PubMed Health⌕ Search

PubMed · 7786921

A bioimaging integration system implemented for neurological applications.

Abstract

A system aimed at the management and fusion of multimodal biomedical images, including X-ray computed tomography, magnetic resonance imaging, positron emission tomography, and single photon emission computed tomography, has been implemented for neurological applications. This bioimaging integration system (BIS) consists of a network for image transmission from acquisition machines to dedicated image processing workstations, a software library for image standardization, and an image registration technique to project multimodal volumetric images into a common reference space. The registration procedure was evaluated in MRI/PET correlation studies, in which misalignment errors of 2.6 mm in the xy transaxial plane and 3.4 mm along the z axis were found. BIS has been validated for the anatomical-functional correlation analysis of MRI and PET images in neurological research protocols and clinical studies.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

G Rizzo, M C Gilardi, A Prinster, G Lucignani, V Bettinardi, F Triulzi, A Cardaioli, S Cerutti, F Fazio. 1994. A bioimaging integration system implemented for neurological applications.. https://pubmed.ncbi.nlm.nih.gov/7786921/

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↗