A multiscale algorithm for three-dimensional free-hand ultrasound.

This paper presents a multiscale algorithm for the reconstruction of human anatomy from a set of ultrasound (US) images. Reconstruction is formulated in a Bayesian framework as an optimization problem with a large number of unknown variables. Human tissues are represented by the interpolation of coefficients associated to the nodes of a 3-D cubic grid. The convergence of the Bayesian method is usually slow and initialization dependent. In this paper, a multiscale approach is proposed to increase the convergence rate of the iterative process of volume estimation. A coarse estimate of the volume is first obtained using a cubic grid with a small number of nodes initialized with a constant value computed from the observed data. The volume estimate is then recursively improved by refining the grid step. Experimental results are provided to show that multiscale method achieves faster convergence rates compared with a single-scale approach. This is the key improvement toward real-time implementations. Experimental results of 3-D reconstruction of human anatomy are presented to assess the performance of the algorithm and comparisons with the single-scale method are presented.