Learning to estimate out-of-plane motion in ultrasound imagery of real tissue.

In freehand 3D ultrasound (US), the relative positions and orientations of the 2D US images are usually obtained from a position tracking device, at the expense of clinical convenience. As an alternative or complement to this approach, transducer motion can be inferred from image content, using image registration techniques to recover in-plane motion and speckle decorrelation to recover out-of-plane motion. One difficulty with the speckle decorrelation approach is that for real tissues, the rate of speckle decorrelation is not only transducer dependent, but also medium dependent. This paper proposes a novel method for estimating the elevational correlation length of US signals in such media by learning its relationship to in-plane image statistics from a pool of synthetic US imagery generated from virtual phantoms of varied micro-structure. Learning takes place within a sparse Gaussian process regression framework. In experiments with synthetic US imagery and real imagery of animal tissue, the approach is shown to generalise well across transducer and medium changes, with performance better than a method based on speckle classification and comparable to our implementation of the heuristic state-of-the-art method. The proposed approach better lends itself to improvement through the creation of more realistic training sets.