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Results for "short term": 21 found

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

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

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

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

A Novel Feature Selection Method for Survival Prediction of Head-and-Neck Following Radiation Therapy

Authors: Xiaoying Pan, X. Sharon Qi

Affiliation: Department of Radiation Oncology, University of California, Los Angeles, School of Computer Science and technology,Xi'an University of Posts and Telecommunications

Abstract Preview: Purpose:
Survival prediction for cancer presents a substantial hurdle in personalized oncology, due to intricate, high-dimensional medical data. Our study introduces an innovative feature selection...

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...

AI-Driven Early Detection of Digital Radiography Performance Degradation: A Predictive Quality Control Approach

Authors: Giovanni Iacca, Gloria Miori, Laura Orsingher, Daniele Ravanelli, Annalisa Trianni

Affiliation: Department of Information Engineering and Computer Science, University of Trento, Medical Physics Department, S.Chiara Hospital, APSS

Abstract Preview: Purpose: This study aims to leverage artificial intelligence (AI) to predict and identify performance degradation in Digital Radiography (DR) systems, enabling proactive maintenance and minimizing cli...

An Explainable Classifier for Enhancing the Quality Assurance of Digital Breast Tomosynthesis Phantom Images

Authors: Hui-Shan Jian, Yu-Ying Lin

Affiliation: Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou

Abstract Preview: Purpose: The image quality assurance of mammographic images is crucial for correct diagnosis. To develop and validate an explainable deep-learning classifier for phantom image quality assessment of di...

Characterization of a Novel Large-Sized Epid System from the Newly Released Ring Shape Radiation Therapy Halos Tx

Authors: Sen Bai, Guyu Dai, Dong Gao, Lecheng Jia, Guangjun Li, Yanfang Liu, Ying Song, Qing Xiao, Wei Zhang

Affiliation: United Imaging Healthcare, West China Hospital of Sichuan University, Shenzhen United Imaging Research Institute of Innovative Medical Equipment

Abstract Preview: Purpose:
Electronic portal imaging device (EPID) is currently the most widely used in-vivo dosimetry (IVD) device. However, the effective detector area shortage hinders further applications. This s...

Characterization of a Prototype Flashdiamond Detector for Conventional and Uhdr Beams

Authors: Dominique Guillet, Arthur Lalonde, Bryan R. Muir, James Renaud, Karim Zerouali

Affiliation: Universite de Montreal, Centre Hospitalier de l'Universite de Montreal, National Research Council

Abstract Preview: Purpose:
To evaluate the dose rate dependence, beam quality dependence, and short-term stability of a novel flashDiamond (fD) prototype detector for conventional and ultra-high dose rate (UHDR) bea...

Comparison of AI-Based and Ants for Longitudinal Deformable Image Registration in Head and Neck Cancer

Authors: Aditya P. Apte, Joseph O. Deasy, Jue Jiang, Nancy Lee, Sudharsan Madhavan, Nishant Nadkarni, Lopamudra Nayak, Harini Veeraraghavan, Wei Zhao

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

Abstract Preview: Purpose: To track early response to radiotherapy using digital twins, it is crucial to quantify tumor volume and mass changes. Traditional tumor detection methods, particularly in image registration, ...

Comparison of Cine Cardiac Magnetic Resonance (CMR) Imaging Performance: 0.6 T Versus 1.5 T, Is 0.6 T Fit for Purpose?

Authors: Jacinta E. Browne, Tzu Cheng Chao, Ajit Deveraj, Ece Ercan, Tim Leiner, Alessio Perazzolo, Michal Povazan, Jouke Smink, Camilla Vita, Spencer Waddle, Dinghui Wang

Affiliation: Università Cattolica del Sacro Cuore, Philips Healthcare, Mayo Clinic

Abstract Preview: Purpose:
In recent years, mid-field MRI has shown promise to meet the technical demands of cardiac imaging1. Mid-field strengths offer advantages to CMR due to shorter T1-relaxation times, lower sp...

Comparison of Linear Energy Transfer Volume Histograms from Two Beam Geometries in a Pediatric Chordoma Patient Treated with Intensity Modulated Proton Therapy

Authors: Michael Confer, Mark A. Newpower

Affiliation: University of Oklahoma Health Sciences Center

Abstract Preview: Purpose: A pediatric patient recently presented to our clinic for treatment for chordoma, which was near the brainstem. The clinician elected to treat this patient using intensity modulated proton the...

