PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 3915644

CT-guided ablative stereotaxis without ventriculography.

L V Laitinen
View original source ↗Export metadata ↓

Abstract

A new technique is described which permits all types of stereotactic surgery to be done without ventriculography, with CT guidance only. The present series consists of 42 patients who underwent thalamotomy, posteromedial hypothalamotomy, dentatotomy or anterior capsulotomy for movement disorders, chronic pain, spasmodic torticollis or psychiatric illness. Postoperative lesion control with repeated CT showed that the method was accurate. The clinical results were considered to be better than those after previous ventriculography-guided surgery. It is concluded that ventriculography is no longer needed for stereotactic neurosurgery.

Explore related subjects

Brain DiseasesCerebral VentriculographyHumansStereotaxic TechniquesTomography, X-Ray ComputedJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

L V Laitinen. 1985. CT-guided ablative stereotaxis without ventriculography.. https://doi.org/10.1159/000101091

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↗