A novel Bayesian-based nonlocal reconstruction method for freehand 3D ultrasound imaging

Abstract

Freehand three-dimensional (3D) ultrasound imaging is an important medical imaging modality in computer-assisted clinical diagnosis and image-guided intervention. In this paper, we present a novel Bayesian-based nonlocal method for the accurate volume reconstruction of freehand 3D ultrasound imaging with irregularly spaced B-scans. In the algorithm, each pixel is represented as the Gamma distribution which corresponds to the speckle noise generated by the interaction of the acoustic wave with the tissues. The variational reconstruction functional is associated with a nonlocal denoising term and a nonlocal inpainting term. To suppress speckle noise in the ultrasound image, the observed data is filtered via nonlocal total variation method firstly. The nonlocal denoising model is adapted to the speckle noise by substituting the Pearson distance-based weight function for the Gaussian weight function. To interpolate the missing data, a new inpainting scheme derived from the nonlocal means filter and its implementation based on fast marching method are introduced to fill the empty regions. This makes interpolation of missing data more accurate and effective. The Pearson distance function derived from the Bayesian estimator is not only used for speckle reduction, but also serves as weight function for building nonlocal means-based inpainting algorithm. Experimental results on synthetic cube data, in-vitro ultrasound abdominal phantom and in-vivo liver of human subject and comparisons with some classical and recent algorithms are used to demonstrate its improvement in both speckle suppression and edge preservation in 3D ultrasound reconstruction.