Three-dimensional Acoustic Tissue Model: a Computational Tissue Phantom for Image Analyses

Abstract

A novel methodology to obtain three-dimensional (3D) acoustic tissue models (3DATMs) is introduced. 3DATMs can be used as computational tools for ultrasonic imaging algorithm development and analysis. In particular, 3D models of biological structures can provide great benefit to better understand fundamentally how ultrasonic waves interact with biological materials. As an example, such models were used to generate ultrasonic images that characterize tumor tissue microstructures. 3DATMs can be used to evaluate a variety of tissue types. Typically, excised tissue is fixed, embedded, serially sectioned, and stained. The stained sections are digitally imaged (24-bit bitmap) with light microscopy. Contrast of each stained section is equalized and an automated registration algorithm aligns consecutive sections. The normalized mutual information is used as a similarity measure, and simplex optimization is conducted to find the best alignment. Both rigid and non-rigid registrations are performed. During tissue preparation, some sections are generally lost; thus, interpolation prior to 3D reconstruction is performed. Interpolation is conducted after registration using cubic Hermite polynoms. The registered (with interpolated) sections yield a 3D histologic volume (3DHV). Acoustic properties are then assigned to each tissue constituent of the 3DHV to obtain the 3DATMs. As an example, a 3D acoustic impedance tissue model (3DZM) was obtained for a solid breast tumor (EHS mouse sarcoma) and used to estimate ultrasonic scatterer size. The 3DZM results yielded an effective scatterer size of 32.9 (±6.1) μm. Ultrasonic backscatter measurements conducted on the same tumor tissue in vivo yielded an effective scatterer size of 33 (±8) μm. This good agreement shows that 3DATMs may be a powerful modeling tool for acoustic imaging applications

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Cite this paper

@inproceedings{Mamou2007ThreedimensionalAT, title={Three-dimensional Acoustic Tissue Model: a Computational Tissue Phantom for Image Analyses}, author={Jonathan Mamou}, year={2007} }