Search Submissions 🔎

Results for "specific deep": 75 found

3D Image Quality Evaluation of a New CT Scanner Employing 3D Landmark Scans, Super Resolution Reconstruction, and Ag Beam Filtration

Authors: Ishika Bhaumik, John M. Boone, Michael T Corwin, Eric S Diaz, Ahmadreza Ghasemiesfe, Andrew M. Hernandez, Sarah E. McKenney, Misagh Piran, Ali Uneri, Eric L White

Affiliation: UC Davis, UC Davis Health, University of California, Johns Hopkins Univ

Abstract Preview: Purpose: A new model CT scanner (Canon Aquilion One Insight) was recently installed at our institution, and it included a 3D Landmark (3DLM) scan for automatic scan planning, a new deep learning recon...

A Deep Learning Approach to the Prediction of Gamma Passing Rates in VMAT Radiotherapy Plans for Adaptive Treatment.

Authors: Jenghwa Chang, Kuan Huang, Lyu Huang, Jason Lima, Jian Liu, Farzin Motamedi

Affiliation: Northwell, Department of Computer Science and Technology, Kean University, Physics and Astronomy, Hofstra University, Hofstra University Medical Physics Program

Abstract Preview: Title: A Deep Learning Approach to the Prediction of Gamma Passing Rates in VMAT Radiotherapy Plans for Adaptive Treatment.
Purpose: This study aims to develop a deep learning algorithm to predict ...

A Deep Learning-Based Approach for Rapid Prediction of IMRT/VMAT Patient-Specific Quality Assurance for Online Adaptive Plans Generated with a 0.35T MR-Linac

Authors: Suman Gautam, Tianjun Ma, William Song

Affiliation: Virginia Commonwealth University

Abstract Preview: Purpose: We propose an artificial intelligence (AI)-based method to rapidly predict the patient-specific quality assurance (PSQA) results for magnetic resonance (MR)-guided online adaptive radiation th...

A Patient-Specific Approach to Surface Guided-DIBH SBRT Candidacy Using Surface Deformation Maps

Authors: Savannah Decker, Grace Gwe-Ya Kim, Laura Padilla

Affiliation: UC San Diego, University of California San Diego

Abstract Preview: Purpose: The success of surface-guided deep-inspiration breath-hold (SG-DIBH) treatments depends on accurate identification of suitable candidates. Sub-optimal patient selection can result in prolonge...

A Pilot Study to Implement a Definitive Breast SBRT Technique

Authors: Charmainne Cruje, Maria Dumol, Nawroz Fatima, Marisa Finlay, Kalaina Johnson, Raman Mohla, Jasleen Uppal

Affiliation: Trillium Health Partners, Carlo Fidani Regional Cancer Centre

Abstract Preview: Purpose: To evaluate the robustness of a breast SBRT protocol in achieving target coverage by utilizing online- and retrospectively-matched CBCT-to-CT images.
Methods: The first pilot patient was s...

Advanced Skeletal Models for Icrp Reference Pediatric Individuals: Development and Dosimetric Implications

Authors: Wesley E. Bolch, Chansoo Choi, Robert Joseph Dawson, Bangho Shin, Yitian Wang

Affiliation: University of Florida

Abstract Preview: Purpose: The skeleton plays a critical role in dosimetry, containing red bone marrow (RBM) and bone endosteum (BE), which are closely linked to radiation-induced leukemia and bone cancer. While severa...

An Advanced Automated Pipeline for Brain Tumor Segmentation on MRI Images in Gamma Knife Radiotherapy

Authors: Zachery Colbert, Matthew Foote, Michael Huo, Mark Pinkham, Prabhakar Ramachandran, Mihir Shanker

Affiliation: Radiation Oncology, Princess Alexandra Hospital, Ipswich Road, Princess Alexandra Hospital

Abstract Preview: Purpose: The study aimed to develop and implement deep learning-based autosegmentation models for the autosegmentation of four key tumor types: brain metastasis, pituitary adenoma, vestibular schwanno...

