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DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application

Background. Magnetic Resonance (MR) diffusion tensor imaging (DTI) is able to quantify in vivo tissue microstructure properties and to detect disease related pathology of the central nervous system. Nevertheless, DTI is limited by low spatial resolution associated with its low signal-to-noise-ratio...

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Autores principales: Laganà, M., Rovaris, M., Ceccarelli, A., Venturelli, C., Marini, S., Baselli, G.
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2804108/
https://www.ncbi.nlm.nih.gov/pubmed/20069121
http://dx.doi.org/10.1155/2010/254032
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author Laganà, M.
Rovaris, M.
Ceccarelli, A.
Venturelli, C.
Marini, S.
Baselli, G.
author_facet Laganà, M.
Rovaris, M.
Ceccarelli, A.
Venturelli, C.
Marini, S.
Baselli, G.
author_sort Laganà, M.
collection PubMed
description Background. Magnetic Resonance (MR) diffusion tensor imaging (DTI) is able to quantify in vivo tissue microstructure properties and to detect disease related pathology of the central nervous system. Nevertheless, DTI is limited by low spatial resolution associated with its low signal-to-noise-ratio (SNR). Aim. The aim is to select a DTI sequence for brain clinical studies, optimizing SNR and resolution. Methods and Results. We applied 6 methods for SNR computation in 26 DTI sequences with different parameters using 4 healthy volunteers (HV). We choosed two DTI sequences for their high SNR, they differed by voxel size and b-value. Subsequently, the two selected sequences were acquired from 30 multiple sclerosis (MS) patients with different disability and lesion load and 18 age matched HV. We observed high concordance between mean diffusivity (MD) and fractional anysotropy (FA), nonetheless the DTI sequence with smaller voxel size displayed a better correlation with disease progression, despite a slightly lower SNR. The reliability of corpus callosum (CC) fiber tracking with the chosen DTI sequences was also tested. Conclusions. The sensitivity of DTI-derived indices to MS-related tissue abnormalities indicates that the optimized sequence may be a powerful tool in studies aimed at monitoring the disease course and severity.
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spelling pubmed-28041082010-01-12 DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application Laganà, M. Rovaris, M. Ceccarelli, A. Venturelli, C. Marini, S. Baselli, G. Comput Intell Neurosci Research Article Background. Magnetic Resonance (MR) diffusion tensor imaging (DTI) is able to quantify in vivo tissue microstructure properties and to detect disease related pathology of the central nervous system. Nevertheless, DTI is limited by low spatial resolution associated with its low signal-to-noise-ratio (SNR). Aim. The aim is to select a DTI sequence for brain clinical studies, optimizing SNR and resolution. Methods and Results. We applied 6 methods for SNR computation in 26 DTI sequences with different parameters using 4 healthy volunteers (HV). We choosed two DTI sequences for their high SNR, they differed by voxel size and b-value. Subsequently, the two selected sequences were acquired from 30 multiple sclerosis (MS) patients with different disability and lesion load and 18 age matched HV. We observed high concordance between mean diffusivity (MD) and fractional anysotropy (FA), nonetheless the DTI sequence with smaller voxel size displayed a better correlation with disease progression, despite a slightly lower SNR. The reliability of corpus callosum (CC) fiber tracking with the chosen DTI sequences was also tested. Conclusions. The sensitivity of DTI-derived indices to MS-related tissue abnormalities indicates that the optimized sequence may be a powerful tool in studies aimed at monitoring the disease course and severity. Hindawi Publishing Corporation 2010 2010-01-05 /pmc/articles/PMC2804108/ /pubmed/20069121 http://dx.doi.org/10.1155/2010/254032 Text en Copyright © 2010 M. Laganà et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Laganà, M.
Rovaris, M.
Ceccarelli, A.
Venturelli, C.
Marini, S.
Baselli, G.
DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application
title DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application
title_full DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application
title_fullStr DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application
title_full_unstemmed DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application
title_short DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application
title_sort dti parameter optimisation for acquisition at 1.5t: snr analysis and clinical application
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2804108/
https://www.ncbi.nlm.nih.gov/pubmed/20069121
http://dx.doi.org/10.1155/2010/254032
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