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Results for "pre trained": 49 found

A Modular Approach to Reversible and Stackable Medical Imaging Translation Models: CBCT-Based Synthetic MRI with Multiple U-Nets in Series (MUNETs)

Authors: Eric Chang, Nguyen Phuong Dang, Andrew Lim, Lauren Lukas, Lijun Ma, Yutaka Natsuaki, Zhengzheng Xu, Hualin Zhang

Affiliation: Radiation Oncology, Keck School of Medicine of USC

Abstract Preview: Purpose: Harnessed the power of AI and Deep Learning (DL), Generalized Neural Network models for medical image transformation are trained to predict target images from reference images, often requirin...

A SAM-Guided and Match-Based Semi-Supervised Segmentation Framework for Medical Imaging

Authors: Weiguo Lu, Jax Luo, Xiaoxue Qian, 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, Harvard Medical School

Abstract Preview: Purpose:
Semi-supervised segmentation leverages sparse annotation information to learn rich representations from combined labeled and label-less data for segmentation tasks. This study leverages th...

A Study of Large Model Alignment Techniques for MRI Images of Small Sample Meningioma

Authors: Xiangli Cui, Man Hu, Wanli Huo, Da Yao, Jianguang Zhang, Yingying Zhang, Shanyang Zhao

Affiliation: Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, the Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University, Departments of Radiation Oncology, Zibo Wanjie Cancer Hospital, Department of Oncology, Xiangya Hospital, Central South University, Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences

Abstract Preview: Purpose:
To study the fine-tuning strategy of pre-trained AI image generation model to adapt to the generation of small sample meningioma MRI images, explore its impact on observer performance, and...

A Vision-Language Deep Learning Model for Predicting Survival Outcomes in Glioblastoma Patients

Authors: Zachary Buchwald, Chih-Wei Chang, Richard L.J. Qiu, Mojtaba Safari, Hui-Kuo Shu, Lisa Sudmeier, Xiaofeng Yang, David Yu, Xiaohan Yuan

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

Abstract Preview: Purpose: This study proposes a novel vision-language model (VLM) to predict survival outcomes in glioblastoma (GBM) patients. By integrating multimodal MRI data and clinical information, the proposed ...

AI-Driven Quality Assurance for Gamma Camera/SPECT Anomaly Detection Using Contrastive Learning

Authors: Shanli Ding, Osama R. Mawlawi, Tinsu Pan

Affiliation: UT MD Anderson Cancer Center

Abstract Preview: Purpose:
Reliable detection of anomalies in Gamma Camera/SPECT flood images is vital for quality assurance (QA). Traditional methods relying on numerical thresholds and manual inspections often mis...

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 Ionizing Radiation Acoustic Imaging: A Deep Learning Approach for Denoising and Quantitative Reconstruction

Authors: Kyle Cuneo, Issam M. El Naqa, Dale W. Litzenberg, Yiming Liu, Xueding Wang, Lise Wei, Wei Zhang, Jiaren Zou

Affiliation: University of Michigan, H. Lee Moffitt Cancer Center

Abstract Preview: Purpose: To quantitatively map 3D dose deposition during radiotherapy, empowering real-time adaptive radiation treatment.

Methods: The research features reconstructing dose deposition from acou...

An FMEA-Based Approach to Improve the Process and Quality Control on MR Imaging from Outside Diagnostic Imaging Centers to be Used for Radiation Treatment Planning

Authors: Olivier Blasi, Eric Cameron, Brad K. Lofton

Affiliation: CAMP, Colorado Assn in Medical Phys (CAMP)

Abstract Preview: Purpose:
Magnetic Resonance (MR) imaging obtained from external centers for radiation therapy (RT) planning can suffer from suboptimal protocols and geometric distortions. These issues can require ...