An Image Representation of Radiomics Data for Enhanced Deep Radiomics Analysis with Consideration of Feature Interactions

Authors: Xiaolong Fu, Runping Hou, Md Tauhidul Islam, Lei Xing

Affiliation: Department of Radiation Oncology, Stanford University, Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine

Abstract Preview: Purpose: To introduce a novel schematic image representation of radiomics data, called OmicsMap, for high-performance deep radiomics analysis. OmicsMap transforms tabular radiomics data into an image ...

Automated IMPT Treatment Planning for CSI: Enhancing Efficiency with Auto-Segmentation and Scripting

Authors: Katja M. Langen, William Andrew LePain, Robert Muiruri, Vivi Nguyen, Mosa Pasha, Roelf L. Slopsema, Alexander Stanforth, Yinan Wang, Mingyao Zhu

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

Abstract Preview: Purpose: Intensity modulated proton therapy (IMPT) treatment planning for craniospinal irradiation (CSI) is complex and requires extensive effort from the planner. This study aims to enhance planning ...

Automatic Contour Quality Assurance Using Deep-Learning Based Contours

Authors: Laurence Edward Court, Raphael Douglas, David Fuentes, Anuja Jhingran, Barbara Marquez, Raymond Mumme, Christine Peterson, Julianne M. Pollard-Larkin, Surendra Prajapati, Dong Joo Rhee, Thomas J. Whitaker

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

Abstract Preview: Purpose: Safe deployment of auto-contouring models requires the inclusion of automated quality assurance (QA). One approach is to use an independent auto-contouring model and compare the contours geom...

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

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

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

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

Biologically Guided Deep Learning for MRI-Based Brain Metastasis Outcome Prediction after Stereotactic Radiosurgery

Authors: Evan Calabrese, Hangjie Ji, Kyle J. Lafata, Casey Y. Lee, Eugene Vaios, Chunhao Wang, Lana Wang, Zhenyu Yang, Jingtong Zhao

Affiliation: Duke University, Department of Radiation Oncology, Duke University, Duke Kunshan University, North Carolina State University

Abstract Preview: Purpose: To develop a biologically guided deep learning (DL) model for predicting brain metastasis(BM) local control outcomes following stereotactic radiosurgery (SRS). By integrating pre-SRS MR image...

Biomechanically Guided Deep Learning for Deformable Multimodality Liver Registration Framework

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

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

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

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

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

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

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

Clinical Validation of a Deep-Learning Segmentation Tool for Head and Neck Cancer Patients and Thoracic and Abdominal Cancer Patients

Authors: Haijian Chen, Katja M. Langen, William Andrew LePain, Claire Tran, Mingyao Zhu

Affiliation: Emory Healthcare, Emory University, Georgia Institute of Technology

Abstract Preview: Purpose: To validate the performance of a commercial deep-learning segmentation (DLS) tool for head and neck cancer (HNC) and thoracic and abdominal cancer (TAC) by comparing it to manual segmentation...

Comparative Analysis of Quantum-Classical Hybrid and Traditional Deep Learning Approaches for Chest X-Ray Image Classification

Authors: Ji Hye Han, Yookyung Kim, Jang-Hoon Oh, Heesoon Sheen, Han-Back Shin

Affiliation: Ewha Womans university, Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, High-Energy Physics Center, Chung-Ang Universit, Ewha Womans University, Kyung Hee University Hospital

Abstract Preview: Purpose: Chest X-rays are critical for diagnosing conditions such as pneumonia, tuberculosis, and COVID-19. Although deep learning (DL) approaches, especially convolutional neural networks, have signi...

Deep Dive of Clinical 5DCT Patient Results

Authors: Ryan Andosca, 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, UCLA, University of California, Los Angeles, UCLA Radiation Oncology

Abstract Preview: Purpose: To demonstrate the performance of the model-based CT scanning protocol, 5DCT, as an alternative to 4DCT.
Methods: 5DCT imaging results for 242 patients were analyzed. Implementation of the...

