Quantitative 3D Analysis of Coronary Wall Morphology in Heart Transplant Patients: OCT-Assessed Cardiac Allograft Vasculopathy Progression

Abstract

Cardiac allograft vasculopathy (CAV) accounts for about 30% of all heart-transplant (HTx) patient deaths. For patients at high risk for CAV complications after HTx, therapy must be initiated early to be effective. Therefore, new phenotyping approaches are needed to identify such HTx patients at the earliest possible time. Coronary optical coherence tomography (OCT) images were acquired from 50 HTx patients 1 and 12 months after HTx. Quantitative analysis of coronary wall morphology used LOGISMOS segmentation strategy to simultaneously identify three wall-layer surfaces for the entire pullback length in 3D: luminal, outer intimal, and outer medial surfaces. To quantify changes of coronary wall morphology between 1 and 12 months after HTx, the two pullbacks were mutually co-registered. Validation of layer thickness measurements showed high accuracy of performed layer analyses with layer thickness measures correlating well with manually-defined independent standard (Rautomated2 = 0.93, y=1.0x-6.2μm), average intimal+medial thickness errors were 4.98 ± 31.24 µm, comparable with inter-observer variability. Quantitative indices of coronary wall morphology 1 month and 12 months after HTx showed significant local as well as regional changes associated with CAV progression. Some of the newly available fully-3D baseline indices (intimal layer brightness, medial layer brightness, medial thickness, and intimal+medial thickness) were associated with CAV-related progression of intimal thickness showing promise of identifying patients subjected to rapid intimal thickening at 12 months after HTx from OCT-image data obtained just 1 month after HTx. Our approach allows quantification of location-specific alterations of coronary wall morphology over time and is sensitive even to very small changes of wall layer thicknesses that occur in patients following heart transplant.

Keywords: CAV prediction; CAV progression; Cardiac allograft vasculopathy (CAV); LOGISMOS; optical coherence tomography (OCT).

Copyright © 2018 Elsevier B.V. All rights reserved.

Figures

Figure 1:

Automated OCT segmentation followed by JEI yielding clinically acceptable 3D segmentation of coronary wall layers. (a) Original cross-sectional and axial views of a 3D OCT dataset. (b) Automated 3-surface 3D LOGISMOS approach shows a regional segmentation inaccuracy (arrows) with lumen in red, outer intima in green and outer media in orange. (c) JEI interactions shown in turquoise color provide a suggested position for the outer media (orange) surface in the axial view. (d) Multi-surface 3D segmentation is re-optimized every time a set of correction points is provided – the few identified points shown completely corrected the inaccuracy in 3D. Note that all JEI modifications are optional such that the full segmentation workflow can be completed either without any interaction (fully automated) or using human expertise to guide the segmentation via JEI when needed. Important to realize is that the experts interact with the algorithm, they never directly retrace the borders in the image.

Figure 2:

OCT–OCT registration process results in axial and orientation match between individual locations of two independent OCT pullbacks depicting the same coronary vessel segment. White vertical lines present matching landmarks. Corresponding frames in 12-month pullback are determined and properly rotated to achieve locational registration.

Figure 3:

Feature carpets of intimal thickness (IT) and thickening. Mutually registered intimal thickness at 1 and 12 months (M), and their differences shown color coded on an axially unwrapped vessel wall. Shadowed areas represent non-measurable exclusion regions caused by guidewire shadow, residual blood, intimal layer thickness exceeding OCT penetration depth, etc. Rightmost chart in each panel shows frame-based IT progression, blue straight line shows a linear fit of frame-based IT progression along the co-registered vessel portion.

Figure 4:

OCT segmentation. (a) Original OCT image (one frame of a 540-frame long pullback). (b) Expert-defined independent standard. (c) Result of the automated multi-layer segmentation with automatically-determined exclusion region (12 to 4 o’clock).

Figure 5:

Distribution of local and regional (3 mm long vessel segments) intimal thickness across the cohort at 1-month and 12-month (80,600 local and 13 regional measurements per pullback on average; e.g., histograms in panels (a) and (c) are based on over 4 million locally co-registered measurements). (a) Histograms of local IT. (b) Histograms of regional IT. (c) Histogram of local ΔIT. (d) Histogram of regional ΔIT. Note the clear indication of intimal thickening between 1M and 12M, both locally and regionally.

Figure 6:

Distribution of local and regional (3 mm long vessel segments) intimal thickness across the cohort at 1-month and 12-month (80,600 local and 13 regional measurements per pullback on average). (a) Histograms of local IMratio. (b) Histograms of regional IMratio. (c) Histogram of local ΔIMratio. (d) Histogram of regional ΔIMratio. Note the clear indication of increased IMratio between 1M and 12M, both locally and regionally.