinson Cancer Center, Department of Radiation Oncology, University of Pennsylvania, University of Pennsylvania, 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: Clinical workflows often rely on auto-segmentation tools trained on adult data, which may exhibit suboptimal performance in pediatric imaging due to inherent anatomical variations and smaller...

Dosimetric Impact of Auto-Segmentation with Replanning: An Analysis of a Prospective Clinical Trial

Authors: Kathryn J. Dess, Martha M. Matuszak, Dan Polan

Affiliation: University of Michigan

Abstract Preview: Purpose: A recent survey demonstrated 18 of 20 top academic institutions have implemented auto-segmentation. Studies to date have focused on geometric contour changes and dosimetric differences using ...

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...

Enhancing Adaptive Radiotherapy Segmentation with a 3D Unet Framework and Prior Fraction Information

Authors: Jennifer L. Dolan, Chengyin Li, Parag Parikh, Doris N. Rusu, Kundan S Thind

Affiliation: Henry Ford Health, Cedars-Sinai Medical Center

Abstract Preview: Purpose: The time and resource demands of online Adaptive Radiation Therapy (ART) can limit its widespread clinical adoption and potentially impact patient throughput. To address this, we developed a ...

Enhancing Proton Treatment and Mitigating Radiation-Induced Lung Injury Using a Novel Cycle Diffusion Approach for Lung Ventilation Estimation

Authors: Yang Lei, Haibo Lin, Tian Liu, Charles B. Simone, Shouyi Wei, Ajay Zheng

Affiliation: Icahn School of Medicine at Mount Sinai, New York Proton Center

Abstract Preview: Purpose: Radiation-induced lung injury (RILI), encompassing pneumonitis and fibrosis, represents a critical dose-limiting factor in lung cancer radiation therapy. Variability in treatment outcomes is ...

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 Commercial Auto-Segmentation Software: Is Performance on Pediatric Organs-at-Risk Accurate?

Authors: Gregory T. Armstrong, James E. Bates, Lei Dong, Ralph Ermoian, Jie Fu, Christine Hill-Kayser, Rebecca M. Howell, Sharareh Koufigar, John T. Lucas, Thomas E. Merchant, Tucker J. Netherton, Sogand Sadeghi

Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, St. Jude Children's Research Hospital, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Department of Radiation Oncology, St. Jude Children’s Research Hospital, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, University of Washington/ Fred Hutchinson Cancer Center, Department of Radiation Oncology, University of Pennsylvania, University of Pennsylvania, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose: This study evaluates the adaptability and limitations of commercially available (MIM, RayStation) tools trained on predominately adult datasets (ages 20–60+ years) for delineating organs at r...

Evaluating Necessity of Patient-Specific Deep Learning-Based Auto-Segmentation for Improved Adaptation for Abdominal Tumors

Authors: Asma Amjad, Renae Conlin, Eric S. Paulson, Christina M. Sarosiek

Affiliation: Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose: In an effort to improve contouring accuracy for abdominal MR guided online adaptive radiotherapy (MRgOART), patient-specific deep learning-based auto-segmentation (PS-DLAS) has been proposed....

Evaluating the Impact of Different Deface Algorithms on the Deep Learning Segmentation Software Performance

Authors: Ali Ammar, Quan Chen, Yi Rong, Libing Zhu

Affiliation: Mayo Clinic Arizona

Abstract Preview: Purpose: To investigate how defacing algorithms, essential for patient privacy in data sharing, impact AI-based segmentation performance in CT imaging for radiation therapy. This study evaluates wheth...

Evaluation of Deformable Image Registration Accuracy Used in MR-Only Ventilation Mapping

Authors: Fei Han, James M. Lamb, Michael Vincent Lauria, Daniel A. Low, Tessa Elizabeth Maurer, Danilo Maziero, Claudia R. Miller, Drew Moghanaki, Louise Naumann, Nicolas Viot

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

Abstract Preview: Purpose: Patients with lung disease outside radiotherapy are barred from high dose protocols used for motion modeling, but MRI could offer no-dose alternatives. Image-based ventilation is a promising ...

Expert Verification of AI-Generated Cardiac Substructures and Dosimetric Differences between Auto-Contoured and Manually Delineated Contours

Authors: Stephen R. Bowen, Richard Cheng, Kylie Kang, Janice Kim, Ana Paula Santos Lima, Dominic A. Maes, Juergen Meyer, Karen Ordovas, Kerry Reding

Affiliation: Department of Radiation Oncology, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Department of Radiology, University of Washington, Division of Cardiology, University of Washington, Department of Biobehavioral Nursing and Health Informatics, School of Nursing, University of Washington

Abstract Preview: Purpose: Artificial intelligence (AI)-based auto-segmentation tools can increase the efficacy and reproducibility of radiotherapy (RT) treatment planning. This study evaluates the quality of AI-genera...

