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Results for "virtual trials": 12 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...

Assessment of the Impact of CT Respiratory Motion on PET SUV Quantification through Virtual Imaging

Authors: Ehsan Abadi, Darrin Byrd, Paul E. Kinahan, Katie Marie Olivas, Ehsan Samei

Affiliation: Duke University, Center for Virtual Imaging Trials, Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System, University of Washington

Abstract Preview: Purpose: To evaluate the impact of respiratory motion during CT acquisitions on PET image quantification using an integrated PET-CT simulation pipeline.
Methods: A validated CT simulator (DukeSim) ...

BEST IN PHYSICS IMAGING: Dosimetric Impact of Iodinated Contrast Agent on Fibroglandular Tissue in Contrast-Enhanced Digital Mammography

Authors: Hannah Grover, Andrew J. Sampson

Affiliation: Oregon Health & Science University, UT Health San Antonio

Abstract Preview: Purpose: The goal of this work was to quantify the dosimetric impact of iodinated contrast on fibroglandular breast tissue to better inform clinical risk and benefit assessments when determining the m...

Does the Method Matter? How in Hominum, In Vivo, in silico, and in Phantasma Measures Compare and Contrast Is Assessing the Utility of Photon Counting CT?

Authors: Ehsan Abadi, Njood Alsaihati, Steven T. Bache, Mridul Bhattarai, Cindy Marie McCabe, Francesco Ria, Ehsan Samei

Affiliation: Duke University, Center for Virtual Imaging Trials, Duke University, Duke University Health System, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System

Abstract Preview: Purpose: To compare and contrast alternative methods including reader (in hominum), phantom (in phantasma), in vivo, and in silico methods deployed to assess the performance of photon counting (PCCT) ...

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...

Harnessing Virtual Imaging Trials to Advance AI Development and Evaluation

Authors: Ehsan Abadi

Affiliation: Duke University

Abstract Preview: N/A...

In-Silico Clinical Trials Enabled By Digital Twin Approach Can Accurately and Prospectively Predict Outcomes of Clinical Trials Combining Radiation and Systemic Therapy

Authors: Clemens Grassberger, David (Bo) McClatchy, Harald Paganetti

Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, Massachusetts General Hospital

Abstract Preview: Purpose: While randomized controlled trials (RCTs) are the gold standard for demonstrating efficacy, nearly 50% of late-stage clinical trials fail to meet their endpoint. Tools to study the design of ...

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...

Predictive Quality of Differing Body Size Measurands for Radiation Risk Estimation in CT Imaging: A Virtual Trial Study

Authors: Njood Alsaihati, Francesco Ria, Ehsan Samei, Justin B. Solomon, Martina Talarico, Jered Wells

Affiliation: Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System

Abstract Preview: Purpose: Radiation dose and associated risk in X-ray imaging is principally informed by patient size, further used in Computed Tomography (CT) to achieve prescribed image quality levels through tube c...

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...

Supercomputing-Enabled CT Virtual Imaging Trials: A Population-Scale Pilot Study

Authors: Ehsan Abadi, Zakaria Aboulbanine, Nicholas D Felice, David Fenwick, Anuj J. Kapadia, Cindy Marie McCabe, Jayasai Ram Rajagopal, Ehsan Samei

Affiliation: Duke University, Oak Ridge National Laboratory, Center for Virtual Imaging Trials, Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System

Abstract Preview: Purpose:
Virtual imaging trials (VITs) offer a computational alternative to clinical imaging trials leveraging virtual patients, scanners, and interpreters to assess imaging questions. To provide m...