Computer-Aided Diagnosis

Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular optical coherence tomography three-dimensional pullbacks

[+] Author Affiliations
Ronny Shalev, Soumya Ray

Case Western Reserve University, Department of Electrical Engineering and Computer Science, Cleveland, Ohio 44106, United States

Madhusudhana Gargesha, David Prabhu, Andrew M. Rollins

Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio 44106, United States

Kentaro Tanaka, Hiram G. Bezerra

University Hospitals Case Medical Center, Harrington Heart and Vascular Institute, Imaging Core Laboratory, Cleveland, Ohio 44106, United States

Guy Lamouche, Charles-Etienne Bisaillon

National Research Council, 75, de Mortagne, Boucherville, Quebec J4B 6Y4, Canada

David L. Wilson

Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio 44106, United States

Case Western Reserve University, Department of Radiology, Cleveland, Ohio 44106, United States

J. Med. Imag. 3(2), 024501 (May 13, 2016). doi:10.1117/1.JMI.3.2.024501
History: Received November 18, 2015; Accepted April 11, 2016
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Abstract.  Analysis of intravascular optical coherence tomography (IVOCT) data has potential for real-time in vivo plaque classification. We developed a processing pipeline on a three-dimensional local region of support for estimation of optical properties of atherosclerotic plaques from coronary artery, IVOCT pullbacks. Using realistic coronary artery disease phantoms, we determined insignificant differences in mean and standard deviation estimates between our pullback analyses and more conventional processing of stationary acquisitions with frame averaging. There was no effect of tissue depth or oblique imaging on pullback parameter estimates. The method’s performance was assessed in comparison with observer-defined standards using clinical pullback data. Values (calcium 3.58±1.74  mm1, lipid 9.93±2.44  mm1, and fibrous 1.96±1.11  mm1) were consistent with previous measurements obtained by other means. Using optical parameters (μt, I, I0), we achieved feature space separation of plaque types and classification accuracy of 92.5±3%. Despite the rapid z motion and varying incidence angle in pullbacks, the proposed computational pipeline appears to work as well as a more standard “stationary” approach.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Ronny Shalev ; Madhusudhana Gargesha ; David Prabhu ; Kentaro Tanaka ; Andrew M. Rollins, et al.
"Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular optical coherence tomography three-dimensional pullbacks", J. Med. Imag. 3(2), 024501 (May 13, 2016). ; http://dx.doi.org/10.1117/1.JMI.3.2.024501


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