Fast Synthetic-CT-Free Dose Calculation in MR Guided RT

Authors: Claus Belka, Stefanie Corradini, George Dedes, Nikolaos Delopoulos, Christopher Kurz, Guillaume Landry, Ahmad Neishabouri, Domagoj Radonic, Adrian Thummerer, Niklas Wahl, Fan Xiao

Affiliation: Department of Radiation Oncology, LMU University Hospital, LMU Munich, Department of Medical Physics, LMU Munich, Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO)

Abstract Preview: Purpose: In MR-guided online adaptive radiotherapy, MRI lacks tissue attenuation information necessary for accurate dose calculations. Instead of using deep learning methods to generate synthetic CT i...

Foundation Model-Augmented Learning for Automatic Delineation in Precision Radiotherapy

Authors: Xianjin Dai, PhD, Michael Gensheimer, Praveenbalaji Rajendran, Lei Xing, Yong Yang

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

Abstract Preview: Purpose: Recent advances in the automatic delineation of radiotherapy treatment targets, which incorporate linguistic clinical data extracted by large language models (LLMs) into traditional visual-on...

Foundation Models with Balanced Data Sampling Enhance Auto-Segmentation for Cardiac Substructures

Authors: Chloe Min Seo Choi, Nikhil Mankuzhy, Aneesh Rangnekar, Andreas Rimner, Maria Thor, Harini Veeraraghavan, Abraham Wu

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

Abstract Preview: Purpose: Cardiac substructure irradiation predisposes patients for poor outcomes in thoracic radiation therapy. A deep learning model was developed to segment the cardiac substructures invariant to co...

From Concept to Clinic: A Phase-Based Approach for Implementing Auto-Segmentation in Radiation Therapy

Authors: Elizabeth L. Covington, Robert T. Dess, Charles S. Mayo, Michelle L. Mierzwa, Dan Polan, Jennifer Shah, Claire Zhang

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

Abstract Preview: Purpose: Auto-segmentation improves contour consistency and standardization in radiation therapy but may introduce variations from current practices, potentially impacting treatment outcomes and toxic...

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 ...

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...

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...

Graph-Based Feature Selection to Improve Stability and Reproducibility of CT-Based Radiomics in Head and Neck Squamous Cell Carcinoma: A Cross-Institutional Study

Authors: Daria Gaykalova, Ranee Mehra, Jason K Molitoris, Hajar Moradmand, Lei Ren, Amit Sawant, Phuoc Tran

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

Abstract Preview: Purpose: Radiomics extracts quantitative imaging biomarkers from medical images. However, maintaining the reproducibility and stability of selected features across institutions and parameter settings ...

High Resolution Head Motion Correction Based on Pilot Tone Signals – a Calibration-Free Method

Authors: Cheng-Chieh Cheng, Jeffrey P Guenette, Yajun Li, Bruno Madore, Lei Qin

Affiliation: Brigham and Women's Hospital, National Sun Yat-sen University, Dana-Farber Cancer Institute

Abstract Preview: Purpose: Pilot tone (PT), a compact RF sensor, has been integrated into clinical practice for motion detection. Prior studies proposed mapping PT signals to head positions using a calibration step tha...

Image Similarity Measurement Based on Handcrafted and Deep Learning Radiomics

Authors: John Ginn, Chenlu Qin, Deshan Yang

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

Abstract Preview: Purpose: Clinical implementation of auto-segmentation tools has been hindered by poor interpretability and generalizability of AI models, necessitating the development of automated contour quality ass...

Improving Segmentation Precision in Prostate Cancer Adaptive Radiotherapy with the Intentional Deep Overfit Learning (IDOL) Approach

Authors: Seungryong Cho, Donghyeok Choi, Joonil Hwang, Byung-Hee Kang, Jin Sung Kim, Eungman Lee, Younghee Park

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

Abstract Preview: Purpose: Radiation therapy (RT) is critical for cancer treatment, but changes in tumor size and shape during therapy challenge precise dose delivery. Adaptive radiation therapy (ART) addresses these v...

