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Results for "followed brain": 9 found

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 Troubleshooting for Truebeam Systems: Development and Testing of a Gpt-4o Chatbot

Authors: Sean P. Devan, Cory S. Knill, Charles K. Matrosic, Zheng Zhang

Affiliation: University of Michigan

Abstract Preview: Purpose: Physicists troubleshooting machine issues during patient treatments often face high-pressure situations, balancing error codes, resource constraints, and time-sensitive decisions. To streamli...

An Advanced Automated Pipeline for Brain Tumor Segmentation on MRI Images in Gamma Knife Radiotherapy

Authors: Zachery Colbert, Matthew Foote, Michael Huo, Mark Pinkham, Prabhakar Ramachandran, Mihir Shanker

Affiliation: Radiation Oncology, Princess Alexandra Hospital, Ipswich Road, Princess Alexandra Hospital

Abstract Preview: Purpose: The study aimed to develop and implement deep learning-based autosegmentation models for the autosegmentation of four key tumor types: brain metastasis, pituitary adenoma, vestibular schwanno...

Assessing the Risks of Synthetic MRI Data in Deep Learning: A Study on U-Net Segmentation Accuracy

Authors: Chuangxin Chu, Haotian Huang, Tianhao Li, Jingyu Lu, Zhenyu Yang, Fang-Fang Yin, Tianyu Zeng, Chulong Zhang, Yujia Zheng

Affiliation: The Hong Kong Polytechnic University, Nanyang Technological University, Australian National University, Medical Physics Graduate Program, Duke Kunshan University, North China University of Technology, Duke Kunshan University

Abstract Preview: Purpose: Deep learning segmentation models, such as U-Net, rely on high-quality image-segmentation pairs for accurate predictions. However, the recent increasing use of generative networks for creatin...

Do We Need Pediatric-Specific Models for Radiotherapy Auto-Contouring? a Comparative Study of Pediatric and Adult-Trained Tools

Authors: Gregory T. Armstrong, James E. Bates, Christine V. Chung, Lei Dong, Ralph Ermoian, Jie Fu, Christine Hill-Kayser, Rebecca M. Howell, Meena S. Khan, Sharareh Koufigar, John T. Lucas, Thomas E. Merchant, Taylor Meyers, Tucker J. Netherton, Constance A. Owens, Arnold C. Paulino, Sogand Sadeghi

Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, Department of Epidemiology and Cancer Control, St. Jude Childrenโ€™s Research Hospital, St. Jude Children's Research Hospital, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Department of Radiation Oncology, St. Jude Childrenโ€™s Research Hospital, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, University of Washington/ Fred Hutchinson Cancer Center, Department of Radiation Oncology, University of Pennsylvania, University of Pennsylvania, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology and Winship Cancer Institute, Emory University, The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences

Abstract Preview: Purpose: Clinical workflows often rely on auto-segmentation tools trained on adult data, which may exhibit suboptimal performance in pediatric imaging due to inherent anatomical variations and smaller...

Latent Diffusion for 3D CT Reconstruction from Biplanar X-Rays

Authors: Guha Balakrishnan, Osama R. Mawlawi, Yiran Sun, Ashok Veeraraghavan

Affiliation: RICE University, UT MD Anderson Cancer Center

Abstract Preview: Purpose:
Previous deep learning (DL) techniques such as X2CT-GAN [1] has shown great promise in reconstructing realistic CT volume from biplanar X-rays, however they introduce numerous artifacts in...

Novel Use of 3D Printing for Pre-Operative Dose Estimation in the First Case of Gammatile Spine Implantation

Authors: Ali Al Asadi, Amanda Lynn DiCarlo, Anthony J. Doemer, Jessie Y. Huang, Ian Y Lee, Alexandra Moceri, Adam Robin, Lisa Scarpace, Mira Shah, Salim Siddiqui, Kundan S Thind

Affiliation: Henry Ford Innovations, Henry Ford Health

Abstract Preview: Purpose: For a patient who had two previous courses of external beam therapy for rectosigmoid adenocarcinoma and presented with painful, recurrent disease in the sacrum, this study describes the first...

Radiation Dose Variability and Predictability Versus PTV Volume and Anatomical Site

Authors: Lars Ewell, Russell J. Hamilton

Affiliation: Banner University Medical Group, Bannerhealth

Abstract Preview: Purpose: To estimate the variability and predictability of radiation dose as a function of the planning target volume (PTV) size and the anatomical sites of head & neck, brain and prostate.
Methods...

The Effect of CT Reconstruction Kernel and Slice Thickness on AI-Based CAD Measurement of Hypodense Volume and Aspects Value for Stroke Evaluation in Non-Contrast Head CT.

Authors: Matthew S Brown, John M. Hoffman, William Hsu, Grace Hyun Kim, Michael F. McNitt-Gray, Spencer Harrison Welland, Anil Yadav

Affiliation: Department of Bioengineering, UCLA, David Geffen School of Medicine at UCLA, UCLA Department of Radiology

Abstract Preview: Purpose: Non-contrast CT (NCCT) is frequently used in initial evaluation of suspected stroke to rule out intracerebral hemorrhage. Quantitative scoring systems like the Alberta Stroke Program Early CT...