Validation of algorithmic CT image quality metrics with preferences of radiologists

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Citation

Published Version (Please cite this version)

10.1002/mp.13795]

Publication Info

Cheng, Yuan, Ehsan Abadi, Taylor Brunton Smith, Francesco Ria, Mathias Meyer, Daniele Marin and Ehsan Samei (2019). Validation of algorithmic CT image quality metrics with preferences of radiologists. MEDICAL PHYSICS, 46(11). pp. 4837–4846. [10.1002/mp.13795]](https://doi.org/10.1002/mp.13795]) Retrieved from https://hdl.handle.net/10161/19750.

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Scholars@Duke

Abadi

Ehsan Abadi

Associate Professor in Radiology

Ehsan Abadi, PhD is an imaging scientist at Duke University. He serves as an Assistant Professor in the departments of Radiology and Electrical & Computer Engineering, a faculty member in the Medical Physics Graduate Program and Carl E. Ravin Advanced Imaging Laboratories, and a co-Lead in the Center for Virtual Imaging Trials. Ehsan’s research focuses on quantitative imaging and optimization, CT imaging, lung diseases, computational human modeling, and medical imaging simulation. He is actively involved in developing computational anthropomorphic models with various diseases such as COPD, and scanner-specific simulation platforms (e.g., DukeSim) for imaging systems. Currently, his work is centered on identifying and optimizing imaging systems to ensure accurate and precise quantifications of lung diseases.

Ria

Francesco Ria

Assistant Professor of Radiology

Dr. Francesco Ria is a medical physicist and he serves as an Assistant Professor in the Department of Radiology. Francesco has an extensive expertise in the assessment of procedure performances in radiology. In particular, his research activities focus on the simultaneous evaluation of radiation dose and image quality in vivo in computed tomography providing a comprehensive evaluation of radiological exams. Moreover, Francesco is developing and investigating novel mathematical models that, uniquely in the radiology field, can incorporate a comprehensive and quantitative risk-to-benefit assessment of the procedures; he is continuing to apply his expertise towards the definition of new patient specific risk metrics, and in the assessment of image quality in vivo also using state-of-the-art imaging technology, such as photon counting computed tomography scanners, and machine learning reconstruction algorithms.

Dr. Ria is a member of the American Association of Physicists in Medicine task group 392 (Investigation and Quality Control of Automatic Exposure Control System in CT), of the American Association of Physicists in Medicine Public Education working group (WGATE), and of the Italian Association of Medical Physics task group Dose Monitoring in Diagnostic Imaging.


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