PubMed Health⌕ Search

PubMed · 7583366

Endoscopic-image display system mounted on the surgical microscope.

Abstract

This report describes a newly developed endoscopic-image display system which is mounted on the optical unit of the surgical microscope. This system permits the neurosurgeon to watch the endoscopic image through one of the eye lenses of a surgical microscope during application of neurosurgical endoscopy under open microsurgical procedures. It frees the neurosurgeon from the major conventional inconvenience that he has to discontinue watching the microscopic view in order to look at the endoscopic images through the ocular of the endoscope or on the video monitor.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M Taneda, A Kato, T Yoshimine, T Hayakawa. 1995. Endoscopic-image display system mounted on the surgical microscope.. https://doi.org/10.1055/s-2008-1053463

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

KEEP EXPLORING

Related citations

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↗