PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 3439901

[Dandy-Walker syndrome].

L S Khodasevich·N M Kabeeva·I V Zuev
View original source ↗Export metadata ↓

Abstract

Two cases of this syndrome occurring in children aged 1.5 and 3 years are reported. In one case, no other abnormalities were present, and the deformity did not manifest itself clinically before the child had fallen from a height. In the other, a cyst of the fourth ventricle coexisted with other abnormalities in the brain which were responsible for neurologic symptoms from the very birth and also, probably, for the atresia of draining orifices.

Explore related subjects

Brain DiseasesChild, PreschoolCystsDandy-Walker SyndromeFemaleHumansHydrocephalusInfantMaleCase ReportsEnglish AbstractJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

L S Khodasevich, N M Kabeeva, I V Zuev. 1987. [Dandy-Walker syndrome].. https://pubmed.ncbi.nlm.nih.gov/3439901/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

A penalized likelihood approach to magnetic resonance image reconstruction.

Currently, images acquired via magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) technology are reconstructed using the discrete inverse Fourier transform. While computationally convenient, this approach is not able to filter out noise. This is a serious limitation because the amount of noise in MRI and fMRI can be substantial. In this paper, we propose an alternative approach to reconstruction, based on penalized likelihood methodology. In particular, we focus on non-linear shrinkage estimators and show that this approach achieves a great reduction in integrated mean squared error (IMSE) of the estimated image with respect to the currently used estimator. This approach is extremely fast and easy to implement computationally. In addition, it can be combined with various alternative approaches to MR image reconstruction and can be easily adapted to other, non-MRI contexts, in which the observed data and the quantities of interest are related via a linear transform.

Brain Diseases↗