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Results for "weighted mri": 63 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...

A Retrospective Assessment of Proton Radiation Response in Brain Tumor Patients Using Diffusion-Weighted MR Imaging

Authors: Liu Hong, Wen C. Hsi, Faraz Kalantari, Romy Megahed, Ganesh Narayanasamy, Maida Ranjbar, Pouya Sabouri, Zhong Su

Affiliation: University of Arkansas for Medical Sciences, Department of Radiation Oncology, University of Arkansas for Medical Sciences (UAMS)

Abstract Preview: Purpose: Quantitative apparent diffusion coefficients (ADC) in diffusion-weighted MRI (dMRI) reflect water diffusivity and thus provide tissue cellular density information. Functional diffusion mappin...

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...

Adversarial Diffusion-Based Self-Supervised Learning for High-Resolution MR Imaging

Authors: Zachary Buchwald, Chih-Wei Chang, Zach Eidex, Richard L.J. Qiu, Mojtaba Safari, Shansong Wang, 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: MRI offers excellent soft tissue contrast for diagnosis and treatment but suffers from long acquisition times, causing patient discomfort and motion artifacts. To accelerate MRI, supervised d...

An Efficient Deep Learning Model with Multi-Scale Integration for Automated Pancreas Segmentation on MR Images

Authors: Jingyun Chen, Yading Yuan

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

Abstract Preview: Purpose: To develop and evaluate the Scale-attention network (SANet) for automated pancreas segmentation on MR images.
Methods: To develop SANet, we extended the classic U-Net design with a dynamic...

Application of a Conditional Diffusion Model to Improve Real-Time MR Imaging in Online Adaptive MR-Guided Radiotherapy

Authors: Hideaki Hirashima, Haruo Inokuchi, Nobutaka Mukumoto, Naruki Murahashi, Mitsuhiro Nakamura, Megumi Nakao, Keiko Shibuya, Linna Zhang

Affiliation: Kyoto University, Osaka Metropolitan University

Abstract Preview: Purpose:
To transform the quality of 2D cine MR images acquired during online adaptive MR-guided radiotherapy (OA-MRgRT) by utilizing a conditional diffusion model to achieve image quality comparab...

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...

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

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

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

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

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

Brain Vessel Segmentation and Tracking in Longitudinal Glioblastoma MRI Scans

Authors: Evan Calabrese, Edward Robert Criscuolo, Deshan Yang

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

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

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...

Comprehensive Assessment of a Large Field of View Phantom for Routine MR Image Quality on a 1.5T Mrgrt System

Authors: Krystal M. Kirby, Aliasghar Rohani, Christopher W. Schneider, Sotirios Stathakis

Affiliation: Mary Bird Perkins Cancer Center, Louisiana State University, Baton Rouge, Louisiana

Abstract Preview: Purpose: Accurate treatment delivery in MR-guided radiotherapy (MRgRT) systems requires consistent imaging quality assurance (QA) to assess MR image quality. However, standard QA phantoms have limited...

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-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...

Determine Noise Weighting Factor in Photon-Counting CT Via Deep Learning for Personalized Noise Reduction

Authors: Xinhui Duan, Roderick W. McColl, Mi-Ae Park, Liqiang Ren, Gary Xu, Kuan Zhang, Yue Zhang

Affiliation: UT Southwestern Medical Center, Department of Radiology, UT Southwestern Medical Center, Imaging Services, UT Southwestern Medical Center

Abstract Preview: Purpose:
Image-based deep-learning noise-reduction techniques have been developed for photon-counting CT (PCCT) to improve image quality with reduced radiation dose. The denoising strength is typic...

Developing Patient-Specific Functional Atlases with Inverse Distance Weighting of MR Images

Authors: Chibawanye I. Ene, Sherise D. Ferguson, Ping Hou, Vinodh A. Kumar, Ho-Ling Anthony Liu, Kyle R. Noll, Sujit S. Prabhu, Jian Ming Teo, Max Wintermark

Affiliation: Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose:
Functional brain atlases are used to guide clinical functional MRI (fMRI) analyses. Imprecise assertions may introduce the ecological fallacy as atlases are reflective of the constituent c...

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...

Development of Foundation Model for Analysis of Prostate Cancer with Mpmri

Authors: Ahmad Algohary, Adrian Breto, Quadre Emery, Radka Stoyanova

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

Abstract Preview: Purpose:
To develop a foundation model (U-Found) for multiparametric MRI (mpMRI) of the prostate by using self-supervised learning to prove the feasibility of a prostate-oriented foundation model u...

