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Results for "William Paul Segars": 5 found

A Topological Technique to Unify Image Texture and Morphology to Enhance Radiomic Feature Representations

Authors: David Brizel, Kyle J. Lafata, Jian-Guo Liu, Yvonne M Mowery, Yvonne M Mowery, William Paul Segars, Jack B Stevens

Affiliation: Department of Physics, Duke University, Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Duke University Medical Center, Duke University, Department of Radiation Oncology, Duke University, University of Pittsburgh

Abstract Preview: Purpose: To develop a technique to quantify tumor topology using a unifying mathematical framework that integrates texture and morphology and to evaluate its feasibility as a prognostic biomarker for ...

An Open-Access Toolkit to Generate Realistic CT and Low-Field MR Images Based on an Xcat Phantom

Authors: Debora de Souza Antonio, Romy Guthier, Konrad Pawel Nesteruk, Erno Sajo, William Paul Segars, Gregory C. Sharp, Atchar Sudhyadhom, Hengyong Yu

Affiliation: Massachusetts General Hospital, Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Duke University Medical Center, Brigham and Women’s Hospital and Dana Farber Cancer Institute, Harvard Medical School,, Massachusetts General Hospital and Harvard Medical School, University of Massachusetts Lowell

Abstract Preview: Purpose: To develop an open-access toolkit for rapidly generating simultaneously realistic CT scans and low-field MR images of the abdominal region, based on patient data, while employing an XCAT phan...

Estimating Organ Dose of Standard Pediatric CT Protocols to Computational Phantoms through the Dukesim CT Simulator

Authors: Dean Darnell, Iyanna M Lewis, Francesco Ria, William Paul Segars

Affiliation: Duke University, Duke University Medical Physics Graduate Program, Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Duke University Medical Center, Duke University Medical Center

Abstract Preview: Purpose: To estimate radiation dose to pediatric patients during standard CT imaging of the chest and abdomen and determine how dose varies with patient age, height and weight and how measurements com...

Patient-Specific Imaging Modality Agnostic Virtual Digital Twins Modeling Temporally Varying Digestive Motion

Authors: James M. Balter, Lando S. Bosma, Jorge Tapias Gomez, Nishant Nadkarni, Mert R Sanbuncu, William Paul Segars, Ergys D. Subashi, Neelam Tyagi, Harini Veeraraghavan

Affiliation: University of Michigan, The University of Texas MD Anderson Cancer Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Duke University Medical Center, Cornell University, University Medical Center Utrecht, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Develop patient-specific virtual digital twin (VDT) cohorts modeling physically realistic spatio-temporal gastrointestinal (GI) organs (stomach and duodenum) digestive motion.
Methods: Pat...

Simulating Realistic Digital Phantoms for Virtual Clinical Trials in Radiology and Radiation Oncology Using a Deep-Learning Based Conditional Denoising Diffusion Probabilistic Model (c-DDPM)

Authors: Matthew Brown, Yushi Chang, Jinhyuk Choi, William Silva Mendes, Lei Ren, Aman Sangal, William Paul Segars, Phuoc Tran, Hualiang Zhong

Affiliation: University of Maryland School of Medicine, Department of Radiation Oncology, University of Maryland School of Medicine, Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Duke University Medical Center, Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose: Digital phantoms like XCAT are essential for imaging and treatment optimization in radiology and radiation oncology. However, the lack of realistic textures (HU distribution) in XCAT limits i...