Mr. Saman Sotoudeh-Paima Profile

Saman Sotoudeh-Paima

at Duke University

SPIE Involvement:

Author

Saman is a third year Ph.D. student at the Department of Electrical & Computer Engineering, Pratt School of Engineering, Duke University. Currently, he is a research assistant at Ravin Advanced Imaging Laboratories (RAI Labs) and the Center of Virtual Imaging Trials (CVIT). Saman has active contributions in the assessment and optimization of CT imaging systems, development of CT harmonization algorithms, discovery of novel and robust imaging biomarkers of COPD, and modeling of computational anthropomorphic phantoms with lung diseases.

Proceedings Article | 3 April 2024 Presentation + Paper

Quantitative accuracy of lung function measurement using parametric response mapping: a virtual imaging study

Amar Kavuri, Fong Chi Ho, Mobina Ghojogh-Nejad, Saman Sotoudeh-Paima, Ehsan Samei, W. Paul Segars, Ehsan Abadi

Proceedings Volume 12927, 129270B (2024) https://doi.org/10.1117/12.3006833

KEYWORDS: Lung, Computed tomography, Scanners, Emphysema, Chronic obstructive pulmonary disease, Pulmonary disorders, Statistical modeling, Chest

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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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Proceedings Article | 1 April 2024 Presentation + Paper

Development and application of a virtual imaging trial framework for longitudinal quantification of emphysema in CT

Saman Sotoudeh-Paima, Fong Chi Ho, Mobina Ghojogh Nejad, Amar Kavuri, Bryan O'Sullivan-Murphy, David Lynch, W. Paul Segars, Ehsan Samei, Ehsan Abadi

Proceedings Volume 12925, 129251H (2024) https://doi.org/10.1117/12.3006925

KEYWORDS: Emphysema, Computed tomography, Lung, Algorithm development, Image segmentation, Chronic obstructive pulmonary disease, Reconstruction algorithms, Image restoration, Evolutionary algorithms, Scanners

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Proceedings Article | 7 April 2023 Presentation + Paper

Development and application of a virtual imaging trial framework for airway quantifications via CT

Fong Chi Ho, Saman Sotoudeh-Paima, W. Paul Segars, Ehsan Samei, Ehsan Abadi

Proceedings Volume 12463, 124631B (2023) https://doi.org/10.1117/12.2654263

KEYWORDS: Scanners, Computed tomography, X-ray computed tomography, Chronic obstructive pulmonary disease, Biological imaging, Matrices, Lung, Gold, Anatomy, Medical 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 | 7 April 2023 Presentation + Paper

CT-HARMONICA: physics-based CT harmonization for reliable lung density quantification

Saman Sotoudeh-Paima, Ehsan Samei, Ehsan Abadi

Proceedings Volume 12465, 124651J (2023) https://doi.org/10.1117/12.2654346

KEYWORDS: Emphysema, Computed tomography, Chronic obstructive pulmonary disease, Lung, Image quality

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Photon-counting CT versus conventional CT for COPD quantifications: intra-scanner optimization and inter-scanner assessments using virtual imaging trials

Saman Sotoudeh-Paima, W. Paul Segars, Ehsan Samei, Ehsan Abadi

Proceedings Volume 12031, 120312I (2022) https://doi.org/10.1117/12.2613003

KEYWORDS: Scanners, Chronic obstructive pulmonary disease, Computed tomography

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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

Development and clinical applications of a virtual imaging framework for optimizing photon-counting CT

Ehsan Abadi, Cindy McCabe, Brian Harrawood, Saman Sotoudeh-Paima, William Segars, Ehsan Samei

Proceedings Volume 12031, 120311Q (2022) https://doi.org/10.1117/12.2612079

KEYWORDS: Monte Carlo methods, Sensors, Imaging systems, Chronic obstructive pulmonary disease, Computed tomography, Scanners, Lung, Systems modeling, Signal detection, Modulation transfer functions

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The purpose of this study was to develop a virtual imaging framework that simulates a new photon-counting CT (PCCT) system (NAEOTOM Alpha, Siemens). The PCCT simulator was built upon the DukeSim platform, which generates projection images of computational phantoms given the geometry and physics of the scanner and imaging parameters. DukeSim was adapted to account for the geometry of the PCCT prototype. To model the photon-counting detection process, we utilized a Monte Carlo-based detector model with the known properties of the detectors. We validated the simulation platform against experimental measurements. The images were acquired at four dose levels (CTDIvol of 1.5, 3.0, 6.0, and 12.0 mGy) and reconstructed with three kernels (Br36, Br40, Br48). The experimental acquisitions were replicated using our developed simulation platform. The real and simulated images were quantitatively compared in terms of image quality metrics (HU values, noise magnitude, noise power spectrum, and modulation transfer function). The clinical utility of our framework was demonstrated by conducting two clinical applications (COPD quantifications and lung nodule radiomics). The phantoms with relevant pathologies were imaged with DukeSim modeling the PCCT systems. Different imaging parameters (e.g., dose, reconstruction techniques, pixel size, and slice thickness) were altered to investigate their effects on task-based quantifications. We successfully implemented the acquisition and physics attributes of the PCCT prototype into the DukeSim platform. The discrepancy between the real and simulated data was on average about 2 HU in terms of noise magnitude, 0.002 mm^-1 in terms of noise power spectrum peak frequency and 0.005 mm^-1 in terms of the frequency at 50% MTF. Analysis suggested that lung lesion radiomics to be more accurate with reduced pixel size and slice thickness. For COPD quantifications, higher doses, thinner slices, and softer kernels yielded more accurate quantification of density-based biomarkers. Our developed virtual imaging platform enables systematic comparison of new PCCT technologies as well as optimization of the imaging parameters for specific clinical tasks.

Proceedings Article | 4 April 2022 Presentation + Paper

Virtual vs. reality: external validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography

Fakrul Islam Tushar, Ehsan Abadi, Saman Sotoudeh-Paima, Rafael Fricks, Maciej Mazurowski, W. Paul Segars, Ehsan Samei, Joseph Lo

Proceedings Volume 12033, 1203305 (2022) https://doi.org/10.1117/12.2613010

KEYWORDS: Data modeling, Computed tomography, Computer simulations, Performance modeling, 3D modeling, Artificial intelligence, Process modeling, Virtual reality, Lung, Device simulation

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