Ehsan Samei

Overview:

Dr. Ehsan Samei, PhD, DABR, FAAPM, FSPIE, FAIMBE is a Persian-American medical physicist. He is a tenured Professor of Radiology, Medical Physics, Biomedical Engineering, Physics, and Electrical and Computer Engineering at Duke University. He serves as the Director of the Duke Medical Physics Graduate Program and the Director of the Clinical Imaging Physics Group. He is certified by the American Board of Radiology, and is a Fellow of the American Association of Physicists in Medicine (AAPM), the International Society of Optics and Phtonics (SPIE), and the American Institute of Biomedical Engineering. He is a Councilor of the National Council of Radiation Protection and Measurements (NCRP), and a Distinguished Investigator of the Academy of Radiology Research. He was the founder or co-founder of the Duke Medical Physics Program, the Duke Imaging Physics Residency Program, the Duke Clinical Imaging Physics Group, and the Society of Directors of Academic Medical Physics Programs (SDAMPP). He has held senior leadership positions in the AAPM, SPIE, SDAMPP, and RSNA. 

Dr. Samei’s interests and expertise include x-ray imaging, theoretical imaging models, simulation methods, and experimental techniques in medical image formation, analysis, assessment, and perception.  His current research includes methods to develop image quality and dose metrics that are clinically relevant and that can be used to design and utilize advanced imaging techniques towards optimum interpretive and quantitative performance. He further has an active interest in bridging the gap between scientific scholarship and clinical practice, in the meaningful realization of translational research, and in clinical processes that are informed by scientific evidence. Those include advanced imaging performance characterization, procedural optimization, and radiomics in retrospective clinical dose and quality analytics. He has mentored over 100 trainees (graduate and postgraduate). He has over 900 scientific publications including over 240 referred journal articles. He has been the recipient of 34 grants as Principle Investigator reflecting $13M of extramural funding.

Positions:

Professor of Radiology

Radiology
School of Medicine

Professor in the Department of Physics

Physics
Trinity College of Arts & Sciences

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Professor in the Department of Electrical and Computer Engineering

Electrical and Computer Engineering
Pratt School of Engineering

Education:

M.E. 1995

University of Michigan, Ann Arbor

Ph.D. 1997

University of Michigan, Ann Arbor

Grants:

3D Digital Breast Phantoms For Multimodality Research

Administered By
Radiology
Awarded By
National Institutes of Health
Role
Collaborator
Start Date
End Date

Information-Theoretic Based CAD in Mammography

Administered By
Radiology
Awarded By
National Institutes of Health
Role
Scientist
Start Date
End Date

Tomosynthesis for Improved Breast Cancer Detection

Administered By
Radiology
Awarded By
National Institutes of Health
Role
Co Investigator
Start Date
End Date

Resolution Requirements for Mammographic Displays

Administered By
Radiology
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

3D Printing of Anatomically Realistic Phantoms for Optimization of Imaging Algorithms

Awarded By
National Institutes of Health
Role
Investigator
Start Date
End Date

Publications:

Variability in image quality and radiation dose within and across 97 medical facilities.

Purpose: To characterize variability in image quality and radiation dose across a large cohort of computed tomography (CT) examinations and identify the scan factors with the highest influence on the observed variabilities. Approach: This retrospective institutional-review-board-exempt investigation was performed on 87,629 chest and abdomen-pelvis CT scans acquired for 97 facilities from 2018 to 2019. Images were assessed in terms of noise, resolution, and dose metrics (global noise, frequency in which modulation transfer function is at 0.50, and volumetric CT dose index, respectively). The results were fit to linear mixed-effects models to quantify the variabilities as affected by scan parameters and settings and patient characteristics. A list of factors, ranked by t -value with p < 0.05 , was ascertained for each of the six mixed effects models. A type III p -value test was used to assess the influence of facility. Results: Across different facilities, image quality and dose were significantly different ( p < 0.05 ), with little correlation between their mean magnitudes and consistency (Pearson's correlation coefficient < 0.34 ). Scanner model, slice thickness, recon field-of-view and kernel, mAs, kVp, patient size, and centering were the most influential factors. The two body regions exhibited similar rankings of these factors for noise (Spearman's correlation coefficient = 0.76 ) and dose (Spearman's correlation coefficient = 0.86 ) but not for resolution (Spearman's correlation coefficient = 0.52 ). Conclusions: Clinical CT scans can vary in image quality and dose with broad implications for diagnostic utility and radiation burden. Average scan quality was not correlated with interpatient scan-quality consistency. For a given facility, this variability can be quite large, with magnitude differences across facilities. The knowledge of the most influential factors per body region may be used to better manage these variabilities within and across facilities.
Authors
Smith, TB; Zhang, S; Erkanli, A; Frush, D; Samei, E
MLA Citation
Smith, Taylor B., et al. “Variability in image quality and radiation dose within and across 97 medical facilities.J Med Imaging (Bellingham), vol. 8, no. 5, Sept. 2021, p. 052105. Pubmed, doi:10.1117/1.JMI.8.5.052105.
URI
https://scholars.duke.edu/individual/pub1481860
PMID
33977114
Source
pubmed
Published In
Journal of Medical Imaging (Bellingham, Wash.)
Volume
8
Published Date
Start Page
052105
DOI
10.1117/1.JMI.8.5.052105

