Mr. Mojtaba Zarei Profile

Mojtaba Zarei

at Duke University

SPIE Involvement:

Author

Publications (9)

Proceedings Article | 2 April 2024 Presentation + Paper

Performance assessment of photon counting versus energy integrated CT: concordance of in vivo and phantom measurements

Francesco Ria, Mojtaba Zarei, Mridul Bhattarai, Steve Bache, Njood Alsaihati, Fides Schwartz, Ehsan Samei

Proceedings Volume 12925, 1292504 (2024) https://doi.org/10.1117/12.3006532

KEYWORDS: Image quality, Computed tomography, Modulation frequency, Mercury, Scanners, Imaging systems, Photon counting, Modulation transfer functions, In vivo imaging, Body scanners

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Proceedings Article | 1 April 2024 Paper

Virtual NLST: towards replicating national lung screening trial

Fakrul Islam Tushar, Liesbeth Vancoillie, Cindy McCabe, Amareswararao Kavuri, Lavsen Dahal, Brian Harrawood, Milo Fryling, Mojtaba Zarei, Saman Sotoudeh-Paima, Fong Chi Ho, Dhrubajyoti Ghosh, Sheng Luo, W. Paul Segars, Ehsan Abadi, Kyle Lafata, Ehsan Samei, Joseph Lo

Proceedings Volume 12925, 129252F (2024) https://doi.org/10.1117/12.3006915

KEYWORDS: Lung, Data modeling, Computed tomography, Scanners, 3D modeling, Computer simulations, Modulation transfer functions, Chest imaging, Medicine, Medical research

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SPIE Journal Paper | 27 December 2023

Coronary stenosis quantification in cardiac computed tomography angiography: multi-factorial optimization of image quality and radiation dose

Mojtaba Zarei, Ehsan Abadi, William Paul Segars, Ehsan Samei

JMI, Vol. 10, Issue 06, 063502, (December 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.6.063502

KEYWORDS: Image quality, Computed tomography, Mathematical optimization, Decision trees, Physics, Data modeling, Clinical trials, Angiography, Scanners, Heart

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BackgroundThe accuracy and variability of quantification in computed tomography angiography (CTA) are affected by the interplay of imaging parameters and patient attributes. The assessment of these combined effects has been an open engineering challenge.PurposeIn this study, we developed a framework that optimizes imaging parameters for accurate and consistent coronary stenosis quantification in cardiac CTA while accounting for patient-specific variables.MethodsThe framework utilizes a task-specific image quality index, the estimability index (e′), approximated by a surrogate estimability polynomial function (EPF) capable of finding the optimal protocol that (1) maximizes image quality with an upper bound for desired radiation dose or (2) minimizes the dose level with a lower bound of acceptable image quality. The optimization process was formulated with the decision variables being subject to a set of constraints. The methodology was verified using CTA data from a prior clinical trial (prospective multi-center imaging study for evaluation of chest pain) by assessing the concordance of its prediction with the trial results. Further, the framework was used to derive an optimum protocol for each case based on the patient attributes, gauging how much improvement would have been possible if the derived optimized protocol would have been deployed.ResultsThe framework produced results consistent with imaging physics principles with approximated EPFs of 97% accuracy. The feature importance evaluation demonstrated a close match with earlier studies. The verification study found e′ scores closely predicting the cardiologist scores to within 95% in terms of the area under the receiver operating characteristic curve and predicting potential for either an average of fourfold increase in e′ within a targeted dose or a reduction in radiation dose by an average of 57% without reducing the image quality.ConclusionsThe protocol optimization framework provides means to assess and optimize CTA in terms of either image quality or radiation dose objectives with its results predicting prior clinical trial findings.

Proceedings Article | 7 April 2023 Presentation + Paper

A systematic assessment of photon-counting CT for bone mineral density and microarchitecture quantifications

Cindy McCabe, Thomas Sauer, Mojtaba Zarei, W. Paul Segars, Ehsan Samei, Ehsan Abadi

Proceedings Volume 12463, 1246303 (2023) https://doi.org/10.1117/12.2654028

KEYWORDS: Bone, Diseases and disorders, Scanners, Computed tomography, X-ray computed tomography, Bone imaging, Osteoporosis, Image sharpness, Biological imaging

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Proceedings Article | 7 April 2023 Presentation + Paper