Deep Learning Aided Oropharyngeal Cancer Autoplanning

Authors: Mark Bowers, Gabriel Carrizo, Jimmy Caudell, Vladimir Feygelman, Kevin Greco, Christian Hahn, Jihye Koo, Kujtim Latifi, Fredrik Lofman, Jacopo Parvizi, Muqeem Qayyum, Caleb Sawyer

Affiliation: RaySearch Laboratories, Moffitt Cancer Center

Abstract Preview: Purpose: Head and neck (H&N) radiotherapy planning is complex, with multiple competing objectives. We endeavored to improve efficiency of planning by developing a deep learning (DL) model trained to p...

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

Authors: Gayoung Kim, Junghoon Lee

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

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

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

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

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

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

Deep Learning-Based Eye Monitoring and Tracking System for Ocular Proton Therapy in a Regular Gantry Room

Authors: David H. Abramson, Christopher Barker, Jasmine H. Francis, Meng Wei Ho, Yen-Po Lee, Haibo Lin, Hang Qi, Andy Shim, Charles B. Simone, Weihong Sun, Xiaoxuan Xu, Siyu Yang, Francis Yu, Anna Zhai

Affiliation: College of Machine Intelligence, Nankai University, New York Proton Center, Department of Biomedical Engineering, Johns Hopkins University, Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Proton therapy is an effective modality for treating ocular tumors such as uveal melanoma. We developed a novel camera‐based eye‐tracking system to provide real-time noninvasive eye positioni...

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-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...

Detector Physics-Incorporated Diffusion Denoising Models for Digital Breast Tomosynthesis with Dual-Layer Flat Panel Detectors

Authors: Alexander Bookbinder, Matthew Tivnan, Xiangyi Wu, Wei Zhao

Affiliation: Stony Brook Medicine, Massachusetts General Hospital

Abstract Preview: Purpose: To investigate and benchmark a system-adaptive diffusion-based digital breast tomosynthesis (DBT) denoising model for a direct-indirect dual-layer flat panel detector (DI-DLFPD) with a k-edge...

Determination of Optimal Surface Rois for Sgrt-Guided Dibh Treatment of Abdominal Cancer

Authors: Megan E. Daly, Ajay Fernandez, Ryan D. Hernandez, Soo Kyoung Kim, Arta Monjazeb, Minahal Naveed, Peter C. Park, Jotsna Singh, Payton H. Stone

Affiliation: UC Davis Cancer Center

Abstract Preview: Purpose: To identify optimal deep-inspiration breath-hold (DIBH) surface-guided radiotherapy (SGRT) regions of interest (ROIs) for abdominal stereotactic body radiation therapy (SBRT) treatments.
M...

Developing an AI-Driven Predictor for Forecasting Treatment Outcomes in Patients with Early-Stage Breast Cancer

Authors: Lucy Jiang, Chengyu Shi

Affiliation: Department of Radiation Oncology, City of Hope Orange County, Amity Regional High School (10th Grade)

Abstract Preview: Purpose: Early-stage breast cancer is common among females, with typically high local tumor control rates (LCR). Brachytherapy is a common way to achieve LCR following surgery. However, the patients m...

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...

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...

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....

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 São Paulo (USP), Institute of Physics, Universidade Estadual de Campinas. Instituto de Física 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...

Foresight Planning: Radiotherapy Plan Optimization Via Self-Supervised Model Predictive Control

Authors: Yang Sheng, Qingrong Jackie Wu, Qiuwen Wu, Xin Wu, Dongrong Yang

Affiliation: Duke University Medical Center

Abstract Preview: Purpose:
Treatment planning for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) relies on inverse planning, an iterative and non-intuitive process of adjust...

From Prediction to Practice: Performance of a Deep Learning-Based Breast Planning Algorithm

Authors: Thomas L. Hayes, Nicholas C. Koch, Han Liu, Qingyang (Grace) Shang, Benjamin J. Sintay, Caroline Vanderstraeten, David B. Wiant

Affiliation: Fuse Oncology, Cone Health, Cone Health Cancer Center

Abstract Preview: Purpose:
This study evaluates the accuracy of a deep learning-based automatic breast planning script in predicting beam energy for breast cancer treatments. The script was validated and implemented...

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...

