Publications [#328131] of Ehsan Samei

Papers Published
  1. Abadi, E; Sanders, J; Samei, E, Patient-specific quantification of image quality: An automated technique for measuring the distribution of organ Hounsfield units in clinical chest CT images., Medical physics, vol. 44 no. 9 (September, 2017), pp. 4736-4746 [doi] .

    To develop and validate an automated technique for measuring organ Hounsfield units (HUs) in clinical chest CT images.An automated computer algorithm was developed to measure the distribution of HUs inside four major organs: the lungs, liver, aorta, and spine. These organs were first identified using image processing techniques. Each organ was segmented into multiple regions of interest (ROIs) and characterized in terms of HU values. The medians of the ROI histograms were computed for each dataset. The automated results were validated by assessing their correlation with manual measurements in fifteen contrast-enhanced and fifteen non-contrast-enhanced clinical chest CT datasets. The robustness of the measurements with respect to dependency on image noise and CTDIvol was ascertained. One utility of the approach was further demonstrated in assessing the variability in aorta HUs across 732 patients undergoing noncontrast and contrast-enhanced examinations.The algorithm successfully measured the histograms of the four organs in both contrast and non-contrast-enhanced chest CT exams. The automated measurements were in agreement with manual measurements with a near unity slope of the relationship between automated and manual measurements with high coefficient of determination (slope = 0.931-1.003, R2 = 0.89-0.99). Organ median HU measurements were found to be largely independent of both image noise and CTDIvol (P > 0.05), as expected. Across patient cases, the program ran successfully across 95% (697/732) of cases. Aorta median HUs demonstrated five times more variability in contrast-enhanced exams compared to that in non-contrast-enhanced exams.Patient-specific organ HUs can be measured from clinical datasets. The algorithm that was developed can be run on both contrast-enhanced and non-contrast-enhanced clinical datasets. The method can be applied to automatically extract image HU-contrast characteristics of clinical CT images, not captured in phantom data, whereby enabling quantification and optimization of image quality and contrast administration.