An analysis of radiomics features in lung lesions in covid-19

Radiomic features extracted from CT imaging can be used to quantitively assess COVID-19. The objective of this work was to extract and analyze radiomics features in RT-PRC confirmed COVID-19 cases to identify relevant characteristics for COVID-19 diagnosis, prognosis, and treatment. We measured 29 morphology and second-order statistical-based radiomics features from 310 lung lesions extracted from 48 chest CT cases. Features were evaluated according to their coefficient of variation (CV). We calculated the CV for each feature under two statistical conditions: one with all lesions weighted equally and one with all cases weighted equally. In analyzing the patient data, there were 6.46 lesions-per-case and for 81.25% of cases, the lesions presented with bilateral lung involvement. For all radiomic features examined except a€energy', the CV was higher in the lesion distribution than the case distribution. The CV for morphological features were larger than second-order in both distributions, 181% and 85% versus 50% and 42%, respectively. The most variable features were a€surface area', a€ellipsoid volume', a€ellipsoid surface area', a€volume', and a€approximate volume', which deviated from the mean 173-255% in the lesion distribution and 119-176% in the case distribution. The features with the lowest CV were a€homogeneity', a€discrete compactness', a€texture entropy', a€sum average', and a€elongation', which deviated less than 31% by case and less than 25% by lesion. Future work will investigate integrating this data with similar studies and other diagnostic and prognostic criterion enhancing the role of CT in detecting and managing COVID-19.
Authors
Gann, AM; Abadi, E; Hoye, J; Sauer, TJ; Segars, WP; Chalian, H; Samei, E
MLA Citation
Gann, A. M., et al. “An analysis of radiomics features in lung lesions in covid-19.” Progress in Biomedical Optics and Imaging  Proceedings of Spie, vol. 11595, 2021. Scopus, doi:10.1117/12.2582296.
URI
https://scholars.duke.edu/individual/pub1478238
Source
scopus
Published In
Progress in Biomedical Optics and Imaging Proceedings of Spie
Volume
11595
Published Date
DOI
10.1117/12.2582296

Weakly Supervised Multi-Organ Multi-Disease Classification of Body CT Scans.

Authors
Tushar, FI; D'Anniballe, VM; Hou, R; Mazurowski, MA; Fu, W; Samei, E; Rubin, GD; Lo, JY
MLA Citation
Tushar, Fakrul Islam, et al. “Weakly Supervised Multi-Organ Multi-Disease Classification of Body CT Scans.Corr, vol. abs/2008.01158, 2020.
URI
https://scholars.duke.edu/individual/pub1454154
Source
dblp
Published In
Corr
Volume
abs/2008.01158
Published Date

Task-based strategy for optimized contrast enhanced breast imaging: analysis of six imaging techniques for mammography and tomosynthesis

Authors
Ikejimba, L; Kiarashi, N; Lin, Y; Chen, B; Ghate, SV; Zerhouni, M; Samei, E; Lo, JY
MLA Citation
Ikejimba, Lynda, et al. “Task-based strategy for optimized contrast enhanced breast imaging: analysis of six imaging techniques for mammography and tomosynthesis.” Medical Imaging 2012: Physics of Medical Imaging, SPIE, 2012. Crossref, doi:10.1117/12.913377.
URI
https://scholars.duke.edu/individual/pub1430647
Source
crossref
Published In
Medical Imaging 2012: Physics of Medical Imaging
Published Date
DOI
10.1117/12.913377

IPhantom: An automated framework in generating personalized computational phantoms for organ-based radiation dosimetry

We propose an automated framework to generate 3D detailed person-specific computational phantoms directly from patient medical images. We investigate the feasibility of this framework in terms of accurately generating patient-specific phantoms and the clinical utility in estimating patient-specific organ dose for CT images. The proposed framework generates 3D volumetric phantoms with a comprehensive set of radiosensitive organs, by fusing patient image data with prior anatomical knowledge from a library of computational phantoms in a two-stage approach. In the first stage, the framework segments a selected set of organs from patient medical images as anchors. In the second stage, conditioned on the segmented organs, the framework generates unsegmented anatomies through mappings between anchor and nonanchor organs learned from libraries of phantoms with rich anatomy. We applied this framework to clinical CT images and demonstrated its utility for patient-specific organ dosimetry. The result showed the framework generates patientspecific phantoms in ∼10 seconds and provides Monte Carlo based organ dose estimation in ∼30 seconds with organ dose errors <10% for the majority of organs. The framework shows the potential for large scale and real-time clinic analysis, standardization, and optimization.
Authors
Fu, W; Segars, PW; Sharma, S; Lo, JY; Samei, E
MLA Citation
Fu, W., et al. “IPhantom: An automated framework in generating personalized computational phantoms for organ-based radiation dosimetry.” Progress in Biomedical Optics and Imaging  Proceedings of Spie, vol. 11595, 2021. Scopus, doi:10.1117/12.2582238.
URI
https://scholars.duke.edu/individual/pub1478550
Source
scopus
Published In
Progress in Biomedical Optics and Imaging Proceedings of Spie
Volume
11595
Published Date
DOI
10.1117/12.2582238