Human-like Deep Learning-Based Whole-Brain Radiotherapy Treatment Planning

Authors: Adnan Jafar, Xun Jia, An Qin

Affiliation: Johns Hopkins University

Abstract Preview: Purpose: 3D whole-brain radiotherapy (WBRT) is widely used due to its simplicity and effectiveness. While modern treatment planning systems, like RayStation, offer automated Field-in-Field planning, p...

Hybrid Prior-Enhanced Deep Image Prior (HPEDIP) Image Reconstruction for Ultra-Short Scans

Authors: Renee Farrell, Jinkoo Kim, Xin Qian, Ziyu Shu, Zhaozheng Yin, Tiezhi Zhang

Affiliation: Stony Brook Medicine, Washington University in St. Louis, Stony Brook University, Stony Brook University Hospital

Abstract Preview: Purpose: Ultra-short CT scan allows fast imaging speed, dose reduction, and compact system design. We developed a deep image prior (DIP) based reconstruction method named Hybrid Prior-Enhanced Deep Im...

Imaging of Mn Washout in the Individual Welder Brain after Wearing Powered Air-Purifying Respirators (PAPRs)

Authors: Ulrike Dydak, Chia-Tien Hsu, Chang Geun Lee, Cora Mizimakoski, Humberto Monsivais, Jae Hong Park

Affiliation: Purdue University

Abstract Preview: Purpose: Overexposure to manganese (Mn) from inhaling welding fumes can lead to cognitive and motor deficits. We developed a whole-brain Mn-mapping approach to detect subtle but significant increases ...

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...

Incorporating Physicians’ Contouring Style into Auto-Segmentation of Clinical Target Volume for Post-Operative Prostate Cancer Radiotherapy Using a Language Encoder

Authors: Steve B. Jiang, Chien-Yi Liao, Dan Nguyen, Daniel Yang, Hengrui Zhao

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

Abstract Preview: Purpose:
Post-operative radiotherapy for prostate cancer requires precise contouring of the clinical target volume (CTV) to account for microscopic disease that is invisible in the image. However, ...

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 Foundation Model with Self-Supervised Learning for Brain Lesion Segmentation with Multimodal and Diverse MRI Datasets

Authors: Zong Fan, Fan Lam, Hua Li, Rita Huan-Ting Peng, Yuan Yang

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

Abstract Preview: Purpose: Accurate lesion segmentation in MRI is critical for early diagnosis, treatment planning, and monitoring disease progression in various neurological disorders. Cross-site MRI data can alleviat...

Inter-Patient Registration Methods for Voxel-Based Analysis in Lung Cancer

Authors: Chloe Min Seo Choi, Joseph O. Deasy, Jue Jiang, Sudharsan Madhavan, Nikhil Mankuzhy, Nishant Nadkarni, Andreas Rimner, Maria Thor, Harini Veeraraghavan, Abraham Wu

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

Abstract Preview: Purpose: Voxel-based analysis (VBA) requires accurate topology-preserving inter-patient deformable image registration (DIR). This study assessed whether guiding a DIR method with geometric priors of t...

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...

Investigate Deep-Learned MRI Reconstruction with Data Consistency Mechanism and Task-Informed Loss

Authors: Mark Anastasio, Hua Li, Zhuchen Shao

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

Abstract Preview: Purpose: Ill-conditioned reconstruction problems in medical imaging, such as those arising from undersampled k-space data in MRI, can result in degraded image quality and clinical task-orientated perf...

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...

Knowledge-Informed Deep Learning for Accurate and Interpretable Extracapsular Extension Detection in Head and Neck Squamous Cell Carcinoma

Authors: William N. Duggar, Amirhossein Eskorouchi, Haifeng Wang

Affiliation: Mississippi State University, University of Mississippi Medical Center

Abstract Preview: Purpose:
Extracapsular extension (ECE) in lymph nodes represents a critical prognostic factor in head and neck squamous cell carcinoma (HNSCC), bearing important implications for staging, treatment...

Medical Data Handler: A Research-Oriented Graphical User Interface for Dicom Processing, Image Analysis, and Data Management

Authors: Andrew R. Godley, Steve B. Jiang, Mu-Han Lin, Austen Matthew Maniscalco, Dan Nguyen, Yang Kyun Park

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

Abstract Preview: Purpose:
Preparing DICOM datasets for research and education is challenging due to the complexity of the format and the necessity for patient-specific handling. Existing workflows demand substantia...

