Spectral strain estimation techniques for tissue elasticity imaging

Abstract

This research project examines the applicability of novel spectral domain strain estimation algorithms to improve the clinical value of tissue elasticity images. Pathological changes can be frequently correlated to changes in soft tissue stiffness. Despite the fact that many cancers, including those found in the breast and prostate, often manifest themselves as stiff focal lesions, there location and shape can make them difficult, if not impossible, to detect during a palpation-based physical examination. The mechanical properties of tissue cannot be measured directly using any current imaging modality. However, an ultrasound-based technique known as elastography has evolved over the last fifteen years which is capable of estimating relative strain distributions in soft tissue. Under certain conditions, these strain images (elastograms) can give a clear depiction of the underlying tissue stiffness distributions, and thus, can be used as a clinical tool for the detection of pathological lesions. Conventionally elastographic techniques estimate tissue strain by tracking spatial features found in congruent pairs of ultrasonic echo backscattered signals before and after a small, quasistatic compression is applied to the tissue surface via the ultrasound transducer. Although this technique has shown promise from a clinical perspective in detecting both benign and malignant lesions of the breast and prostate, it is sensitive to extraneous motions that ultimately compromise the strain estimation procedure. To alleviate this problem, the applicability of spectral-domain strain estimation algorithms and techniques were examined. Both simulation and experimental (in vitro) results obtained using an elastographic phantom indicate that this approach holds promise to improve the clinical value of the images produced. Also, initial results obtained using a novel elastographic animal model further support the efficacy of spectral-based strain imaging in vivo.%%%%Ph.D., Bioengineering – Drexel University, 2005