Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1782176135788888064 |
---|---|
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. |
format | Text |
id | pubmed-2804108 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT laganam dtiparameteroptimisationforacquisitionat15tsnranalysisandclinicalapplication AT rovarism dtiparameteroptimisationforacquisitionat15tsnranalysisandclinicalapplication AT ceccarellia dtiparameteroptimisationforacquisitionat15tsnranalysisandclinicalapplication AT venturellic dtiparameteroptimisationforacquisitionat15tsnranalysisandclinicalapplication AT marinis dtiparameteroptimisationforacquisitionat15tsnranalysisandclinicalapplication AT basellig dtiparameteroptimisationforacquisitionat15tsnranalysisandclinicalapplication |