Diffusion-Weighted MRI: An Early Biomarker for Treatment Response in MR-Guided Treatment of Rectal Cancer

Authors: Huiming Dong, Jonathan Pham, X. Sharon Qi, Ann Raldow

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

Abstract Preview: Purpose: The study aimed to investigate longitudinal apparent diffusion coefficient (ADC) as an early biomarker of treatment response in patients with locally advanced rectal cancer (LARC) undergoing ...

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...

Evaluation of Synaptive MRI Geometric Distortion with Sun Nuclear MR Distortion and Image Fusion Head Phantom

Authors: Michael Evan Chaga, Timothy Chen, Darra M. Conti, Jing Feng, Wenzheng Feng, Joseph Hanley, Jeff Stainsby, Tingyu Wang

Affiliation: Synaptive Medical, Hackensack Meridian Health, Jersey Shore University Medical Center

Abstract Preview: Purpose:
The Synaptive MRI is a 0.5T superconducting head only system (first US install 2023). This is the first study that evaluates the geometric accuracy using a Sun Nuclear MR Distortion and Im...

Evaluation of an Adaptive Denoising Diffusion Probabilistic Model (DDPM) for Fast MRI in Radiotherapy Planning of Pediatric Brain Tumors

Authors: Chia-Ho Hua, Jirapat Likitlersuang, Jinsoo Uh

Affiliation: St. Jude Children's Research Hospital

Abstract Preview: Purpose: AI-based fast MRI, which reconstructs images from undersampled k-space data, has not yet been tailored for RT planning. This study aims to evaluate the fast MRI performance of our recently pr...

Exploiting the Glycerol CH2 Resonances for Estimating Relative Free Fatty Acid to Triglyceride Concentration Ratios

Authors: José Antonio Klautau Toffoli, Anthony G. Tessier, Atiyah Yahya

Affiliation: Department of Oncology, University of Alberta

Abstract Preview: Purpose: To demonstrate that magnetic resonance spectroscopy (MRS) methyl (≈0.9 ppm) to glycerol (≈4.1-4.3 ppm) resonance area ratios correlate with relative concentrations of free fatty acids to trig...

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...

Functional MRI Guided Partial Tumor Irradiation to Improve Tumor Control and Spare Tumor Microenvironment

Authors: Bingqi Guo, Ping Xia

Affiliation: Cleveland Clinic

Abstract Preview: Purpose:
Spatially fractionated radiation therapy (SFRT) delivers a “GRID” or “lattice” of high and low doses to tumors to increase tumor control, minimize normal tissue damage, and preserve the im...

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...

Geometrically Derived Density Compensation Function for 3D Non-Cartesian MRI Reconstruction

Authors: Oluyemi Bright Aboyewa, KyungPyo Hong, Daniel Kim

Affiliation: Department of Radiology, Northwestern University

Abstract Preview: Purpose: While non-Cartesian MRI is desirable for fast imaging with high spatial resolution and robustness to motion, it requires long post-processing times. Preconditioning with an adequate density c...

Improvement of Spine Phantom for MR Imaging of the Spine

Authors: Richard Dortch, Thammathida Ketsiri, Zhiqiang Li, Shiv P. Srivastava

Affiliation: Barrow Neurological Institute, Dignity Health Cancer Institute, St. Joseph's Hospital & Medical Center

Abstract Preview: Purpose: Imaging the spinal cord post-surgery is challenging due to metal surgical implants, which induce signal loss and geometric distortions. Together, this hinders the visualization of the spinal ...

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...

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...

Investigating the Use of a DWI Phantom for Routine QA of an MR-Linac at Room Temperature

Authors: Nicholas Carlson, Joel J. St-Aubin

Affiliation: University of Iowa Hospitals and Clinics, University of Iowa

Abstract Preview: Purpose: To establish baselines metrics and determine longitudinal accuracy and reproducibility of Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) values for a 1.5T Elekta Un...

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-Center Diffusion-Weighted MRI Validation for 0.35T MR-Linac: A Repeatability and Reproducibility Study

Authors: Tess Armstrong, Nema Bassiri, Alonso N. Gutierrez, Michael Kasper, Natalia Lutsik, Eric Mellon, Kathryn E. Mittauer, Siamak P. Nejad-Davarani, Shyam Pokharel, Suresh Rana, Hui Wang, Joseph Weygand

Affiliation: Department of Radiation Oncology and Applied Science, Dartmouth Health, Miami Cancer Institute, Baptist Health South Florida, ViewRay, Inc., Miami Cancer Institute, Lynn Cancer Institute, Boca Raton Regional Hospital, Baptist Health South Florida, Department of Radiation Oncology, University of Miami

Abstract Preview: Purpose: Radiation treatments on the MR-linac (MRL) enable daily acquisition of anatomical and physiological images for adaptive treatment planning. The apparent diffusion coefficient (ADC) estimated ...

