Results 1 - 10 of 8,741

Brain magnetic resonance imaging with contrast dependent on blood oxygenation.

by S Ogawa , T M Lee , A R Kay , D W Tank - Proc. Natl. Acad. Sci. USA , 1990
"... ABSTRACT Paramagnetic deoxyhemoglobin in venous blood is a naturally occurring contrast agent for magnetic resonance imaging (MRI). By accentuating the effects of this agent through the use of gradient-echo techniques in high fields, we demonstrate in vivo images of brain microvasculature with imag ..."
Abstract - Cited by 648 (1 self) - Add to MetaCart
to regional neural activity. Magnetic resonance imaging (MRI) is a widely accepted modality for providing anatomical information. Current research (1) involves extending MRI methods to provide information about biological function, in addition to the concomitant anatomical information. In addition

Multimodality Image Registration by Maximization of Mutual Information

by Frederik Maes, André Collignon, Dirk Vandermeulen, Guy Marchal, Paul Suetens - IEEE TRANSACTIONS ON MEDICAL IMAGING , 1997
"... A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or in ..."
Abstract - Cited by 791 (10 self) - Add to MetaCart
and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomography (PET) images by comparison with the stereotactic registration

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

by Yongyue Zhang, Michael Brady, Stephen Smith - IEEE TRANSACTIONS ON MEDICAL. IMAGING , 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limi ..."
Abstract - Cited by 639 (15 self) - Add to MetaCart
The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic

HAMMER: hierarchical attribute matching mechanism for elastic registration

by Dinggang Shen, Christos Davatzikos - IEEE Trans. on Medical Imaging , 2002
"... A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate remarkably high accuracy in superposition of images from different subjects, thus enabling very precise localization of morphological cha ..."
Abstract - Cited by 278 (95 self) - Add to MetaCart
A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate remarkably high accuracy in superposition of images from different subjects, thus enabling very precise localization of morphological

Analysis of fMRI Data by Blind Separation Into Independent Spatial Components

by Martin J. Mckeown, Scott Makeig, Greg G. Brown, Tzyy-ping Jung, Sandra S. Kindermann, Anthony J. Bell, Terrence J. Sejnowski - HUMAN BRAIN MAPPING 6:160–188(1998) , 1998
"... Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent comp ..."
Abstract - Cited by 317 (18 self) - Add to MetaCart
Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent

Adaptive fuzzy segmentation of magnetic resonance images

by Dzung L. Pham, Jerry L. Prince - IEEE TRANS. MED. IMAG , 1999
"... An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-me ..."
Abstract - Cited by 158 (10 self) - Add to MetaCart
An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C

Magnetic resonance image tissue classification using a partial volume model

by David W. Shattuck, Stephanie R. Sandor-Leahy, Kirt A. Schaper, David A. Rottenberg, Richard M. Leahy - NEUROIMAGE , 2001
"... We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for imag ..."
Abstract - Cited by 137 (6 self) - Add to MetaCart
We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate

Analysis of Functional MRI Time-Series

by K. J. Friston, P. Jezzard, R. Turner - HUMAN BRAIN MAPPING , 1994
"... A method for detecting significant and regionally specific correlations between sensory input and the brain's physiological response, as measured with functional magnetic resonance imaging (MRI), is presented in this paper. The method involves testing for correlations between sensory input and ..."
Abstract - Cited by 274 (10 self) - Add to MetaCart
A method for detecting significant and regionally specific correlations between sensory input and the brain's physiological response, as measured with functional magnetic resonance imaging (MRI), is presented in this paper. The method involves testing for correlations between sensory input

Automated model-based tissue classification of MR images of the brain

by Koen Van Leemput, Frederik Maes, Dirk Vandermeulen, Paul Suetens , 1999
"... We describe a fully automated method for model-based tissue classification of Magnetic Resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multi ..."
Abstract - Cited by 214 (14 self) - Add to MetaCart
We describe a fully automated method for model-based tissue classification of Magnetic Resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single

Sequence-independent segmentation of magnetic resonance images

by Bruce Fischl, David H. Salat, A Nikos Makris, Florent Ségonne, B Brian T. Quinn, Anders M. Dale A - Neuroimage , 2004
"... We present a set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm. This results in substantial invariance to acquisition parameters, as the effect of these parameters on the contras ..."
Abstract - Cited by 112 (20 self) - Add to MetaCart
We present a set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm. This results in substantial invariance to acquisition parameters, as the effect of these parameters
Results 1 - 10 of 8,741