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

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

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

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

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

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

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

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

Big Data in-Vivo Epid Image Prediction for VMAT Radiotherapy

Authors: Casey E. Bojechko, Lance C Moore

Affiliation: University of California, San Diego, University of California San Diego

Abstract Preview: Purpose: EPID images collected during treatment can serve as an in-vivo error detection mechanism. Previous works have shown that comparing in-vivo EPID images to AI-predicted EPID images for IMRT pla...

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...

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

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

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

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

Deep Learning-Based Multileaf Collimator Sequence Prediction for Automated VMAT Treatment Planning in Pancreatic Cancer

Authors: Zixu Guan, Takahiro Iwai, Takashi Mizowaki, Mitsuhiro Nakamura, Michio Yoshimura

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

Abstract Preview: Purpose:
The goal of this study is to develop a fully automated treatment planning approach for VMAT in pancreatic cancer that can convert patient anatomy into LINAC machine parameters. In this wor...

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

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

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

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

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...

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

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

Affiliation: Icahn School of Medicine at Mount Sinai

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

Diffusion-Based PET Image Enhancement in Bgrt

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

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

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

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

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

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

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

Evaluating Deep Learning Models for Accurate Segmentation of GTV and Oars in MR-Guided Adaptive Radiotherapy for Pancreatic Cancer

Authors: Christopher G. Ainsley, Pradeep Bhetwal, Yingxuan Chen, Wookjin Choi, Vimal K. Desai, Karen E. Mooney, Adam Mueller, Hamidreza Nourzadeh, Yevgeniy Vinogradskiy, Maria Werner-Wasik

Affiliation: Thomas Jefferson University

Abstract Preview: Purpose: MR-guided adaptive radiotherapy (MRgART) has demonstrated improved outcomes for patients with pancreatic cancer. However, the time-consuming re-segmentation of targets and organs-at-risk (OAR...

Evaluating the Impact of Contour Variability on the Effectiveness of Deep Learning Features in Head and Neck Imaging

Authors: Hania A. Al-Hallaq, Xuxin Chen, Anees H. Dhabaan, Elahheh (Ella) Salari, Xiaofeng Yang

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

Abstract Preview: Purpose:
Radiomics image analysis could lead to the development of predictive signatures and personalized radiotherapy treatments. However, variations in delineation are known to affect hand-crafte...

Fast 3D Scintillation Dosimetry Using Single View Deep Learning Reconstruction

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

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

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

Feasibility of Extracting Diagnosis and Staging at Scale from Clinical Notes Via a Real-World Data Warehouse

Authors: Melissa Bronson, Elizabeth L. Covington, Robert T. Dess, Joseph R. Evans, William C. Jackson, Charles S. Mayo, Michelle L. Mierzwa, Benjamin S. Rosen, VG Vinod Vydiswaran, Grant Weyburne, Zheng Zhang, Henry Zocher

Affiliation: PCORnet®, The National Patient-Centered Clinical Research Network, University of Michigan

Abstract Preview: Purpose: Diagnosis and staging are an integral part of cancer care, but this information is often scattered across various electronic medical records. The fragmentation increases overall documentation...

Fine-Tuning AI-Based Generative Models for Small-Sample Glioma MRI Generation.

Authors: Xiangli Cui, Chunyan Fu, Man Hu, Wanli Huo, Jingyu Liu, Jianguang Zhang, Yingying Zhang, Shanyang Zhao

Affiliation: Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, the Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University, Departments of Radiation Oncology, Zibo Wanjie Cancer Hospital, Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Department of Oncology, Xiangya Hospital, Central South University, College of Information Engineering, China Jiliang University, Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences

Abstract Preview: Purpose: To quantify the impact of fine-tuning strategies for pre-trained AI image generation models on glioma MRI image quality and observer performance, and to determine the optimal fine-tuning conf...

Image Quality Enhancement for Transrectal Ultrasound Imaging of Prostate Brachytherapy Using Deep Learning: A Needle Eraser

Authors: Hilary P Bagshaw, Mark K Buyyounouski, Serdar Charyyev, Xianjin Dai, PhD, Yu Gao, Thomas R. Niedermayr, Lei Xing

Affiliation: Department of Radiation Oncology, Stanford University

Abstract Preview: Purpose: Real-time transrectal ultrasound imaging is the gold standard for needle placement and treatment planning of real-time based-ultrasound-based high dose-rate (HDR) prostate brachytherapy. Cumu...