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...

Multidimensional Diffusion MRI Based Microstructural Heterogeneity Model on a 5T MR System

Authors: Jiayi Chen, Shaolei Li, Fuhua Yan, Yingli Yang, Jie Zhang

Affiliation: Department of Radiology, Ruijin Hospital, Institute for Medical Imaging Technology, Ruijin Hospital, Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Shanghai United imaging Healthcare Advanced Technology Research Institute, Department of Radiation Oncology, Ruijin Hospital

Abstract Preview: Purpose: The aim of this exploratory study is to investigate the feasibility of establishing a model to explore tissue component heterogeneity using multidimensional diffusion magnetic resonance imagi...

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...

Next Generation Nanoparticles for Fractionated MRI-Guided Radiation Therapy

Authors: Stephanie Bennett, Ross I. Berbeco, Guillaume Bort, Needa Brown, Lena Carmes, Sandrine Dufort, Michael John Lavelle, Geraldine Le Duc, Francois Lux, Toby Morris, Zeinaf Muradova, Andrea Protti, Olivier Tillement

Affiliation: University de Lyon, NH TherAGuIX, University of Massachsetts Lowell and Dana-Farber Cancer Institute Boston, Department of Radiation & Cellular Oncology, University of Chicago, University of Central Florida, Department of Radiation Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, NH TherAguix, Universite de Lyon, Brigham and Women's Hospital, Dana-Farber Cancer Institute

Abstract Preview: Purpose: AGuIX is a theranostic Gd-based nanoparticle currently under phase-2 clinical testing where patients receive 2-3 doses at 1-week intervals prior to imaging and irradiation. AGuIX-Bi is a new ...

Non-Invasive Ferumoxytol MRI Evaluation of Dual Tumors Response for Hypoxia Region after Radiotherapy

Authors: Deng-Yuan Chang, Matthew L. Scarpelli

Affiliation: Purdue University

Abstract Preview: Purpose: Tumor-associated macrophages (TAMs) can be assessed by Ferumoxytol-MRI in breast cancer because they are negatively correlated with the prognosis of patient outcome. However, some studies hav...

Novel Automatic Detection of Surface Brachytherapy Applicators and Catheters on Vibe MR Images

Authors: Ivan M. Buzurovic, Evangelia Kaza

Affiliation: Department of Radiation Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Brigham and Women's Hospital, Harvard Medial School, Dana-Farber Cancer Institute, Department of Radiation Oncology

Abstract Preview: Purpose: Recent advances allow for depiction of surface brachytherapy flap applicators using MRI, with empty catheters detected by signal intensity reduction. As manual catheter detection is subjectiv...

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...

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

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

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

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

Patient-Specific Ultra-Sparse k-Space Reconstruction Using Motion Decomposition and Sinusoidal Representation Networks for Dynamic Volumetric MRI in Radiotherapy

Authors: Karyn A Goodman, Yang Lei, Tian Liu, Charlotte Elizabeth Read, Jing Wang, Qian Wang, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai, Department of Radiation Oncology, Beth Israel Deaconess Medical Center

Abstract Preview: Purpose: Accurate motion management in MRI-guided radiotherapy (MRIgRT) relies on real-time volumetric MRI to track intra-fractional anatomical changes. Dense k-space sampling, while capable of produc...

Patterns of Nanoparticle Uptake for Patients with Multiple Brain Metastases: Similarities and Differences to Standard Gbca

Authors: Stephanie Bennett, Ross I. Berbeco, Ning Jin, Sonal Josan, Justin Michael Sheetz, Atchar Sudhyadhom

Affiliation: Department of Radiation & Cellular Oncology, University of Chicago, University of Massachusetts - Lowell, Siemens Healthineers, Brigham and Women’s Hospital and Dana Farber Cancer Institute, Harvard Medical School,, Brigham and Women's Hospital

Abstract Preview: Purpose: AGuIX, a Gadolinium-based theranostic radiosensitizing nanoparticle, is currently under clinical evaluation in Europe and the US. Using patients from the double-blinded NanoBrainMets trial, u...