Insights into Deep Learning Auto-Segmentation for Abdominal Organs in MR-Guided Adaptive Radiation Therapy: A Single-Institution CT-MR Comparison

Authors: Asma Amjad, Renae Conlin, Eric S. Paulson, Christina M. Sarosiek

Affiliation: Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose:
MR-guided adaptive radiation therapy (MRgART) is transforming clinical workflows, requiring fast, accurate organs-at-risk (OARs) contouring. While deep learning auto-segmentation (DLAS) of...

Integrating Clinical Knowledge Via Llms for Precise Organ-at-Risk Segmentation in Pancreatic Cancer SBRT

Authors: Karyn A Goodman, Yang Lei, Tian Liu, Pretesh Patel, Jing Wang, Kaida Yang, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose: This study aims to improve organ-at-risk (OAR) segmentation in pancreatic cancer stereotactic body radiotherapy (SBRT) by integrating clinical guidelines into deep learning workflows. We use ...

Integrating Large Kernel Attention Mechanism into Deep Learning Model for Automatic and Auccrate Segmentation of Gross Tumor Volume in Lung Cancer Patients

Authors: Xuezhen Feng, Li-Sheng Geng, Haoze Li, Xi Liu, Tianyu Xiong, Ruijie Yang

Affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, School of Physics, Beihang University, School of Nuclear Science and Technology, University of South China, Department of Radiation Oncology, Peking University Third Hospital

Abstract Preview: Purpose: This study aimed to develop a deep learning-based algorithm for automatically delineate gross tumor volume (GTV) for lung cancer patients, alleviating the workload of radiologists and improvi...

Integrating Multiple Modalities with Pretrained Swin Foundation Model for Head and Neck Tumor Segmentation

Authors: Jue Jiang, Aneesh Rangnekar, Shiqin Tan, Harini Veeraraghavan

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Weill Cornell Graduate School of Medical Sciences

Abstract Preview: Purpose: Clinicians often use information from FDG-PET and CT to interpret and delineate gross tumor (GTVp) and nodal (GTVn) volumes for radiotherapy planning in head and neck (HN) cancer patients. He...

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...

Inter-Fractional Target Similarity during Tandem and Ring HDR Brachytherapy

Authors: Olubunmi Odunola Aregbe, Clara Ferreira, Margaret Reynolds, David A. Sterling

Affiliation: University of Minnesota, University of Minnesota Physicians, Department of Radiation Oncology, University of Minnesota, Minneapolis

Abstract Preview: Purpose: Current best practices recommend daily imaging and planning for Tandem and Ring (T&R) HDR patient plans. Accurate target delineation is a critical, yet time consuming step in this process. Th...

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...

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...

Nnae: Automating Anomaly Detection and Quality Assurance in Medical Image Segmentation

Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Kangning Zhang

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

Abstract Preview: Purpose:
Deep learning-based automatic medical image segmentation is increasingly employed in clinical practice, significantly reducing manual workload. However, verifying segmentation results rema...

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...

Novel Fast Cone-Beam CT for Adaptive Radiotherapy: Assessment of Image Distortion, Auto-Contouring, and Dose Delivery Accuracy in the Presence of Periodic Subject Motion

Authors: David P. Adam, William T. Hrinivich, Taoran Li, Alexander Lu, Michael Salerno, Alejandro Sisniega, Boon-Keng Kevin Teo

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

Abstract Preview: Purpose: Cone beam CT (CBCT)-guided online adaptive radiotherapy (ART) is of growing interest, with recent improvements in image quality provided through larger detector panels and fast gantry rotatio...

Optimizing Atlas Counts for MRI-Guided Atlas-Based Autosegmentation of Swallowing Muscles in Head and Neck Radiotherapy

Authors: Zayne Belal, Rachel Drummey, Clifton David Fuller, Stephen Y. Lai, Brigid A. McDonald, Setareh Sharafi, Sonja Stieb, Kareem Abdul Wahid

Affiliation: Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, Hospital of the University of Pennsylvania, Department of Radiology, Johns Hopkins University, KSA-KSB, Cantonal Hospital Aarau, College Of Osteopathic Medicine, NOVA Southeastern University, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose:
Radiotherapy-induced dysphagia can significantly impair head and neck (H&N) cancer patients’ quality of life. Despite the dose-dependent relationship between radiotherapy and dysphagia, sw...