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

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

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

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

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

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

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

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

Neural Implicit K-Space for Accelerated Patient-Specific Non-Cartesian MRI Reconstruction

Authors: Daniel O Connor, Mary Feng, Hui Lin, Hengjie Liu, Xin Miao, Michael Ohliger, Jess E. Scholey, Ke Sheng, DI Xu, Wensha Yang, Yang Yang

Affiliation: UCSF, University of California, Los Angeles, Department of Radiation Oncology, University of California San Francisco, Department of Radiation Oncology, University of California, San Francisco, Department of Radiation Oncology, University of California at San Francisco, University of San Francisco, Department of Radiology, University of California, San Francisco, University of California San Francisco, Siemens Medical Solutions USA Inc.

Abstract Preview: Purpose: The scanning time for a fully sampled MRI is lengthy. Compressed sensing (CS) has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is...

Optimizing Dose Coverage and Mitigating Electron Return Effects and Electron Stream Effects in MR-Linac Treatment of Extremity Malignancies: The Impact of Bolus and Gel Use

Authors: Kin Yin Cheung, Chen-Yu Huang, Chi Wah Kong, Ka Ki Lau, Pei-Xiong Li, Darren M. C. Poon, Bin Yang, Siu Ki Yu, Jing Yuan, Chi To Yung, Shang Peng Felix Yung

Affiliation: Comprehensive Oncology Centre, Hong Kong Sanatorium and Hospital, Medical Physics Department, Hong Kong Sanatorium and Hospital, Research Department, Hong Kong Sanatorium and Hospital

Abstract Preview: Purpose: This study investigates optimal treatment strategies utilizing MR-linac for extremity lesions, with a specific focus on a 0.6 cm deep lesion in the right palm. The magnetic field-induced dose...

Patient-Specific Bio-Morphological Features in Cherenkov Imaging for Positioning Verification: A Retrospective Analysis in Accelerated Partial Breast Irradiation (aPBI) VMAT Radiotherapy

Authors: Yao Chen, Lesley A Jarvis, Allison Matous, Rongxiao Zhang

Affiliation: Dartmouth College, University of Missouri, Dartmouth Cancer Center, Dartmouth Health

Abstract Preview: Purpose: Precise patient positioning is critical in accelerated partial breast irradiation (aPBI) to ensure accurate dose delivery to the tumor bed while minimizing exposure to surrounding healthy tis...

Patient-Specific Coronary Artery Habitat Model for Enhanced Cardiac Sparing

Authors: Blessing Akinro, Soumyanil Banerjee, Ming Dong, Carri K. Glide-Hurst, Prashant Nagpal, Chase Ruff, Nicholas R. Summerfield, Timothy P. Szczykutowicz

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

Abstract Preview: Purpose: Radiation dose to coronary arteries (CAs) during thoracic radiotherapy (RT) is linked to cardiotoxicity. However, precise CA delineation for avoidance is limited by image quality and CA compl...

Patient-Specific Deep Reinforcement Learning Framework for Automatic Replanning in Proton Therapy for Head-and-Neck Cancer

Authors: Malvern Madondo, Mark McDonald, Zhen Tian, Christopher Valdes, Ralph Weichselbaum, Xiaofeng Yang, David Yu, Jun Zhou

Affiliation: Department of Radiation & Cellular Oncology, University of Chicago, University of Chicago, Emory University, Department of Radiology, University of Chicago, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose: Head-and-neck (HN) cancer patients often experience significant anatomical changes during treatment course. Proton therapy, particularly intensity-modulated proton therapy (IMPT), is sensitiv...

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...

Patient-Specific Orthogonal Projection Based Real-Time Volumetric X-Ray Imaging for Proton Therapy

Authors: Hao Chen, Kai Ding, Xiaoyu Hu, Xun Jia, Heng Li, Devin Miles

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

Abstract Preview: Purpose: Accurately delivering radiation dose is critical in intensity-modulated proton therapy (IMPT), where intrafraction motion management plays a pivotal role. Our proton therapy system equipped x...