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

Authors: Marissa Brown, Geoffrey D. Clarke, Luke Norton

Affiliation: University of Texas Health Science Center at San Antonio

Abstract Preview: Purpose: To evaluate how different learning strategies affect convolutional neural network (CNN) estimates of the liver's intravoxel incoherent motion (IVIM) parameters.
Methods: A 3-stage U-Net wa...

Improving 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...

Innovative Deep Learning Network for Overall Survival Prediction for NSCLC: Outperforming Pre-Trained Models VGG16 and ResNet50

Authors: Ryan Alden, Tithi Biswas, Kaushik Halder, Felix Maria-Joseph, Michael Mix, Rihan Podder, Tarun Kanti Podder

Affiliation: SUNY Upstate Medical University, IIT-Roorkee, University of Florida

Abstract Preview: Purpose: Early-stage NSCLC patients undergoing SBRT often die due to intercurrent illnesses. However, prediction of overall survival (OS) remains crucial due to the risk of disease recurrence. This st...

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...

Knowledge-Based Online Adaptive Proton Stereotactic Ablative Radiotherapy (SABR) for Localized Prostate Cancer Using Gaussian Process Regression

Authors: Hania A. Al-Hallaq, Duncan Henry Bohannon, Chih-Wei Chang, Anees H. Dhabaan, Vishal Dhere, H Scott McGinnis, Pretesh Patel, Sagar Patel, Keyur Shah, Xiaofeng Yang, Jun Zhou

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

Abstract Preview: Purpose: Two-fraction proton SABR is an attractive alternative to brachytherapy for localized prostate cancer. However, potential interfractional anatomical changes necessitate online adaptation, espe...

Knowledge-Based Planning in Proton Therapy: Validation and Model Size Considerations with Limited Data

Authors: Gregory A Azzam, Michael Butkus, Nesrin Dogan, Robert Kaderka, Nhan Vu, Yihang Xu

Affiliation: University of Miami, Department of Radiation Oncology, University of Miami, University of Miami Sylvester Comprehensive Cancer Center, University of Miami, Sylvester Comprehensive Cancer Center

Abstract Preview: Purpose: Knowledge-based planning (KBP) has demonstrated potential for improving planning quality and efficiency. Adoption of KBP in particle therapy has been slow due to limited number of plans to tr...

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

Authors: Mark Anastasio, Hua Li, Zhuchen Shao

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

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

MRI Radiomics-Based Machine Learning Model for Predicting BNCT Treatment Response in Glioblastoma

Authors: Huang Chi-Shiuan, Wu Chih-Chun, Hui-Yu Cathy Tsai, Chen Yan-Han, Chen Yi-Wei, Pan Yi-Ying

Affiliation: Institute of Nuclear Engineering and Science, National Tsing Hua University, Taipei Veterans General Hospital, Tri-Service General Hospital

Abstract Preview: Purpose:
This study aims to develop and validate a machine learning (ML) model based on MRI-derived radiomic features to predict progressive disease (PD) in glioblastoma (GBM) patients four months ...

Mitigating Data-Driven Uncertainty in Machine Learning-Based Radiotherapy Outcome Prediction

Authors: Ali Ajdari, Alice Bondi, Thomas R. Bortfeld, Gregory Buti, Xinru Chen, Zhongxing Liao, Antony John Lomax, Ting Xu

Affiliation: The University of Texas MD Anderson Cancer Center, Department Of Radiation Oncology, Massachusetts General Hospital (MGH), Massachusetts General Hospital & Harvard Medical School, Paul Scherrer Institut, ETH Zurich

Abstract Preview: Title: Addressing Imaging and Biomarker-driven Uncertainty in Machine Learning-based Radiotherapy Outcome Prediction
Alice Bondi, Gregory Buti, Antony Lomax, Thomas Bortfeld, Xinru Chen, Ting Xu, Z...

