| DOI | Resolve DOI: https://doi.org/10.1117/12.2043654 |
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| Author | Search for: Shalev, Ronny; Search for: Gargesha, Madhusudhana; Search for: Prabhu, David; Search for: Tanaka, Kentaro; Search for: Rollins, Andrew M.; Search for: Costa, Marco; Search for: Bezerra, Hiram G.; Search for: Lamouche, Guy1; Search for: Wilson, David L. |
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| Affiliation | - National Research Council of Canada. Energy, Mining and Environment
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| Format | Text, Article |
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| Conference | SPIE Medical Imaging, February 16-17, 2014, San Diego, CA, USA |
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| Subject | medical 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 |
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| 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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| Publication date | 2014 |
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| Publisher | SPIE |
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| In | |
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| Series | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 21272891 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 26278b9d-35ab-473d-8558-9daff23bec65 |
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| Record created | 2014-12-03 |
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| Record modified | 2020-04-22 |
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