Non-infectious meningitis and CNS demyelinating diseases: A conceptual review.

Many cases of aseptic meningitis or meningoencephalitis, unresponsive to antimicrobial treatments, have been reported recently in patients with established/new-onset central nervous system (CNS) inflammatory demyelinating diseases (CNSIDDs). Given the higher probability of infectious etiologies, CNSIDDs are rarely considered among the differentials in meningitis or meningoencephalitis cases. We gathered and tabulated cases of non-infectious, steroid-responsive meningitis or meningoencephalitis associated with neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein-associated disease (MOGAD). This conceptual review highlights the need to bolster routine infectious workups with immunological workups in cases of meningoencephalitis or meningitis where potential autoimmune etiologies can be suspected. Although differentiating CNSIDDs with meningeal involvement from infectious meningitis may not substantially affect acute treatment strategies, long-term management and follow-up of the two are entirely different. We also discuss future research directions and hypotheses on how CNSIDDs may be associated with meningitis-like presentations, e.g. overlapping glial fibrillary acidic protein astrocytopathy or autoimmune encephalitis, alterations in regulatory T-helper cells function, and undetected viral agents.

Bayesian winner-take-all reconstruction of intermediate views from stereoscopic images.

This paper presents a new algorithm for the reconstruction of intermediate views from a pair of still stereoscopic images. The algorithm is designed to address the issue of blur caused by linear filtering often employed in such reconstruction. The proposed algorithm is block-based and to reconstruct the intermediate views employs nonlinear disparity-compensated filtering by means of a winner-take-all strategy. The reconstructed image is modeled as a tiling by fixed-size blocks coming from various positions (disparity compensation) of either the left or right images, while the tiling map itself is modeled by a binary decision field. In addition to that, an observation model relating the left and right images via a disparity field, and a disparity field model are used. All models are probabilistic and are combined into a maximum a posteriori probability criterion. The intermediate intensities, disparities and the binary decision field are estimated jointly using the expectation-maximization algorithm. The new approach is compared experimentally on complex natural images with a reference block-based algorithm employing linear filtering. Although the improvements are localized and often subtle, they demonstrate that a high-quality intermediate view reconstruction for complex scenes is feasible.