Validation of parameter estimation methods for determining optical properties of atherosclerotic tissues in intravascular OCT

Ronny Shalev; Madhusudhana Gargesha; David Prabhu; Kentaro Tanaka; Andrew M. Rollins; Marco Costa; Hiram G. Bezerra; Guy Lamouche; David L. Wilson

doi:10.1117/12.2043654

11 March 2014 Validation of parameter estimation methods for determining optical properties of atherosclerotic tissues in intravascular OCT

Ronny Shalev, Madhusudhana Gargesha, David Prabhu, Kentaro Tanaka, Andrew M. Rollins, Marco Costa, Hiram G. Bezerra, Guy Lamouche, David L. Wilson

Author Affiliations +

Proceedings Volume 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment; 90371D (2014) https://doi.org/10.1117/12.2043654
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

In this paper we present a new process for assessing optical properties of tissues from 3D pullbacks, the standard clinical acquisition method for iOCT data. Our method analyzes a volume of interest (VOI) consisting of about 100 A-lines spread across the angle of rotation (θ) and along the artery, z. The new 3D method uses catheter correction, baseline removal, speckle noise reduction, alignment of A-line sequences, and robust estimation. We compare results to those from a more standard, “gold standard” stationary acquisition where many image frames are averaged to reduce noise. To do these studies in a controlled fashion, we use a realistic optical artery phantom containing of multiple “tissue types.” Precision and accuracy for 3D pullback analysis are reported.

Our results indicate that when implementing the process on a stationary acquisition dataset, the uncertainty improves at each stage while the uncertainty is reduced. When comparing stationary acquisition dataset to pullback dataset, the values were as follows: calcium: 3.8±1.09mm^-1 in stationary and 3.9±1.2 mm^-1 in a pullback; lipid: 11.025±0.417 mm^-1 in stationary and 11.27±0.25 mm^-1 in pullback; fibrous: 6.08±1.337 mm^-1 in stationary and 5.58±2.0 mm^-1. These results indicates that the process presented in this paper introduce minimal bias and only a small change in uncertainty when comparing a stationary and pullback dataset, thus paves the way to a highly accurate clinical plaque type discrimination, enabling automatic classification.

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Ronny Shalev, Madhusudhana Gargesha, David Prabhu, Kentaro Tanaka, Andrew M. Rollins, Marco Costa, Hiram G. Bezerra, Guy Lamouche, and David L. Wilson "Validation of parameter estimation methods for determining optical properties of atherosclerotic tissues in intravascular OCT", Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90371D (11 March 2014); https://doi.org/10.1117/12.2043654

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