DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application. (22nd November 2009)

• Record Type: Journal Article
• Authors: Laganà, M.; Rovaris, M.; Ceccarelli, A.; Venturelli, C.; Marini, S.; Baselli, G.
• Other Names: Babiloni Fabio Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2010(2010)
• Publication Date: 2009-11-22
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2010/254032
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : 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. Conclusion . 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.