Nonlinear tissue characterization with intravascular ultrasound harmonic imaging

Current intravascular ultrasound (IVUS) techniques of tissue characterization are morphology based, a characterization based on surface echogenicity and spectral parameters from the fundamental imaging alone. Complex harmonic signals develop as a result of heterogeneous composition of nonlinear tissue such as calcified lipids, not easily characterized in the fundamental mode. Tissue harmonic imaging (THI) offers substantial advantages such as nonlinear information, better lateral resolution, higher contrast resolution, low near field spatial variation and decreased sidelobes. The objective is to enhance tissue characterization with the extent of tissue nonlinearity for a better diagnosis. Broadband high resolution focused polymer transducers enabling harmonic imaging were fabricated. Cadaver human aorta and rabbit aortic grafts with neointimal growth were imaged. Fundamental (at 20 MHz and 40 MHz) and Harmonic signals (at 40 MHz and 80 MHz) were obtained from the tissue in one scan line using a previously established pulse inversion technique with 60% bandwidth Gaussian pulses. The frequency spectrum from fundamental imaging was extended with additional parameters around the second harmonic frequency representative of tissue nonlinearity. A combined tissue nonlinear parameter was estimated from the harmonics. The image from fundamental mode was modulated with the tissue nonlinear parameter and color coded. Color coded images highlighted the relative differences in the nonlinearity of heterogeneous tissue. Histological correlation was done to estimate the significance of nonlinear tissue characterization. Transducers exhibited good axial resolution (~15 ?m) and broad bandwidth (~119%) facilitating harmonic imaging. Neointimal growth over the graft was observed more readily in the nonlinear imaging mode due to relative nonlinearity differences between the neointima and the graft. Nonlinear spectral parameters from THI better classify the tissue components as they exte- nd the set of parameters from fundamental imaging used for tissue classification in ´Virtual Histology´.