PCA-Based Future Frame Prediction for Real-Time MRI-Guided Radiotherapy

Authors: B. Gino Fallone, Gawon Han, Keith D. Wachowicz, Mark G. Wright, Eugene Yip, Jihyun Yun

Affiliation: Medical Physics Division, Department of Oncology, University of Alberta, Dept. of Medical Physics, Cross Cancer Institute and Dept. of Oncology, University of Alberta, Medical Physics Division, Department of Oncology, University of Alberta and Department of Medical Physics, Cross Cancer Institute, Dept. of Medical Physics, Cross Cancer Institute and Dept. of Oncology, University of Alberta; MagnetTx Oncology Solutions, www.magnetTX.com

Abstract Preview: Purpose: MRI-radiotherapy hybrid systems can guide the therapeutic beam, dynamically adjusting to a moving tumor in real-time. However, there is a time delay from imaging and beam control, requiring p...

Pancrea-Seg-Net: A Semi-Supervised Deep Learning Framework for Pancreatic Tumor and Vessel Segmentation

Authors: Manju Liu, Ning Wen, Fuhua Yan, Yanzhao Yang, Zhenyu Yang, Haoran Zhang, Lei Zhang, Yajiao Zhang

Affiliation: Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy where precise segmentation of tumors and adjacent vessels is crucial for effective treatment planning. This study dev...

Quality Monitoring of Temporal Performance Degradation in Clinical Use of AI Auto-Segmentation

Authors: Ali Ammar, Quan Chen, Jingwei Duan, Yi Rong, Nathan Y. Yu, Libing Zhu

Affiliation: Mayo Clinic Arizona, University of Alabama at Birmingham

Abstract Preview: Purpose: Clinical performance of deep learning-based auto-segmentation (DLAS) can degrade over time due to AI ā€œagingā€ from unseen data input compared to the initial model training data. This study aim...

Retrospective Analysis of Shape and Dosage Changes in Structures during Radiotherapy for Head and Neck Cancer Patients Based on Velocity

Authors: Daming LI, Jinsen Xie, Zhe Zhang

Affiliation: Peking University Shenzhen Hospital Radiotherapy Department, School of Nuclear Science and Technology, University of South China

Abstract Preview: Purpose: To analyze the actual doses received during radiotherapy for head and neck cancers (HNC) using Velocity, providing insights for adaptive radiotherapy decision-making.
Methods: Thirty-three...

Retrospective MRI-Based Investigation of Bulboclitoris and Vaginal Canal Morphological and Physiological Changes in GYN Patients Treated with External Beam Radiation Therapy

Authors: Diandra Ayala-Peacock, Junzo Chino, Oana I. Craciunescu, Allison Jones, Kyle J. Lafata, Kim Light, Sheridan G. Meltsner, Jack B Stevens

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

Abstract Preview: Purpose: This study aims to develop an MR-based method that retrospectively correlates longitudinal changes in morphology and physiology for the bulboclitoris and vaginal canal with dose received duri...

Sensitivity of CT Ventilation Imaging to Image Acquisition and Reconstruction Parameters: A Phantom Study

Authors: Hilary Louisa Byrne, Paul J. Keall, John Kipritidis, Jeremy Lim

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

Abstract Preview: Purpose:
Non-contrast CT ventilation imaging (CTVI) has been developed as a cost-effective and accessible alternative to PET/SPECT V/Q imaging for visualizing lung function. However, the sensitivit...

Simultaneous Synthesis of Lung Perfusion and Ventilation Images from CT Using a Dual-Decoder Residual Attention Network for Lung Disease Diagnosis

Authors: Li-Sheng Geng, David Huang, Haoze Li, Xi Liu, Meng Wang, Tianyu Xiong, Ruijie Yang, Weifang Zhang, Meixin Zhao

Affiliation: School of Physics, Beihang University, Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Department of Radiation Oncology, Peking University Third Hospital, Department of Nuclear Medicine, Peking University Third Hospital, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: This study aimed to develop a deep learning-based framework for simultaneously generating lung perfusion and ventilation images from three-dimensional computed tomography (3D CT) images.
M...

Spatially Informed Auto-Segmentation of Cardiac Nodes for Radiotherapy Treatment Planning

Authors: Ming Dong, Carri K. Glide-Hurst, Joshua Pan, 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: Radiation dose to the cardiac nodes is more strongly associated with conduction disorders and arrythmias than whole heart (WH) metrics. However, node segmentation is challenging due to comple...