Predicting and Monitoring Response to Head and Neck Cancer Radiotherapy Using Multi-Modality Imaging and Radiobiological Digital Twin Simulations

Authors: Eric Aliotta, Michalis Aristophanous, Joseph O. Deasy, Bill Diplas, Milan Grkovski, James Han, Vaios Hatzoglou, Jeho Jeong, Nancy Y Lee, Ramesh Paudyal, Nadeem Riaz, Heiko Schoder, Amita Shukla-Dave

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

Abstract Preview: Purpose: To forecast radiotherapy treatment response for head and neck cancer (HNC) using multimodality imaging and personalized radiobiological modeling.
Methods: Multi-modality imaging data from ...

Prediction of Vertebral Compression Fracture after Stereotactic Body Radiotherapy for Spinal Metastases Using Clinical, Radiomic and Dosiomic Features

Authors: Yukio Fujita, Syoma Ide, Kei Ito, Tomohiro Kajikawa, Satoshi Kito, Keiko Murofushi, Yujiro Nakajima, Yuhi Suda, Kentaro Taguchi, Naoki Tohyama, Fumiya Tsurumaki

Affiliation: Komazawa University Graduate School, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Department of Radiology, Kyoto Prefectural University of Medicine

Abstract Preview: Purpose: Stereotactic body radiotherapy (SBRT) for spine metastases is more effective for pain relief and local control than conventional radiotherapy. However, it is associated with vertebral compres...

Radiomics Changes in MR Images of Prostate and Dominant Intra-Prostate Lesions during SBRT on MR-Linac

Authors: David L. Barbee, David Byun, Ting Chen, Paulina E. Galavis, Siming Lu, Sarah Rosemary Morris, Hesheng Wang, Michael J Zelefsky

Affiliation: NYU Langone Health

Abstract Preview: Purpose: MR-Linac enables dose escalation in prostate SBRT on accurately defined dominant intra-prostate lesion (DIL) on daily MR images. This study aims to evaluate inter-fraction changes in the radi...

Region-Specific Structure-Function Coupling Alterations in Parkinson’s Disease: Insights from Multi-Modal MRI

Authors: Yifei Hao, Ting Huang, Wenxuan Li, Xiang Li, Manju Liu, Rong Liu, Tao Peng, Yulu Wu, Fang-Fang Yin, Lei Zhang, Yaogong Zhang, Jiangtao Zhu

Affiliation: Duke University, Department of Radiology, The Second Affiliated Hospital of Soochow University, School of Future Science and Engineering, Soochow University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: This study investigates the alterations in structure-function coupling (SC-FC) networks in Parkinson’s disease (PD) patients, focusing on region-specific disruptions and compensatory mechanis...

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

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

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

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

Segmentation Regularized Registration Training Improves Multi-Domain Generalization of Deformable Image Registration for MR-Guided Prostate Radiotherapy

Authors: Lando S. Bosma, Victoria Brennan, Nicolas Cote, ChengCheng Gui, Nima Hassan Rezaeian, Jue Jiang, Sudharsan Madhavan, Josiah Simeth, Neelam Tyagi, Harini Veeraraghavan, Michael J Zelefsky

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, NYU Langone Health, University Medical Center Utrecht, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Deep learning-based deformable image registration (DIR) models often lack robustness when applied to datasets with differing imaging characteristics. We aimed to (1) improve registration netw...

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...

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...

Using Multiple Sequences MRI for Synthesizing CT Based on a Deep Learning Approach

Authors: Jie Hu, Nan Li, Chuanbin Xie, Shouping Xu, Xinlei Xu, Gaolong Zhang, Zhilei Zhang

Affiliation: School of Physics, Beihang University, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, Peopleʼs Republic of China, Department of Radiation Oncology, School of Physics, Beihang University, Beijing, 102206, Peopleʼs Republic of China

Abstract Preview: Purpose: This study aims to synthesize CT images for MRI-only radiation therapy using a deep learning approach that integrates information from the T1- and T2-weighted MRI sequence.
Methods: 97 hea...

Utilizing Multiple Modalities to Improve Models to Predict Changes in International Prostate Score for Prostate Cancer

Authors: Matthew C Abramowitz, Alan Dal Pra, Rodrigo Delgadillo, Nesrin Dogan, John C. Ford, Kyle R. Padgett, Levent Sensoy, Benjamin Spieler, Matthew T. Studenski, Jace Allen Walker

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

Abstract Preview: Purpose:
Toxicities that affect a patient’s quality-of-life due to prostate cancer (pCa) radiation therapy (RT) are receiving more attention as RT has become increasingly successful in treating pCA...