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Results for "mri generation": 27 found

3D Bioprinted Brain Cancer Constructs for Experimental Synchrotron Radiotherapy

Authors: John Paul Ortiz Bustillo, Elette Engels, Elrick T. Inocencio, Michael Lerch, Julia Rebecca D Posadas, Anatoly Rosenfeld, Kiarn Roughley, Moeava Tehei, Gordon Wallace, Danielle Warren, Vincent de Rover

Affiliation: Centre for Medical Radiation Physics, University of Wollongong Australia, Department of Radiology, University of the Philippines- Philippine General Hospital, Intelligent Polymer Research Institute, ARC Centre of Excellence for Electromaterials Science, AIIM Facility, University of Wollongong Australia, Centre for Medical Radiation Physics, University of Wollongong, Intelligent Polymer Research Institute, ARC Centre of Excellence for Electromaterials Science, AIIM Facility, University of Wollongong, University of the Philippines Manila; Centre for Medical Radiation Physics, University of Wollongong Australia

Abstract Preview: Purpose: To compare the biological response through cell viability assay and fluorescence imaging of 3D bioprinted brain cancer (glioma) constructs relative to 2D monolayer and 3D spheroid culture for...

A Deep Learning-Based Method for Rapid Generation of Spot Weights in Single Field Optimization for Proton Therapy in Prostate Cancer

Authors: Yu Chang, Mei Chen

Affiliation: Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine

Abstract Preview: Purpose: Spot weights optimization, as a critical step in the proton therapy, is often time-consuming and labor-intensive. Deep learning, with its powerful learning and computational efficiency, can e...

A Ground Truth Label-Mediated Method for Improved Bone and Gas Cavity Definition for MRI-Guided Online Adaptive Radiotherapy Workflows Using Synthetic CT Images.

Authors: Benito De Celis Alonso, Braian Adair Maldonado Luna, Gerardo Uriel Perez Rojas, René Eduardo Rodríguez-Pérez, Kamal Singhrao

Affiliation: Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute, Harvard Medical School, Faculty of Physics and Mathematics, Benemérita Universidad Autónoma de Puebla

Abstract Preview: Purpose: Artificial Intelligence (AI)-generated synthetic CT (sCT) images can be used to provide electron densities for dose calculation for online adaptive MRI-guided stereotactic body radiotherapy (...

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 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...

Addressing Missing MRI Sequences: A DL-Based Region-Focused Multi-Sequence Framework for Synthetic Image Generation

Authors: Amir Abdollahi, Oliver JĂ€kel, Maxmillian Knoll, Rakshana Murugan, Adithya Raman, Patrick Salome

Affiliation: UKHD & DKFZ, Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), German Cancer Research Centre(DKFZ), DKFZ, MGH

Abstract Preview: Purpose:
Missing MRI sequences, due to technical issues in data handling or clinical constraints like contrast agent intolerance, limit the use of medical imaging datasets in computational analysis...

An Automated System for MRI Coil Performance Evaluation

Authors: Michael Cuddy, Samuel J. Fahrenholtz, Khushnood Hamdani, Saman Jirjes, Robert G. Paden, Jeremiah W. Sanders, William F. Sensakovic, Wolfgang Stefan, Jeffrey Xiao, Yuxiang Zhou

Affiliation: Mayo, Mayo Clinic Arizona, Mayo Clinic

Abstract Preview: Title: An automated system for MRI coil performance evaluation

Purpose: To develop an automated quality control (QC) system for MRI coils to assess element-level signal-to-noise ratio (SNR), ar...

Assessment of Automated Planning Templates for Genitourinary and Gastrointestinal Disease Sites for Online MR-Guided Adaptive Radiotherapy

Authors: Shahed Badiyan, Tsuicheng D. Chiu, Viktor M. Iakovenko, Steve Jiang, Christopher Kabat, Mu-Han Lin, Roberto Pellegrini, Arnold Pompos, Edoardo Salmeri, David Sher, Sruthi Sivabhaskar, Justin D. Visak

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, Global Clinical Science, Elekta AB, UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center

Abstract Preview: Purpose: Adaptive treatment planning requires robust strategies to enable streamlined on-couch processes, creating a significant barrier for planners transitioning from conventional to adaptive planni...

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...

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

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

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

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

Development and Implementation of MRI-Only Simulation, Planning and Treatment Workflow for Pelvic Radiotherapy Using Synthetic CT on MR-Linac

Authors: Peter Balter, Elaine Eunnae Cha, Seungtaek Choi, Yao Ding, Eun Young Han, Yusung Kim, Rajat J. Kudchadker, Belinda Lee, Surendra Prajapati, Reza Reiazi, Ergys D. Subashi, Sarath Vijayan, Jinzhong Yang, Yao Zhao

Affiliation: The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose: This study evaluates the feasibility of an MR-only simulation, planning, and treatment (MROSPT) workflow for pelvic cancer patients using synthetic CT generated from MRI data. By validating s...

Development of an Ultrafast MR Technique to Detect Linac Radiation Pulses for Biological Imaging on a Clinical 0.35T MR-Linac

Authors: Pierre Gardair, Johnathan E Leeman, Claire Keun Sun Park, Atchar Sudhyadhom

Affiliation: Brigham and Women’s Hospital, Dana Farber Cancer Institute, Harvard Medical School, Brigham and Women’s Hospital and Dana Farber Cancer Institute, Harvard Medical School,, Brigham and Women’s Hospital and Dana Farber Cancer Institute, Harvard Medical School, Harvard Medical School

Abstract Preview: Purpose: Dose is a poor surrogate for radiobiological damage but no in vivo technology exists to directly measure damage such as DNA strand breaks and free radical generation (FRG). Recent advances in...

