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Objective—To determine whether spectral analysis of unprocessed radiofrequency (RF) signal offers advantages over standard videodensitometric analysis in identifying the morphology of coronary atherosclerotic plaques.
Methods—97 regions of interest (ROI) were imaged at 30 MHz from postmortem, pressure perfused (80 mm Hg) coronary arteries in saline baths. RF data were digitised at 250 MHz. Two different sizes of ROI were identified from scan converted images, and relative amplitudes of different frequency components were analysed from raw data. Normalised spectra was used to calculate spectral slope (dB/MHz), y-axis intercept (dB), mean power (dB), and maximum power (dB) over a given bandwidth (17-42 MHz). RF images were constructed and compared with comparative histology derived from microscopy and radiological techniques in three dimensions.
Results—Mean power was similar from dense fibrotic tissue and heavy calcium, but spectral slope was steeper in heavy calcium (−0.45 (0.1)) than in dense fibrotic tissue (−0.31 (0.1)), and maximum power was higher for heavy calcium (−7.7 (2.0)) than for dense fibrotic tissue (−10.2 (3.9)). Maximum power was significantly higher in heavy calcium (−7.7 (2.0) dB) and dense fibrotic tissue (−10.2 (3.9) dB) than in microcalcification (−13.9 (3.8) dB). Y-axis intercept was higher in microcalcification (−5.8 (1.1) dB) than in moderately fibrotic tissue (−11.9 (2.0) dB). Moderate and dense fibrotic tissue were discriminated with mean power: moderate −20.2 (1.1) dB, dense −14.7 (3.7) dB; and y-axis intercept: moderate −11.9 (2.0) dB, dense −5.5 (5.4) dB. Different densities of fibrosis, loose, moderate, and dense, were discriminated with both y-axis intercept, spectral slope, and mean power. Lipid could be differentiated from other types of plaque tissue on the basis of spectral slope, lipid −0.17 (0.08). Also y-axis intercept from lipid (−17.6 (3.9)) differed significantly from moderately fibrotic tissue, dense fibrotic tissue, microcalcification, and heavy calcium. No significant differences in any of the measured parameters were seen between the results obtained from small and large ROIs.
Conclusion—Frequency based spectral analysis of unprocessed ultrasound signal may lead to accurate identification of atherosclerotic plaque morphology.
Keywords: tissue characterisation; intravascular ultrasound; spectral analysis; radiofrequency data