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Results for "Ali Uneri": 4 found

3D Image Quality Evaluation of a New CT Scanner Employing 3D Landmark Scans, Super Resolution Reconstruction, and Ag Beam Filtration

Authors: Ishika Bhaumik, John M. Boone, Michael T Corwin, Eric S Diaz, Ahmadreza Ghasemiesfe, Andrew M. Hernandez, Sarah E. McKenney, Misagh Piran, Ali Uneri, Eric L White

Affiliation: UC Davis, UC Davis Health, University of California, Johns Hopkins Univ

Abstract Preview: Purpose: A new model CT scanner (Canon Aquilion One Insight) was recently installed at our institution, and it included a 3D Landmark (3DLM) scan for automatic scan planning, a new deep learning recon...

BEST IN PHYSICS IMAGING: Population-Based Cardio-Respiratory Motion Model to Simulate 4D CT Angiography and 2D+t Fluoroscopy for Percutaneous Coronary Intervention

Authors: Debarghya China, Junghoon Lee, Ali Uneri

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

Abstract Preview: Purpose: This study aims to develop a population-based cardio-respiratory motion model and apply it to patient-specific 3D CTA to simulate 4D CTA and 2D+t fluoroscopy sequences. The developed motion m...

Developing a Dataset for Investigations into the Impact of CT Acquisition and Reconstruction Conditions on Quantitative Imaging Using Paired Image Quality and Radiomics Phantom Data

Authors: Morgan A. Daly, David J. Goodenough, Andrew M. Hernandez, John M. Hoffman, Joshua Levy, Michael F. McNitt-Gray, Ali Uneri, Bino Varghese

Affiliation: University of California, George Washington University, David Geffen School of Medicine at UCLA, Johns Hopkins Univ, University of Southern California, The Phantom Laboratory

Abstract Preview: Purpose: Quantitative imaging is affected by CT acquisition and reconstruction conditions, limiting robustness in multi-site or -scanner studies. This work aimed to develop a dataset that will enable ...

Image Quality-Based Clinical CT Cohort Selection from Midrc Using a Multi-Institutional Phantom Dataset

Authors: John M. Boone, Andrew M. Hernandez, Paul E. Kinahan, Michael F. McNitt-Gray, Jeffrey H. Siewerdsen, Ali Uneri

Affiliation: University of California, Johns Hopkins Univ, UT MD Anderson Cancer Center, David Geffen School of Medicine at UCLA, University of Washington, UC Davis Health

Abstract Preview: Purpose: Measuring image quality (IQ) in large clinical databases, such as the Medical Imaging and Data Resource Center (MIDRC), is challenging due to the inherent complexity of image content and the ...