Evaluation of a Novel Multimodal Deformable Image Registration Algorithm for Pelvic MRI-CT Fusion in Radiotherapy

Authors: Christian Fiandra, Marco Fusella, Gianfranco Loi, Silvia Pesente, Lorenzo Placidi, Claudio Vecchi, Orlando Zaccaria, Stefania Zara

Affiliation: Abano Terme Hospital, University of Turin, Maggiore della CaritĂ , Tecnologie Avanzate Srl, Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Abstract Preview: Purpose: Deformable-image-registration (DIR) is essential in modern radiotherapy for adaptive RT, re-irradiation, and other clinical applications. Multimodal DIR is especially important in MRI-only wo...

Feasibility Study of Deep Learning-Based MRI-to-PET Generation for Rectal Cancer: Overall Survival Prediction and Pathological Complete Response Assessment

Authors: Weigang Hu, Zhenhao Li, Jiazhou Wang, Xiaojie Yin, Zhen Zhang

Affiliation: Fudan University Shanghai Cancer Center

Abstract Preview: Purpose:
This study aims to develop and validate a novel deep learning method to generate synthetic PET images for rectal cancer from MRI data. By incorporating metabolic information from the synth...

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...

Free Radical Irradiation Emergent (FIRE) MRI: Real-Time MR-Based Quantification of Radiation-Produced Free Radical Generation on a Clinical MR-Linac

Authors: Claire Keun Sun Park, Atchar Sudhyadhom, Veena Venkatachalam, Noah Warner

Affiliation: Harvard–MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Brigham and Women’s Hospital and Dana Farber Cancer Institute, Harvard Medical School,, Brigham and Women’s Hospital and Dana Farber Cancer Institute, Harvard Medical School

Abstract Preview: Purpose: Modern radiotherapy achieves sub-millimeter spatial accuracy but fails to account for patient- and spatially-specific variations in biological response. Free radical generation (FRG), particu...

Inspiring the Next Generation: A 4-Year Review of a Successful Medical Physics Undergraduate Internship Program

Authors: Manuel M. Arreola, Izabella L. Barreto, Amanda Schwarz

Affiliation: University of Florida, University of Florida College of Medicine

Abstract Preview: Purpose: In 2020, a medical physics undergraduate internship program was developed to attract competitive students to the field of medical physics.
Methods: The program offered a unique, immersive ...

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...

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...

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 ...

Prompt Preset for Imaging Physics Education and Board Style Question Generation: Development and Validation

Authors: Rose Al Helo, Shengwen Deng, Sven L. Gallo, David W. Jordan

Affiliation: Department of Radiology, Radiation Safety, University Hospitals Cleveland Medical Center, University Hospitals Cleveland Medical Center, Department of Radiology, Radiation Safety, University Hospitals Cleveland Medical Center; School of Medicine, Case Western Reserve University; Department of Radiology, Louis Stokes Cleveland VA Medical Center

Abstract Preview: Purpose: From an educator perspective, preparing test questions for trainees is time-consuming and requires a lot of quality verification steps (review of stems, distractors, referencing) that can pot...

Research on Glioma MRI Image Generation Based on Large Language Model and Diffusion Model

Authors: Xiangli Cui, Chi Han, Man Hu, Wanli Huo, Xunan Wang, Jianguang Zhang, Yingying Zhang

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

Abstract Preview: Purpose:
Medical image generation has broad application prospects in deep learning, but the model training effect is often limited due to the lack of real image data. This study aims to explore the...

Synthetic CT Generation from a Cycle Diffusion Model Based Framework for Ultrasound-Based Prostate HDR Brachytherapy

Authors: Michael Baine, Charles Enke, Yang Lei, Yu Lei, Ruirui Liu, Su-Min Zhou

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

Abstract Preview: Purpose: This study presents a framework for generating synthetic CT images using a Cycle Diffusion model, which can be utilized to enhance needle conspicuity in ultrasound-guided prostate HDR brachyt...

Synthetic Vs. Conventional Planar Imaging: Performance and Clinical Feasibility

Authors: Jennifer Kwak, Chelsea Manica, Justin K. Mikell, Michael Silosky, Wendy Siman

Affiliation: Washington University School of Medicine in St. Louis, University of Colorado Anschutz Medical Campus, School of Medicine, Rocky Vista University

Abstract Preview: Purpose:
This study evaluates synthetic planar imaging (synP) from SPECT projections against conventional planar imaging, focusing on detectability, spatial resolution, and feasibility. SynP allows...

Teaching an Old Dog New Tricks: Unlocking Hidden Potential in Existing Frameworks for Versatile Radiotherapy Applications

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

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

Abstract Preview: Purpose:
This work demonstrates how existing software, when creatively adapted, can address a wide range of clinical challenges. By focusing on data exploration and application-specific modificatio...

Validation of an Ivim Tool for Liver Hypoxia Assessment for an Online Adaptive MR-Linac System

Authors: Jean-Pierre Bissonnette, Catherine Coolens, Laura Dawson, Ryan A Kuhn, Michael Maddalena, Teo Stanescu

Affiliation: Princess Margaret Hospital, The Princess Margaret Cancer Centre - UHN, University of Toronto, Princess Margaret Cancer Centre

Abstract Preview: Purpose: To investigate the generation and reproducibility of 3D hypoxia maps in liver hepatocellular carcinoma (HCC) patients using data derived from an Intravoxel Incoherent Motion (IVIM) MR sequenc...