Mr. Alex Ivanov Profile

Alex Ivanov

at Massachusetts General Hospital

SPIE Involvement:

Author

Publications (2)

SPIE Journal Paper | 9 January 2021 Open Access

Task-dependent estimability index to assess the quality of cardiac computed tomography angiography for quantifying coronary stenosis

Ehsan Samei, Taylor Richards, William Segars, Melissa Daubert, Alex Ivanov, Geoffrey Rubin, Pamela Douglas, Udo Hoffmann

JMI, Vol. 8, Issue 01, 013501, (January 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.1.013501

KEYWORDS: Computed tomography, Image quality, Scanners, Motion models, Stochastic processes, Angiography, Clinical trials, Calcium, Statistical analysis, Imaging systems

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Purpose: Quantifying stenosis in cardiac computed tomography angiography (CTA) images remains a difficult task, as image noise and cardiac motion can degrade image quality and distort underlying anatomic information. The purpose of this study was to develop a computational framework to objectively assess the precision of quantifying coronary stenosis in cardiac CTA. Approach: The framework used models of coronary vessels and plaques, asymmetric motion point spread functions, CT image blur (task-based modulation transfer functions) and noise (noise-power spectrums), and an automated maximum-likelihood estimator implemented as a matched template squared-difference operator. These factors were integrated into an estimability index (e ′ ) as a task-based measure of image quality in cardiac CTA. The e ′ index was applied to assess how well it can to predict the quality of 132 clinical cases selected from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain trial. The cases were divided into two cohorts, high quality and low quality, based on clinical scores and the concordance of clinical evaluations of cases by experienced cardiac imagers. The framework was also used to ascertain protocol factors for CTA Biomarker initiative of the Quantitative Imaging Biomarker Alliance (QIBA). Results: The e ′ index categorized the patient datasets with an area under the curve of 0.985, an accuracy of 0.977, and an optimal e ′ threshold of 25.58 corresponding to a stenosis estimation precision (standard deviation) of 3.91%. Data resampling and training–test validation methods demonstrated stable classifier thresholds and receiver operating curve performance. The framework was successfully applicable to the QIBA objective. Conclusions: A computational framework to objectively quantify stenosis estimation task performance was successfully implemented and was reflective of clinical results in the context of a prominent clinical trial with diverse sites, readers, scanners, acquisition protocols, and patients. It also demonstrated the potential for prospective optimization of imaging protocols toward targeted precision and measurement consistency in cardiac CT images.

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Proceedings Article | 1 March 2019 Paper

Cardiac CT estimability index: an ideal estimator in the presence of noise and motion

Taylor Richards, Alex Ivanov, Paul Segars, Udo Hoffmann, Ehsan Samei

Proceedings Volume 10948, 109485W (2019) https://doi.org/10.1117/12.2513035

KEYWORDS: Estimation theory, Cardiac imaging, Image quality, Stochastic processes, Computed tomography, Motion estimation, Motion models, Scanners, Modulation transfer functions, Statistical analysis, Image acquisition, X-ray computed tomography

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