Patient-Specific Treatment Plan Optimization through Intentional Deep Overfit Learning As a Warm Start for Longitudinal Adaptive Radiotherapy

Authors: Wouter Crijns, Frederik Maes, Loes Vandenbroucke, Liesbeth Vandewinckele

Affiliation: Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven; Department of Radiation Oncology, UZ Leuven, Department ESAT/PSI, KU Leuven; Medical Imaging Research Center, UZ Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven

Abstract Preview: Purpose: To explore intentional deep overfit learning (IDOL) to exploit the initial treatment plan to predict an adaptive radiotherapy plan.
Methods: A conditional generative adversarial network is...

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 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 Imaging and Spatial Transcriptomics Reveal Compartment-Specific Immune Dynamics in HPV+ Oropharyngeal Cancer

Authors: Casey C. Heirman, Kyle J. Lafata, Xiang Li, Breylon Riley, Jack B Stevens, Tammara Watts

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

Abstract Preview: Purpose: To leverage quantitative fluorescence imaging and spatial transcriptomics for characterizing the spatial and molecular heterogeneity of the tumor microenvironment (TME) in HPV+ head and neck ...

Reliable Markerless Lung Tumor Tracking with Built-in Patient-Specific Quality Assurance

Authors: Weixing Cai, Laura I. Cervino, Qiyong Fan, Yabo Fu, Tianfang Li, Xiang Li, Jean M. Moran, Hai Pham, Pengpeng Zhang

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

Abstract Preview: Purpose: AAPM Task Group Report 273 emphasizes the importance of rigorous validation to ensure the generalizability and robustness of machine learning-based clinical tools before their implementation ...

SPECT/CT Multimodal Segmentation of Bone Marrow for Theranostic Dosimetry

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

Affiliation: UCLA, David Geffen School of Medicine at UCLA

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

Scoring Functions for Reinforcement Learning in Accelerated Partial Breast Irradiation Treatment Planning

Authors: Rafe A. McBeth, Kuancheng Wang, Ledi Wang

Affiliation: Department of Radiation Oncology, University of Pennsylvania, Georgia Institute of Technology, University of Pennsylvania

Abstract Preview: Purpose:
The integration of AI in clinical workflows presents unprecedented opportunities to enhance treatment quality in radiation oncology, yet it also demands innovative approaches to address th...

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

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

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

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

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...

Task-Specific Deep-Neural-Network Architecture Optimization for CBCT Scatter Correction

Authors: Hoyeon Lee

Affiliation: University of Hong Kong

Abstract Preview: Purpose: Deep-learning approaches are widely investigated for Cone-Beam CT (CBCT) scatter correction to improve the quality of the linear-accelerator mounted CBCT. This study aims to optimize the deep...

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...

Unidose: A Universal Framework for IMRT Dose Prediction

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

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

Abstract Preview: Purpose: Dose prediction (DP) is essential in guiding radiotherapy planning. However, current DP models for intensity-modulated radiation therapy (IMRT) primarily rely on fixed-beam orientations and a...

Unsupervised Task-Specific Histology Image Stain Standardization and Crypt Detection for Evaluating Normal Tissue Flash Irradiation Response

Authors: Muhammad Ramish Ashraf, Kerriann Casey, Suparna Dutt, Jie Fu, Edward Elliot Graves, Xuejun Gu, Hao Jiang, Brianna Caroline Lau, Billy W Loo, Weiguo Lu, Rakesh Manjappa, Stavros Melemenidis, Erinn Bruno Rankin, Lawrie Skinner, Luis Armando Soto, Murat Surucu, Vignesh Viswanathan, Zi Yang, Amy Shu-Jung Yu

Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, 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, Department of Comparative Medicine, Stanford University School of Medicine, Department of Radiation Oncology, Stanford University Cancer Center

Abstract Preview: Purpose: The intestine is a classical preclinical model for studying radiation injury, and histological quantification of intestinal crypts is a key assay for assessing this response. However, substan...

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...

“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...