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

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DOIResolve DOI: http://doi.org/10.1117/12.2043654
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TypeArticle
Proceedings titleProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ConferenceMedical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 16 February 2014 through 17 February 2014, San Diego, CA
ISSN1605-7422
ISBN9780819498304
Volume9037
Article number90371D
SubjectMedical imaging; Noise abatement; Optical properties; Optical tomography; Tissue; Automatic classification; Intravascular imaging; IOCT; OCT; Optical parameter; Parameter estimation method; Speckle noise reduction; Volume of interest; Tissue engineering
AbstractIn 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.
Publication date
PublisherSPIE
LanguageEnglish
AffiliationNational Research Council Canada; Energy, Mining and Environment
Peer reviewedYes
NPARC number21272891
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Record identifier26278b9d-35ab-473d-8558-9daff23bec65
Record created2014-12-03
Record modified2016-05-09
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