Three-dimensional isotropic wavelets for post-acquisitional extraction of latent images of atherosclerotic plaque components from micro-computed tomography of human coronary arteries.

RATIONALE AND OBJECTIVES

The capability of wavelet transforms to separate signals into frequency bands is the basis for its use in image compression and storage, data management and transmission, and, recently, extraction of latent images of tissue components from noisy medical images. Analysis of temporal variations of radiofrequency backscatter of intravascular ultrasound with one-dimensional wavelets can detect lipid-laden plaque in coronary arteries with a sensitivity and specificity of >80%. In this study we evaluate the capability of a novel, 3-dimensional isotropic wavelet analysis to perform high resolution, non-directionally biased, statistically reliable, non-invasive discrimination between components of human coronary atherosclerotic plaques in micro-CT.

MATERIALS AND METHODS

Coronary artery segments (5-15 mm) were excised at necropsy from 18 individuals with advanced coronary atherosclerosis. Specimens were imaged using a GE Locus SP ex vivo micro-CT scanner and processed for histological correlation (833 sections). The isotropic wavelet constructs were applied to the entire volume of CT data of each arterial segment to distinguish tissue textures of varying scales and intensities. Voxels were classified and plaque characterization achieved by comparing the relative magnitudes of these wavelet constituents to that of several reference plaque tissue components.

RESULTS

Processing of micro-CT images via these isotropic wavelet algorithms permitted 3-D, color-coded, high resolution, digital discrimination between lumen, calcific deposits, lipid-rich deposits, and fibromuscular tissue providing detail not possible with conventional thresholding based on Hounsfield intensity units. Using the isotropic wavelets (with histology as the gold standard), lipid-rich pools approaching the size of the filter for the isotropic wavelet algorithm (0.25 mm [250 microns] in length) were identified with 81% sensitivity and 86% specificity. Calcific deposits, fibromuscular tissue, and lumen equal to or larger than the wavelet filter size were detected without error (100% sensitivity and specificity).

CONCLUSION

Isotropic wavelet analysis permits high resolution, multi-dimensional identification of coronary atherosclerotic plaque components in micro-CT with sensitivity and specificity similar to that achieved with data obtained invasively (from IVUS in vivo) using one-dimensional wavelets. Further studies are necessary to test the applicability of this technology to clinical, multi-detector scanners.