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1.
Magn Reson Imaging ; 28(3): 441-50, 2010 Apr.
Article in English | MEDLINE | ID: mdl-20133097

ABSTRACT

Echo planar imaging (EPI) is an ultrafast magnetic resonance imaging (MRI) technique that allows one to acquire a 2D image in about 100 ms. Unfortunately, the standard EPI images suffer from substantial geometric distortions, mainly originating from susceptibility differences in adjacent tissues. To reduce EPI distortions, correction methods based on a field map, which is a map of the off-resonance frequencies, have been developed. In this work, a nonlinear least squares estimator is used to optimize the estimation of the field map of the B(0) field. The model of the EPI and reference data includes parameters for the phase evolution, the complex magnitude, the relaxation of the MRI signal and the EPI-specific phase difference between odd and even echoes, and from these parameters, additional corrections might be computed. The reference data required to estimate the field map can be acquired with a modified EPI-sequence. The proposed method is tested on simulated as well as experimental data and proves to be significantly more robust against noise, compared to the previously suggested method.


Subject(s)
Algorithms , Echo-Planar Imaging/methods , Image Enhancement/methods , Image Interpretation, Computer-Assisted/methods , Echo-Planar Imaging/instrumentation , Phantoms, Imaging , Reproducibility of Results , Sensitivity and Specificity
2.
PLoS One ; 3(9): e3184, 2008 Sep 10.
Article in English | MEDLINE | ID: mdl-18781203

ABSTRACT

BACKGROUND: Male songbirds learn their songs from an adult tutor when they are young. A network of brain nuclei known as the 'song system' is the likely neural substrate for sensorimotor learning and production of song, but the neural networks involved in processing the auditory feedback signals necessary for song learning and maintenance remain unknown. Determining which regions show preferential responsiveness to the bird's own song (BOS) is of great importance because neurons sensitive to self-generated vocalisations could mediate this auditory feedback process. Neurons in the song nuclei and in a secondary auditory area, the caudal medial mesopallium (CMM), show selective responses to the BOS. The aim of the present study is to investigate the emergence of BOS selectivity within the network of primary auditory sub-regions in the avian pallium. METHODS AND FINDINGS: Using blood oxygen level-dependent (BOLD) fMRI, we investigated neural responsiveness to natural and manipulated self-generated vocalisations and compared the selectivity for BOS and conspecific song in different sub-regions of the thalamo-recipient area Field L. Zebra finch males were exposed to conspecific song, BOS and to synthetic variations on BOS that differed in spectro-temporal and/or modulation phase structure. We found significant differences in the strength of BOLD responses between regions L2a, L2b and CMM, but no inter-stimuli differences within regions. In particular, we have shown that the overall signal strength to song and synthetic variations thereof was different within two sub-regions of Field L2: zone L2a was significantly more activated compared to the adjacent sub-region L2b. CONCLUSIONS: Based on our results we suggest that unlike nuclei in the song system, sub-regions in the primary auditory pallium do not show selectivity for the BOS, but appear to show different levels of activity with exposure to any sound according to their place in the auditory processing stream.


Subject(s)
Finches/physiology , Magnetic Resonance Imaging/methods , Prosencephalon/anatomy & histology , Acoustic Stimulation/methods , Animal Communication , Animals , Auditory Cortex/physiology , Auditory Pathways/physiology , Image Processing, Computer-Assisted , Male , Neurons/metabolism , Prosencephalon/physiology , Sound , Telencephalon/physiology , Time Factors , Vocalization, Animal/physiology
3.
Phys Med Biol ; 52(5): 1335-48, 2007 Mar 07.
Article in English | MEDLINE | ID: mdl-17301458

ABSTRACT

Estimation of the noise variance of a magnetic resonance (MR) image is important for various post-processing tasks. In the literature, various methods for noise variance estimation from MR images are available, most of which however require user interaction and/or multiple (perfectly aligned) images. In this paper, we focus on automatic histogram-based noise variance estimation techniques. Previously described methods are reviewed and a new method based on the maximum likelihood (ML) principle is presented. Using Monte Carlo simulation experiments as well as experimental MR data sets, the noise variance estimation methods are compared in terms of the root mean squared error (RMSE). The results show that the newly proposed method is superior in terms of the RMSE.


Subject(s)
Artifacts , Artificial Intelligence , Data Interpretation, Statistical , Image Interpretation, Computer-Assisted/methods , Magnetic Resonance Imaging/methods , Pattern Recognition, Automated/methods , Image Enhancement/methods , Likelihood Functions , Reproducibility of Results , Sensitivity and Specificity
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