Standardized Immobilization and Setup Procedure Improves Accuracy of Multi-Time Point SPECT/CT Image Registration for Radiopharmaceutical Therapy (RPT) Dosimetry

Authors: Bryan Bednarz, Laura Bennett, Abby E. Besemer, Tyler J Bradshaw, Steve Y Cho, John M. Floberg, Joseph Grudzinski, Elissa Khoudary, Michael J. Lawless

Affiliation: Department of Radiology, University of Wisconsin, University of Wisconsin-Madison Department of Medical Physics, Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin - Madison, Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Voximetry, Inc, University of Pennsylvania, Department of Radiology, University of Wisconsin - Madison, Department of Human Oncology, University of Wisconsin-Madison

Abstract Preview: Purpose: To assess the impact of using a standardized immobilization setup for multi-time-point SPECT/CT imaging on radiopharmaceutical therapy (RPT) dosimetry image registration.

Methods: Ten ...

Tailor-TS System: Tailored Tumor Segmentation System with Facility-Specific Semi-Supervised Learning

Authors: Gong Vincent Hao, Daisuke Kawahara, Jokichi Kawazoe, Yuji Murakami, Ikuno Nishibuchi, Peiying Colleen Ruan, Daguang Xu, Dong Yang

Affiliation: Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Graduate School of Biomedical and Health Sciences, Hiroshima University, NVIDIA

Abstract Preview: Purpose:
Accurate tumor segmentation in head and neck cancer is critical for effective treatment planning, but variability in practices across medical facilities poses challenges for standardizatio...

The Effect of 2D Antiscatter Grid-Based CBCT on Tissue Visualization in the Prostate Region: An Observer Study on Tissue Delineation Accuracy

Authors: Cem Altunbas, Adam Avant, Farhang Bayat, Roy Bliley, Ian Boor, Brian Kavanagh, Uttam Pyakurel, Tyler Robin, Ryan Sabounchi

Affiliation: Department of Radiation Oncology, University of Colorado School of Medicine, Taussig Cancer Center, Cleveland Clinic

Abstract Preview: Purpose: Improved soft tissue visualization plays an important role in target localization and treatment plan adaptation in CBCT-guided radiotherapy. In this work, a novel CBCT approach, 2D antiscatte...

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 AI Decision-Support for Online Adaptive Radiotherapy (oART): A Preliminary Study on CBCT-Guided Post-Prostatectomy Oart

Authors: Michael Cummings, Olga M. Dona Lemus, Hana Mekdash, Tyler Moran, Alexander R Podgorsak, Sean M. Tanny, Matthew J. Webster, Lexiang Yang, Dandan Zheng, Yuwei Zhou, Xiaofeng Zhu

Affiliation: Department of Radiation Oncology, University of Rochester, University of Miami, Inova Schar Cancer Institute, University of Rochester

Abstract Preview: Purpose: oART is revolutionizing radiotherapy by allowing treatment plans to be adjusted based on daily imaging, improving targeting precision and potentially enhancing patient outcomes. However, its ...

Two-Stage Clustering and Auto Machine Learning to Predict Chemoradiation Response in Tumor Subregions on FDG PET for La-NSCLC

Authors: Stephen R. Bowen, Shijun Chen, Chunyan Duan, Daniel S. Hippe, Qiantuo Liu, Qianqian Tong, Jiajie Wang, Shouyi Wang, Faisal Yaseen

Affiliation: The University of Texas at Austin, 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: Tumor subregion clustering and prediction of region-specific response can augment assessments and adaptive treatment decisions. A modeling framework was constructed to predict chemoradiation ...

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...

Uprightvision: A Deep-Learning Toolkit for Transforming Supine Anatomy to Upright

Authors: Ming Dong, Carri K. Glide-Hurst, Behzad Hejrati, Joshua Pan, Yuhao Yan

Affiliation: Department of Computer Science, Wayne State University, Departments of Human Oncology and Medical Physics, University of Wisconsin-Madison, Department of Human Oncology, University of Wisconsin-Madison

Abstract Preview: Purpose: Upright patient positioners and vertical CT reduce tumor motion and stabilize internal anatomy during treatment delivery. Yet, to fully exploit the advantages of upright, translation of stand...

Weakly Supervised Spatial Implicit Neural Representation Learning for 3D MRI-Ultrasound Deformable Image Registration in HDR Prostate Brachytherapy

Authors: Michael Baine, Yang Lei, Yu Lei, Ruirui Liu, Tian Liu, Jing Wang

Affiliation: Icahn School of Medicine at Mount Sinai, University of Nebraska Medical Center

Abstract Preview: Purpose: Accurate 3D deformable registration of MRI and ultrasound (US) is essential for real-time image guidance during high-dose-rate (HDR) prostate brachytherapy. However, MRI-US registration of th...