Improving three-dimensional mechanical imaging of breast lesions with principal component analysis.

TLDR: Three dimensional shear modulus images of benign breast lesions for two subjects were generated and it was found that the lesions were visualized more clearly in images generated using the displacement data de‐noised through the use of principal components.

Abstract: Purpose Elastography has emerged as a new tool for detecting and diagnosing many types of diseases including breast cancer. To date, most clinical applications of elastography have utilized two-dimensional strain images. The goal of this paper is to present a new quasi-static elastography technique that yields shear modulus images in three dimensions Methods An automated breast volume scanner was used to acquire ultrasound images of the breast as it was gently compressed. Cross-correlation between successive images was used to determine the displacement within the tissue. The resulting displacement field was filtered of all but compressive motion through principal component analysis. This displacement field was usedto infer spatial distribution of shear modulus by solving a 3D elastic inverse problem. Results Three dimensional shear modulus images of benign breast lesions for two subjects were generated using the techniques described above. It was found that the lesions were visualized more clearly in images generated using the displacement data de-noised through the use of principal components. Conclusions We have presented experimental and algorithmic techniques that lead to three dimensional imaging of shear modulus using quasi-static elastography. This work demonstrates feasibility of this approach, and lays the foundation for images of other, more informative, mechanical parameters. This article is protected by copyright. All rights reserved.