Noninvasive Assessment of Liver Diseases using 2D Shear Wave Elastography.

There has been great interest in the development of non-invasive techniques for the diagnosis of liver fibrosis in chronic liver diseases, including ultrasound elastographic methods. Some of these methods have already been adequately studied for the non-invasive assessment of diffuse liver diseases. Others, however, such as two-dimensional Shear Wave Elastography (SWE), of more recent appearance, have yet to be validated and some aspects are for the moment incompletely elucidated. This review discusses some of the aspects related to two-dimensional SWE: the examination technique, the examination performance indicators, intra and interobserver agreement and clinical applications. Recommendations for a high-quality examination technique are formulated.

Maximum-likelihood based synthesis of volumetric textures from a 2D sample.

We propose a genuine 3D texture synthesis algorithm based on a probabilistic 2D Markov random field conceptualization, capable of capturing the visual characteristics of a texture into a unique statistical texture model. We intend to reproduce, in the volumetric texture, the interactions between pixels learned in an input 2D image. The learning is done by nonparametric Parzen-windowing. Optimization is handled voxel by a relaxation algorithm, aiming at maximizing the likelihood of each voxel in terms of its local conditional probability function. Variants are proposed regarding the relaxation algorithm and the heuristic strategies used for the simultaneous handling of the orthogonal slices containing the voxel. The procedures are materialized on various textures through a comparative study and a sensitivity analysis, highlighting the variants strengths and weaknesses. Finally, the probabilistic model is compared objectively with a nonparametric neighborhood-search-based algorithm.