Harmonizing CT images via physics-based deep neural networks

Mojtaba Zarei, Saman Sotoudeh-Paima, Cindy McCabe, Ehsan Abadi, Ehsan Samei

Proceedings Volume 12463, 124631Q (2023) https://doi.org/10.1117/12.2654215

KEYWORDS: Computed tomography, Education and training, Modulation transfer functions, Lung, Chronic obstructive pulmonary disease, Radiomics, Image quality, Neural networks, Scanners, Point spread functions

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The rendition of medical images influences the accuracy and precision of quantifications. Image variations or biases make measuring imaging biomarkers challenging. The objective of this paper is to reduce the variability of computed tomography (CT) quantifications for radiomics and biomarkers using physics-based deep neural networks (DNNs). The proposed framework used the knowledge of ground truth through a virtual imaging trial (VIT) methodology to harmonize the different renditions of CT scans across variations in reconstruction kernel and dose. This harmonization was done by developing a generative adversarial network (GAN) model, informed by pixel values and patient-based modulation transfer function (MTF) estimates. To train the network, the VIT platform was used to acquire CT images from a set of forty computational human models (XCAT). Models included varying levels of pulmonary diseases including lung nodules and emphysema. The patient models were imaged with a validated CT simulator (DukeSim) modeling a commercial CT scanner operating across a range of dose (20-100 mAs) and reconstruction kernels (15 from smooth to sharp). The harmonized virtual images were evaluated in three different ways: 1) visual assessment of the images, 2) bias and variation in density-based biomarkers, and 3) bias and variation in morphological-based biomarkers. The harmonization improved the bias and variability of the test set images yielding a structural similarity index of 95±1%, a normalized mean squared error of 10.2±1.5%, and a peak signal-to-noise ratio of 31.8±1.5 dB compared to variations in these metrics when no harmonization was applied (87±9%, 14.2±4%, and 29.6±2, respectively). Emphysema-based imaging biomarkers of LAA-950 (-1.5±1.8%), Perc15 (13.65±9.3 HU), and Lung mass (0.1±0.3 g) had more precise and consistent quantifications compared to values when images were not harmonized (9.9±12%, -36.0±53 HU, and 0.2±0.4g, respectively). These results suggest that the method can be promising to improve consistency in multi-center studies when reliable and consistent quantification of data from multiple systems are required.

Proceedings Article | 4 April 2022 Poster + Paper

A truth-based primal-dual learning approach to reconstruct CT images utilizing the virtual imaging trial platform

Mojtaba Zarei, Saman Sotoudeh-Paima, Ehsan Abadi, Ehsan Samei

Proceedings Volume 12031, 120313B (2022) https://doi.org/10.1117/12.2613168

KEYWORDS: Signal attenuation, Reconstruction algorithms, Signal to noise ratio, Network architectures, X-ray computed tomography, Inverse problems, Computed tomography, Neural networks, CT reconstruction, Sensors

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Proceedings Article | 4 April 2022 Presentation + Paper

Optimization of imaging parameters of an investigational photon-counting CT prototype for lung lesion radiomics

Cindy McCabe, Mojtaba Zarei, W. Paul Segars, Ehsan Samei, Ehsan Abadi

Proceedings Volume 12033, 1203337 (2022) https://doi.org/10.1117/12.2612973

KEYWORDS: Image segmentation, Lung, Computed tomography, Lung cancer, Photon counting, Cancer

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

A probabilistic conditional adversarial neural network to reduce imaging variation in radiography

Mojtaba Zarei, Ehsan Abadi, Rafael Fricks, W. Paul Segars, Ehsan Samei

Proceedings Volume 11595, 115953Y (2021) https://doi.org/10.1117/12.2582336

KEYWORDS: Radiography, Neural networks, Diagnostics, Visualization, Visual analytics, Imaging systems, Systems modeling, Medical imaging, Instrument modeling, Image processing

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Proceedings Article | 15 February 2021 Presentation + Paper

Multi-factorial optimization of imaging parameters for quantifying coronary stenosis in cardiac CT

Mojtaba Zarei, Ehsan Abadi, W. Paul Segars, Taylor Richards, Ehsan Samei

Proceedings Volume 11595, 1159504 (2021) https://doi.org/10.1117/12.2582311

KEYWORDS: Computed tomography, Data modeling, Transform theory, Mathematical modeling, Computer programming

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Showing 5 of 9 publications

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