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Results for "enhanced weighted": 31 found

3D Topological Features for Outcome Assessment of Therapeutic Responses to Neoadjuvant Chemoradiotherapy (NCRT) with and without Anti-CD40 Immunotherapy in Local Advanced Rectal Cancer (LARC)

Authors: Todd A Aguilera, Gaurav Khatri, Jiaqi Liu, Hao Peng, Nina N. Sanford, Robert Timmerman, Haozhao Zhang

Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, 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:
This study first integrates 3D topological data analysis with radiomics from local advanced rectal cancer T2-weighted MRI to evaluate therapeutic responses and quantify treatment-induced c...

4D CBCT Reconstruction Using Denoising Diffusion Implicit Models

Authors: Weixing Cai, Laura I. Cervino, Yabo Fu, Bohong Huang, Licheng Kuo, Tianfang Li, Xiang Li, Jean M. Moran, Huiqiao Xie, Hao Zhang

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

Abstract Preview: Purpose: Four-dimensional cone-beam computed tomography (4D-CBCT) is critical in image-guided radiotherapy (IGRT) for visualizing tumor motion. However, sparse projection sampling often introduces sev...

A Method for Automatic Working Angle Prediction during Intracranial Aneurysms Embolization

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

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

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

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

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

Affiliation: SuperAccuracy Science & Technology Co. Ltd.

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

A 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 ...

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

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

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

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

AI-Based Registration-Free 3T T2-Weighted MRI Synthesis Using Truefisp MRI Acquired on a 0.35T MR-Linac System

Authors: Hilary P Bagshaw, Mark K Buyyounouski, Cynthia Fu-Yu Chuang, Yu Gao, Dimitre Hristov, Lianli Liu, Lawrie Skinner, Lei Xing

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

Abstract Preview: Purpose:
MR-guided radiation therapy has introduced a significant leap in cancer treatment by allowing adaptive treatment. The low-field MR-guided system predominantly uses the TrueFISP sequence, w...

AI-Driven Drug Discovery through an Interactive Analysis of Radiomics and Biological Insights in Glioblastoma

Authors: Nobuki Imano, Yuzuha Kadooka, Daisuke Kawahara, Misato Kishi, Yuji Murakami, Shumpei Onishi

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

Abstract Preview: Purpose: Radiomics has proven useful in predicting overall survival in glioblastoma (GBM) patients, but consistent molecular correlations remain unidentified, leaving its biological basis unclear. Thi...

Assessment of Image Quality and Dose in Dual-Energy X-Ray Imaging for Lung Tumor Tracking

Authors: Nawal Alqethami, Felix Ginzinger, Prasannakumar Palaniappan, Marco Riboldi, Philipp Steininger, Wentao Xie

Affiliation: Research & Development, medPhoton GmbH, Department of Medical Physics, Ludwig-Maximilians-Universität (LMU) München, Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich)

Abstract Preview: Purpose:
This study evaluates the image quality of Dual-Energy (DE) X-ray imaging for lung tumor tracking across various sizes and protocols, comparing its imaging dose to Single-Energy (SE) imagin...

Auto-Contouring of OAR Enhances Patient Safety and Workflow in Gamma Knife Stereotactic Radiosurgery

Authors: Sven Ferguson, S. Murty Goddu, Ana Heermann, Taeho Kim, Nels C. Knutson, Hugh HC Lee, Shanti Marasini, Timothy Mitchell, Seungjong Oh, Kevin Renick

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

Abstract Preview: Purpose: In the Gamma Knife stereotactic radiosurgery (GK-SRS), the delineation of organs-at-risks (OARs) was not fully automated. Due to the cumbersome nature of manual OAR contouring, dose evaluatio...

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

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

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

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

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...

Cerebellar Mutism Syndrome Prediction with 3D Residual Convolutional Neural Network

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

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

Abstract Preview: Purpose: Cerebellar mutism syndrome (CMS) is the most frequently observed complication in children undergoing surgical resection of posterior fossa tumors. Previous work explored lesion to symptom map...

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 ...

Deep-Learning Based Spectral Artifact Removal with In Vivo 7T Proton MRSI Data

Authors: Anke Henning, Mahrshi Jani, Tianyu Wang, Andrew Wright, Xinyu Zhang

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

Abstract Preview: Purpose: Proton MRSI offers critical metabolic insights into diseased brain processes but is prone to artifacts, and current post-processing methods are often insufficient, resulting in low-quality da...

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

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

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

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

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...

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

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

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

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

Enhancing CNN-Based Brain Metastasis Detection in MRI By Integrating Locoregional 3D Deformation Technique

Authors: Minbin Chen, Ke Lu, Kaizhong Shi, Chunhao Wang, Chuan Wu, Zhenyu Yang, Fang-Fang Yin, Jingtong Zhao

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

Abstract Preview: Purpose: MRI-based automatic detection of brain metastases is often challenged by the small size and subtle nature of metastases. This study aimed to develop a novel deep learning-based brain metastas...

