Dr. Stefano Young Profile

Dr. Stefano Young

Software Developer at Retain Health

SPIE Involvement:

Author | Leadership Workshop Facilitator

Publications (5)

Proceedings Article | 24 March 2016 Paper

Assessing nodule detection on lung cancer screening CT: the effects of tube current modulation and model observer selection on detectability maps

J. Hoffman, F. Noo, K. McMillan, S. Young, M. McNitt-Gray

Proceedings Volume 9787, 97870Q (2016) https://doi.org/10.1117/12.2217348

KEYWORDS: Lung, Lung cancer, Modulation, Computed tomography, Sensors, Signal attenuation, Data modeling

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Proceedings Article | 24 March 2016 Paper

Effects of CT dose and nodule characteristics on lung-nodule detectability in a cohort of 90 national lung screening trial patients

Stefano Young, Pechin Lo, John Hoffman, H. J. Grace Kim, Matthew Brown, Michael McNitt-Gray

Proceedings Volume 9785, 97850G (2016) https://doi.org/10.1117/12.2217405

KEYWORDS: Lung, Computer aided diagnosis and therapy, Computer aided design, Computed tomography, Lung cancer, Reconstruction algorithms, Databases, Scanners, Medicine, Image segmentation, Solids

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Proceedings Article | 19 March 2014 Paper

Estimating lesion volume in low-dose chest CT: How low can we go?

Stefano Young, Michael McNitt-Gray

Proceedings Volume 9033, 903306 (2014) https://doi.org/10.1117/12.2043730

KEYWORDS: Computed tomography, Lung, Chest, Photons, Sensors, Scanners, Data modeling, Heart, Lung cancer, Signal attenuation

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Proceedings Article | 3 March 2012 Paper

A mathematical framework for including various sources of variability in a task-based assessment of digital breast tomosynthesis

Subok Park, Andreu Badal, Stefano Young, Kyle Myers

Proceedings Volume 8313, 83134S (2012) https://doi.org/10.1117/12.911241

KEYWORDS: Convolution, Breast, Monte Carlo methods, X-rays, Sensors, Digital breast tomosynthesis, X-ray detectors, Quantum electronics, Computer simulations, Chest

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Proceedings Article | 14 March 2009 Paper

Estimating breast tomosynthesis performance in detection tasks with variable-background phantoms

Stefano Young, Subok Park, S. Kyle Anderson, Aldo Badano, Kyle Myers, Predrag Bakic

Proceedings Volume 7258, 72580O (2009) https://doi.org/10.1117/12.813900

KEYWORDS: Digital breast tomosynthesis, Breast, Sensors, Imaging systems, Performance modeling, Mathematical modeling, Data modeling, Tumors, X-rays, Tissues

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Digital breast tomosynthesis (DBT) shows potential for improving breast cancer detection. However, this technique has not yet been fully characterized with consideration of the various uncertainties in the imaging chain and optimized with respect to system acquisition parameters. To obtain maximum diagnostic information in DBT, system optimization needs to be performed across a range of patients and acquisition parameters to quantify their impact on tumor detection performance. In addition, a balance must be achieved between x-ray dose and image quality to minimize risk to the patient while maximizing the system's detection performance. To date, researchers have applied a task-based approach to the optimization of DBT with use of mathematical observers for tasks in the signal-known-exactly background-known-exactly (SKE/BKE) and signal-known-exactly background-known statistically (SKE/BKS) paradigms1-3. However, previous observer models provided insufficient treatment of the spatial correlations between multi-angle DBT projections, so we incorporated this correlation information into the modeling methodology. We developed a computational approach that includes three-dimensional variable background phantoms for incorporating background variability, accurate ray-tracing and Poisson distributions for generating noise-free and noisy projections of the phantoms, and a channelized-Hotelling observer4 (CHO) for estimating performance in DBT. We demonstrated our method for a DBT acquisition geometry and calculated the performance of the CHO with Laguerre-Gauss channels as a function of the angular span of the system. Preliminary results indicate that the implementation of a CHO model that incorporates correlations between multi-angle projections gives different performance predictions than a CHO model that ignores multi-angle correlations. With improvement of the observer design, we anticipate more accurate investigations into the impact of multi-angle correlations and background variability on the performance of DBT.

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