A Systematized Review of Quantitative Ultrasound Based on First-Order Speckle Statistics

Abstract

Since the late 1970s, the speckle interference patterns ubiquitous in pulse-echo ultrasound images have been used to characterize subresolution tissue structures. During this time, new models, estimation methods, and processing techniques have proliferated, offering a wealth of recommendations for the task of tissue characterization. A literature review was performed to draw attention to these various methods and to critically track assumptions and gaps in knowledge. A total of 388 articles were collected from a systematic search for first-order speckle statistics in diagnostic ultrasound in the NIH PubMed database and Elsevier's Scopus database. Articles were grouped by basic characteristics and evaluated for addressing fundamental assumptions. A sampling of models and methods is presented to reveal the state of the art in speckle statistics as well as sources of measurement error and other important considerations. While this body of literature emphasizes the value of speckle analysis in diagnostic ultrasound, it is shown that relatively little attention is devoted to basic assumptions such as the linearity of system response and scatterer geometry. Additionally, several areas of investigation are available to improve upon speckle statistics analysis, potentially leading to the advancement of this unique tool.

Fig. 1.

The three main scenarios discernable with first-order speckle statistics are the pre-Rayleigh caused by sparse or clustered scatterers, Rayleigh caused by diffuse scatterers, and post-Rayleigh caused by aligned or periodically-arranged scatterers. These scenarios exist within the context of the resolutional unit of the acoustic beam, i.e. the resolution cell. The resolutional unit, here depicted as an ellipse, is defined as the region in space that contributes to a single received signal sample.

Fig. 2.

Envelope statistics analysis in diagnostic ultrasound begins with defining a region of interest (ROI) in which speckle statistics estimates, $\widehat{\theta }$, will be calculated. The ROI may further be subdivided into smaller parameter estimation regions (PERs) in which the histogram of signal amplitudes will be characterized. In many cases, PERs are allowed to overlap to improve parametric image resolution. An image of speckle estimates may be formed from the parameters calculated in each PER, ${\widehat{\theta }}_{\mathit{i},\mathit{j}}$.

Fig. 3.

Number of articles employing each main speckle statistics model. The sum is greater than the total number of articles found because many publications employ more than one model.

Fig. 4.

Number of articles focused on each tissue application. The sum is greater than the total number of articles found because many publications apply analysis to more than one tissue.

Fig. 5.

Number of articles included in the search by year of publication.

Fig. 6.

The depth-dependence of resolution has significant effects on speckle statistics. The resolutional unit, here depicted as an ellipsoid, is defined as the region in space that contributes to a single received signal sample. Strong beam divergence will cause resolution cells to encompass more scatterers as depth increases, and speckle statistics will approach the Rayleigh regime. Conversely, if the resolution cell maintains constant size with respect to depth, speckle statistics will be more consistent and maintain sensitivity to microstructural changes. The “scatterer” fields are identical on the left and right of this figure.