Enhancing T2-Weighted Brain MRI Resolution across Orientations Using AI-Based Volumetric Reconstruction

Authors: Mengqi Shen, Meghna Trivedi, Tony J.C. Wang, Andy (Yuanguang) Xu, Yading Yuan

Affiliation: Columbia University Medical Center, Dept of Med Hematology & Oncology, Data Science Institute at Columbia University, Columbia University Irving Medical Center, Department of Radiation Oncology, Columbia University Irving Medical Center

Abstract Preview: Purpose: T2-weighted (T2w) images are critical for identifying pathological changes due to their superior contrast in differentiating tissue types. However, they often lack detailed anatomical resolut...

Enhancing Urethral Visualization for Prostate SBRT Using Post-Void T2-Weighted Imaging on a Low-Field 0.35T MR-Linac System

Authors: Nebi Demez, Michael Kasper, Noufal Manthala Padannayil, Shyam Pokharel, Suresh Rana, Lauren A. Rigsby, Tino Romaguera, Nishan Shrestha, Somol Sunny

Affiliation: Lynn Cancer Institute, Boca Raton Regional Hospital, Baptist Health South Florida

Abstract Preview: Purpose: Accurate delineation of the urethra is critical for optimizing tumor control and minimizing urethral toxicity in prostate stereotactic body radiation therapy (SBRT). The purpose of this study...

Generalizable 7T T1 Map Synthesis from 1.5T and 3T T1W MRI for High-Resolution MRI-Guided Radiation Therapy

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

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

Abstract Preview: Purpose: MRI-guided radiation therapy (MRgRT) benefits significantly from enhanced soft-tissue contrast and spatial resolution, which aid in accurately delineating tumors and organs at risk. Although ...

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

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

Affiliation: Icahn School of Medicine at Mount Sinai

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

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

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

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

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

Investigating the Multimodal Fusion Techniques to Improve Prediction Accuracy of Biochemical Recurrence of Prostate Cancer

Authors: Clint Bahler, Ruchika Reddy Chimmula, Harrison Louis Love, Oluwaseyi Oderinde, Courtney Yong

Affiliation: Purdue University, Department of Urology, Indiana University School of Medicine, Advanced Molecular Imaging in Radiotherapy (AdMIRe) Research Laboratory, School of Health Sciences, Purdue University

Abstract Preview: Purpose: Prostate cancer (PCa) is a common malignancy in men, and predicting biochemical recurrence (BCR) is crucial for guiding treatment decisions. Integrating multimodal data, including clinical, i...

Neural Network Based Differentiable Optimization for Volumetric Modulated Arc Therapy (VMAT)

Authors: Peng Dong, Lei Xing

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

Abstract Preview: Purpose: Volumetric Modulated Arc Therapy (VMAT) optimization is a complex, non-convex problem with numerous variables and intricate constraints. Traditional optimization methods often lack efficiency...

Optimization of Impulsed Acquisition Protocols on 1.5T MRI Using Simulation-Based Bayesian Experimental Design for Cell Size Imaging

Authors: Yan Dai, Jie Deng, Xun Jia, Wen Li, Junzhong Xu

Affiliation: Johns Hopkins University, Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Department of Radiology, Vanderbilt University Medical Center, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center

Abstract Preview: Purpose: Cell size is a vital parameter in evaluating the tumor microenvironment, including cell apoptosis and radiotherapy(RT)-induced immune cell infiltration. The IMPULSED(Imaging Microstructural P...

Standardized MRI-CT Hybrid Workflow for High-Dose-Rate Image-Guided Adaptive Brachytherapy in Cervical Cancer: Aapm TG-303 Implementation

Authors: Kim Creach, Kim Howard, Julius G. Ojwang, Richard A. Shaw, Neelu Soni

Affiliation: Mercy Hospital Springfield

Abstract Preview: Purpose: To present a standardized MRI-CT hybrid workflow for High-Dose-Rate (HDR) Image-Guided Adaptive Brachytherapy (IGBT) in cervical cancer, aligned with AAPM TG-303, as a model to assist with im...

To Investigate the Utility of Magnetic Resonance Imaging (MRI)-Based Radiomics for Predicting Tumor Response and Adverse Effects, Specifically Gastrointestinal (GI) Toxicity, in Cervical Cancer Patients Undergone Radiotherapy.

Authors: Issam M. El Naqa, Kurukulasuriya Ruwani Fernando, Himani Himani, Vivek Kumar, Arun Oinam, Manju Sharma

Affiliation: Panjab University, Moffitt Cancer Center, H. Lee Moffitt Cancer Center, Post Graduate Institute of Medical Sciences, University of California San Francisco

Abstract Preview: Purpose: To investigate the utility of Magnetic Resonance Imaging (MRI)-based radiomics for predicting tumor response and adverse effects, specifically gastrointestinal (GI) toxicity, in cervical canc...

Tumor Density on Time-Weighted Mid-Position Images for Thoracic Tumors

Authors: Katrinus Keijnemans, Eric S. Paulson, An Tai

Affiliation: Department of Radiotherapy, University Medical Center Utrecht, Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose: Time-weighted mid-position images (MidPs), derived from four-dimensional computed tomography (4DCT) via deformable image registration (DIR), are frequently selected for treatment planning due...

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

Authors: Yunxiang Li, Weiguo Lu, Xiaoxue Qian, Hua-Chieh Shao, You Zhang

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

Abstract Preview: Purpose:
Curating high-quality, labeled data for medical image segmentation can be challenging and costly, considering the existence of various image domains with differing modalities/protocols. Cr...