Early GU Toxicity Prediction in Prostate SBRT Using Delivered Dosimetry Via Long Short-Term Memory Model

Authors: Amar Kishan, Jun Lian, Yunkui Pang, Jonathan Pham, X. Sharon Qi, Michael Steinberg, Luca F Valle, Pew-Thian Yap

Affiliation: Department of Radiation Oncology, University of California, Los Angeles, University of North Carolina at Chapel Hill

Abstract Preview: Purpose: Stereotactic body radiotherapy (SBRT) is a highly effective treatment for prostate cancer, yet predicting genitourinary (GU) toxicity has primarily relied on planned dosimetry. This study inv...

Enhanced Predictive Model for Toxicity and 3-Year Survival in HCC Patients Using Learning Health System Infrastructure and AI-Driven Statistical Profiling

Authors: Ameer Elaimy, Theodore Lawrence, Charles S. Mayo, Seyyedeh Azar Oliaei Motlagh, Benjamin S. Rosen

Affiliation: University of Michigan

Abstract Preview: Purpose: To analyze the impact of clinical features on short-term survival, toxicity, and poor outcomes in HCC patients treated with SBRT,using automated data aggregation and enhanced algorithms with ...

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 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...

Is Simplicity Even Better: Deep Learning Algorithms for Breath Motion Phase Prediction in Motion Management

Authors: Amanda J. Deisher, Andrew YK Foong, Witold Matysiak, Jing Qian, Xueyan Tang, Erik J. Tryggestad, Mi Zhou

Affiliation: Mayo Clinic

Abstract Preview: Purpose: Phase gating is commonly employed to mitigate the impact of tumor motion in radiotherapy. Due to the machine-specific time delay between triggering and radiation delivery, the triggering sign...

Liver Tumor Auto-Contouring Using Recurrent Neural Networks on MRI-Linac for Adaptive Radiation Therapy

Authors: Yan Dai, Jie Deng, Christopher Kabat, Weiguo Lu, Ying Zhang, Hengrui Zhao

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

Abstract Preview: Purpose:
MRI-guided adaptive radiotherapy (MRgART) using MR-LINAC systems offers significant advantages for liver cancer, enabling superior tumor delineation and online plan adaptation. However, ma...

Multi-Mechanism CNN and Long Short-Term Memory Fusion Model for Improved CT-Based Thyroid Cancer Diagnosis

Authors: Yunfei Dong, Dongyang Guo, Jiongli Pan, Tao Peng, Caiyin Tang, Zhenyu Yang, Fang-Fang Yin, Lei Zhang, Tianyi Zhang, Yaogong Zhang

Affiliation: Duke Kunshan University, Department of Radiology, Taizhou People’s Hospital Affiliated to Nanjing Medical University, School of Future Science and Engineering, Soochow University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: This study aims to improve the accuracy of CT-based diagnosis of thyroid cancer by developing a hybrid model that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (...

Personalized Radiotherapy: A Novel Approach to Multi-Criteria Optimization with Patient-Specific Risk Integration

Authors: Ali Ajdari, Thomas R. Bortfeld, Zhongxing Liao, Mara Schubert, Katrin Teichert

Affiliation: The University of Texas MD Anderson Cancer Center, Department Of Radiation Oncology, Massachusetts General Hospital (MGH), Massachusetts General Hospital & Harvard Medical School, Fraunhofer ITWM

Abstract Preview: Purpose: Radiotherapy (RT) treatment planning often involves solving a multi-criteria optimization (MCO) problem. Conventionally, MCO considers a set of generic (population-wide) dosimetric criteria, ...

Predicting Pathological Complete Response to Neoadjuvant Chemotherapy for Breast Cancer at Early Time Points Using a Two-Stage Dual-Task Deep Learning Strategy

Authors: Bowen Jing, Jing Wang

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: Medical images acquired at multiple time points during neoadjuvant chemotherapy allow physicians to assess patients’ responses and personalize treatment plans accordingly. Studies from the I-...

Reinforcement Learning Based Machine Parameter Optimization for Two-Arc Prostate VMAT Planning

Authors: William T. Hrinivich, Junghoon Lee, Lina Mekki

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

Abstract Preview: Purpose: Volumetric modulated arc therapy (VMAT) planning is a computationally expensive process. In this work, we propose a reinforcement learning (RL) framework to automatically optimize dose rate a...

Two Years in - Lessons Learned from Managing a Global Medical Physics Webinar Series

Authors: Ghada Aldosary, Abdullah A. Alshreef, Amineh Khatib Hamad, Eenas A. Omari

Affiliation: Loma Linda University Medical Center, Jerusalem, KASCH- Ministry of National Guard Health Affairs, Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose:
Webinars are an effective tool for disseminating knowledge and expertise to a global audience. A medical physics webinar series was established in 2022 to provide accessible continued educ...