Deconvolution and elastography based on three-dimensional ultrasound

This paper is in two parts and addresses two ways of getting more information out of the RF signal from a three-dimensional (3D) mechanically-swept medical ultrasound scanner. The first topic is the use of non-blind deconvolution to improve the clarity of the data, particularly in the direction perpendicular to the individual B-scans. The second topic is strain imaging. We present a robust and efficient approach to the estimation and display of axial strain information. For deconvolution, we calculate an estimate of the point-spread function at each depth in the image using Field II. This is used as part of an Expectation Maximisation (EM) framework in which the ultrasound scatterer field is modelled as the product of (a) a piecewise smooth function and (b) a fine-grain varying function. In the E step, a Wiener filter is used to estimate the scatterer field based on an assumed piecewise smooth component. In the M step, wavelet de-noising is used to estimate the piecewise smooth component from the scatterer field. For strain imaging, we use a quasi-static approach with efficient phase-based algorithms. Our contributions lie in robust and efficient 3D displacement tracking, point-wise quality-weighted averaging, and a stable display that shows not only strain but also an indication of the quality of the data at each point in the image. This enables clinicians to see where the strain estimate is meaningful and where it is mostly noise. For deconvolution, we present in-vivo images and simulations with quantitative performance measures. With the blurred 3D data taken as 0 dB, we get an improvement in signal to noise ratio of 4.6 dB with a Wiener filter alone, 4.36 dB with the ForWaRD algorithm and 5.18 dB with our EM algorithm. For strain imaging we show images based on 2D and 3D data and describe how full 3D analysis can be performed in about 20 seconds on a typical computer. We will also present initial results of our clinical study to explore the applications of our s- - ystem in our local hospital.