Browsing by Author "Abadi, Ehsan"
Now showing items 1-10 of 10
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A comparison of COVID-19 and imaging radiation risk in clinical patient populations.
Ria, Francesco; Fu, Wanyi; Chalian, Hamid; Abadi, Ehsan; Segars, Paul W; Fricks, Rafael; Khoshpouri, Pegah; ... (8 authors) (J Radiol Prot, 2020-10-07)OBJECTIVE: The outbreak of coronavirus SARS-COV2 affected more than 180 countries necessitating fast and accurate diagnostic tools. Reverse transcriptase polymerase chain reaction (RT-PCR) has been identified as a gold standard ... -
Classification of COVID-19 in chest radiographs: assessing the impact of imaging parameters using clinical and simulated images
Fricks, Rafael; Abadi, Ehsan; Ria, Francesco; Samei, Ehsan (Medical Imaging 2021: Computer-Aided Diagnosis, 2021-02-15)As computer-aided diagnostics develop to address new challenges in medical imaging, including emerging diseases such as COVID-19, the initial development is hampered by availability of imaging data. Deep learning algorithms ... -
Deep learning classification of COVID-19 in chest radiographs: performance and influence of supplemental training
Fricks, Rafael B; Ria, Francesco; Chalian, Hamid; Khoshpouri, Pegah; Abadi, Ehsan; Bianchi, Lorenzo; Segars, William P; ... (8 authors) (Journal of Medical Imaging, 2021-12-01) -
Development and Application of Realistic Anatomical and Imaging Models for Virtual Clinical Trials in Computed Tomography
Abadi, Ehsan (2018)The purpose of this dissertation was to develop comprehensive toolsets for performing quality based virtual clinical trials in computed tomography. The developed toolsets in this dissertation enable rigorous quantification ... -
Estimation of in vivo noise in clinical CT images: comparison and validation of three different methods against ensemble noise gold-standard
Ria, Francesco; Smith, Taylor; Abadi, Ehsan; Solomon, Justin; Samei, Ehsan (Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115952P, 2021-02-15)Image quality estimation is crucial in modern CT with noise magnitude playing a key role. Several methods have been proposed to estimate noise surrogates in vivo. This study aimed to ascertain the accuracy of three different ... -
Modeling Patient-Informed Liver Contrast Perfusion in Contrast-enhanced Computed Tomography.
Setiawan, Hananiel; Ria, Francesco; Abadi, Ehsan; Fu, Wanyi; Smith, Taylor B; Samei, Ehsan (J Comput Assist Tomogr, 2020-11)OBJECTIVE: To determine the correlation between patient attributes and contrast enhancement in liver parenchyma and demonstrate the potential for patient-informed prediction and optimization of contrast enhancement in liver ... -
Patient-informed modelling of hepatic contrast dynamics in contrast-enhanced CT imaging
Setiawan, Hananiel; Ria, Francesco; Abadi, Ehsan; Fu, Wanyi; Smith, Taylor; Samei, Ehsan (Medical Imaging 2020: Physics of Medical Imaging, 2020-03-16)PURPOSE Iodinated contrast agents are commonly used in CT imaging to enhance tissue contrast. Consistency in contrast enhancement (CE) is critical in radiological diagnosis. Contrast material circulation in individual patients ... -
Scientific Abstracts and Sessions
Ria, Francesco; Smith, Taylor; Abadi, Ehsan; Solomon, Justin; Samei, ehsan (Medical Physics, 2020-06)Purpose Image quality estimation in CT is crucial for technology assessment, procedure optimization, and overall radiological benefit evaluation, with noise magnitude playing a key role. Over the years, several methods have ... -
Validation of algorithmic CT image quality metrics with preferences of radiologists
Cheng, Yuan; Abadi, Ehsan; Smith, Taylor Brunton; Ria, Francesco; Meyer, Mathias; Marin, Daniele; Samei, Ehsan (MEDICAL PHYSICS, 2019-11-01) -
Validation of Algorithmic CT Image Quality Metrics with Preferences of Radiologists.
Cheng, Yuan; Abadi, Ehsan; Smith, Taylor Brunton; Ria, Francesco; Meyer, Mathias; Marin, Daniele; Samei, Ehsan (Medical physics, 2019-08-29)PURPOSE:Automated assessment of perceptual image quality on clinical Computed Tomography (CT) data by computer algorithms has the potential to greatly facilitate data-driven monitoring and optimization of CT image acquisition ...