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...

PET Imaging and Novel Cardiac Radiomics to Predict Pre-Radiotherapy Cardiac Conditions for Lung Cancer Patients Undergoing Radiotherapy.

Authors: Wookjin Choi, Michael Dichmann, Adam Dicker, Nilanjan Haldar, Yingcui Jia, Nicole L Simone, Eugene Storozynsky, Yevgeniy Vinogradskiy, Maria Werner-Wasik

Affiliation: Thomas Jefferson University, 9Department of Radiation Oncology, Thomas Jefferson University

Abstract Preview: Purpose: Cardiotoxicity remains a significant limitation for lung cancer patients treated with radiotherapy. Pre-radiotherapy cardiac conditions increase the probability of patients developing cardiot...

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...

Prior-Informed Neural Super-Resolution Dosimetry for Radiotherapy QA from Sparse Dosimeter Arrays

Authors: Muhammad Ramish Ashraf, Clinton Gibson, Gregory Szalkowski, Lei Wang, Siqi Wang, Lei Xing

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

Abstract Preview: Purpose: To develop a neural network-based super-resolution framework for enhancing the resolution of sparse dosimetry measurements in patient-specific radiotherapy QA. Sparse detector arrays, such as...

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 ...

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

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

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

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

Robustness of Deep Learning-Based Motion Compensated 4D-CBCT Reconstruction to out-of-Distribution Data

Authors: Geoffrey D. Hugo, Eric Laugeman, Thomas R. Mazur, Pamela Samson, Kim A. Selting, Zhehao Zhang

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

Abstract Preview: Purpose: To investigate the robustness of a deep learning (DL)-based 4D-CBCT motion-compensated (MoCo) reconstruction method to out-of-distribution data.
Methods: Our developed 4D-CBCT reconstructi...

Structure-Based Diffusion Model for CT Synthesis from MR Images for Radiotherapy Treatment Planning

Authors: Samuel Kadoury, Redha Touati

Affiliation: Polytechnique Montréal

Abstract Preview: Purpose:
Generating synthetic CT images from MR acquisitions for radiotherapy planning allows to integrate soft tissue contrast alongside density information stemming from CT, thus improving tumor ...

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...

Transformer-Based Proton Dose Prediction with and without Diffusion Process

Authors: Jing Qian, Brandon Reber, David M. Routman, Satomi Shiraishi

Affiliation: Mayo Clinic

Abstract Preview: Purpose: The dose distribution in proton radiotherapy (PRT) is characterized by sharp gradients, posing a challenge for machine learning-based dose prediction. While denoising with diffusion processes...

Using Machine Learning to Predict Esophagitis Risk in Lung Cancer Radiotherapy Based on Clinical and Dosimetric Factors

Authors: Ibtisam Almajnooni, Siyong Kim, Nathaniel Miller, Elisabeth Weiss, Lulin Yuan

Affiliation: Virginia Commonwealth University

Abstract Preview: Purpose: Radiation-induced esophagitis (RE) is a common concern in lung cancer IMRT. Recent studies have indicated that the risk of radiation side effects varies greatly with patients’ baseline clinic...

Weak-to-Strong Generalization for Interpretable Deep Learning-Based Histological Image Classification Guided By Hand-Crafted Features

Authors: Mark Anastasio, Zong Fan, Hua Li, Changjie Lu, Lulu Sun, Xiaowei Wang, Zhimin Wang, Michael Wu

Affiliation: University of Illinois at Urbana-Champaign, University of Illinois at Chicago, Washington University School of Medicine, University of Illinois Urbana-Champaign, Washington University in St. Louis, University Laboratory High School

Abstract Preview: Purpose: Histological whole slide images (WSIs) are vital in clinical diagnosis. Although deep learning (DL) methods have achieved great success in this task, they often lack interpretability. Traditi...

“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...