Dynamic Resolution Selection in Ultrasonic Strain Imaging

Abstract

Ultrasonic strain imaging promises to be a valuable tool in medical diagnostics. Reliability and ease-of-use have become important considerations. These depend on selection of appropriate imaging parameters. Two tasks are undertaken here. The tradeoff between resolution and estimation precision is examined closely to establish models for the relationships with imaging parameters and data properties. These models are then applied in a system that automatically sets the imaging parameters responsive to the data quality and the required estimation precision, so as to produce more meaningful images under varying scan conditions. The new system is applied to simulation, in vitro and in vivo data for validation. It reduces the complexity of the sonographer's role in strain imaging, and produces images of reliable quality even when the level of signal decorrelation varies throughout the ultrasound data.

An Intelligent Interface for Freehand Strain Imaging

“…We provide illustrations based on an example, in which displacement tracking is by Weighted Phase Separation [14] with Amplitude Modulation Correction [16], and axial strain estimation is performed by piecewise-linear least squares regression [11]. This offers a good demonstration, not primarily because of its competitive estimation accuracy, but more importantly because it has already been analysed and tested rigorously, resulting in a promising method for predicting the strain estimation variance [15,17]. Nonetheless, the aim of this paper is to describe our interface concept in general; the reader may envisage numerous specific applications.…”

“…In order to produce consistently meaningful images, these parameters need to be controlled locally so as to adjust for different conditions during the scan. [15] and [17] describe such a system. However, adjustment of parameters cannot alone overcome all of the difficulties associated with practical strain imaging.…”

“…The method for achieving these predictions is not critical, although the advantages of our concept are likely to be greatest if the precision predictions are highly accurate. The prediction method employed to produce results in this report is based on the work reported in [15].…”

“…In each least squares kernel, the overall strain estimation variance can be predicted by evaluating an average of the displacement variances weighted by the square of the distance from the kernel centre, dividing by the sum of squared distances. A more accurate estimate can be produced by applying a more complicated formula that accounts for the correlations between nearby errors [15]. This was not applied in the results that we present here -although it might bring a slight improvement in some cases -because it is less important when we only consider fixed analytical parameters, and we only require predictions of relative (rather than absolute) precision.…”

Uniform precision ultrasound strain imaging

“…However, this variation can be quantified, since it is straightforward to obtain a reasonable estimate of the precision (inverse of measurement variance) of each measurement (Lindop et al, 2008a). Strain images require some form of normalisation to convert the strain into a displayable range, and to reduce variation that is simply a result of variation in the applied stress (Lindop et al, 2008c).…”