Retrospective image-based gating of intracoronary optical coherence tomography: implications for quantitative analysis.

AIMS: Images acquired of coronary vessels during a pullback of time-domain optical coherence tomography (OCT) are influenced by the dynamics of the heart. This study explores the feasibility of applying an in-house developed retrospective image-based gating method for OCT and the influence of catheter dislocation and luminal changes during the cardiac cycle on the outcome of quantitative OCT (QOCT). METHODS AND RESULTS: The gating method was developed using Matlab (The Mathworks, Natick, MA, USA) and operates in a fully-automatic manner. OCT image data of 20 randomly selected patients, acquired with a commercially available system (Lightlab Imaging, Westford, MA, USA), were pulled from our OCT database for development and validation. Twelve of the 20 datasets could be gated; the other eight pullbacks could not be gated due to a lack of motion induced artefacts. Computations required approximately 30 minutes/dataset. Quantitative comparisons between the gated and the non-gated QOCT results showed significant differences for mean areas and volumes (p <0.001) and mean relative differences of -11% (range -2 up to -20%) for lumen areas (gated) and -13% (range -5 up to -24%) for volumes. CONCLUSIONS: Retrospective image-based time-domain OCT gating in the presence of motion induced artefacts is feasible. Significant changes in coronary lumen dimensions during the cardiac cycle were observed by OCT and in consequence, quantitative gated OCT analysis showed significant differences compared to non-gated QOCT analyses.

Fully automatic three-dimensional quantitative analysis of intracoronary optical coherence tomography: method and Validation.

OBJECTIVES AND BACKGROUND: Quantitative analysis of intracoronary optical coherence tomography (OCT) image data (QOCT) is currently performed by a time-consuming manual contour tracing process in individual OCT images acquired during a pullback procedure (frame-based method). To get an efficient quantitative analysis process, we developed a fully automatic three-dimensional (3D) lumen contour detection method and evaluated the results against those derived by expert human observers. METHODS: The method was developed using Matlab (The Mathworks, Natick, MA). It incorporates a graphical user interface for contour display and, in the selected cases where this might be necessary, editing. OCT image data of 20 randomly selected patients, acquired with a commercially available system (Lightlab imaging, Westford, MA), were pulled from our OCT database for validation. RESULTS: A total of 4,137 OCT images were analyzed. There was no statistically significant difference in mean lumen areas between the two methods (5.03 + or - 2.16 vs. 5.02 + or - 2.21 mm(2); P = 0.6, human vs. automated). Regression analysis showed a good correlation with an r value of 0.99. The method requires an average 2-5 sec calculation time per OCT image. In 3% of the detected contours an observer correction was necessary. CONCLUSION: Fully automatic lumen contour detection in OCT images is feasible with only a select few contours showing an artifact (3%) that can be easily corrected. This QOCT method may be a valuable tool for future coronary imaging studies incorporating OCT.