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. 1. Introduction Magnetic Resonance (MR) diffusion tensor imaging (DTI) allows in vivo examination of the tissue microstructure, obtained by exploiting the properties of water diffusion. The DT computed for each voxel allowed us to calculate the magnitude of water diffusion, reflected by the mean diffusivity (MD) and the degree of anisotropy, which is a measure of tissue organization, expressed as an a-dimensional index, such as fractional anisotropy (FA) [1]. The pathological elements of multiple sclerosis (MS) have the potential to alter the permeability or geometry of structural barriers to water diffusion in the brain. Consistent with this, several in vivo DTI studies have reported increased MD and decreased FA values in T2-visible lesions, normal-appearing (NA) white matter (WM), and grey matter (GM) from patients with MS [2]. Combined with fibre tractography techniques, DTI reveals WM fibers characteristics and connectivity in the brain noninvasively. In MS, tractographic reconstruction has to deal with a general FA reduction in normal appearing white matter (NAWM) and a high FA reduction in lesions with high structural loss [2–5]. The best acquisition and postprocessing strategies for DTI sequences in the disease, especially in
References
[1]
P. J. Basser, J. Mattiello, and D. Lebihan, “Estimation of the effective self-diffusion tensor from the NMR spin echo,” Journal of Magnetic Resonance B, vol. 103, no. 3, pp. 247–254, 1994.
[2]
M. Rovaris, F. Agosta, E. Pagani, and M. Filippi, “Diffusion tensor MR imaging,” Neuroimaging Clinics of North America, vol. 19, no. 1, pp. 37–43, 2009.
[3]
M. Cercignani, G. Lannucci, and M. Filippi, “Diffusion-weighted imaging in multiple sclerosis,” The Italian Journal of Neurological Sciences, vol. 20, supplement 2, pp. S246–S249, 1999.
[4]
D. H. Miller, A. J. Thompson, and M. Filippi, “Magnetic resonance studies of abnormalities in the normal appearing white matter and grey matter in multiple sclerosis,” Journal of Neurology, vol. 250, no. 12, pp. 1407–1419, 2003.
[5]
M. Rovaris, A. Gass, R. Bammer, et al., “Diffusion MRI in multiple sclerosis,” Neurology, vol. 65, no. 10, pp. 1526–1532, 2005.
[6]
D. K. Jones, M. A. Horsfield, and A. Simmons, “Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging,” Magnetic Resonance in Medicine, vol. 42, no. 3, pp. 515–525, 1999.
[7]
X. Liu, T. Zhu, T. Gu, and J. Zhong, “Optimization of in vivo high-resolution DTI of non-human primates on a 3T human scanner,” Methods. In press.
[8]
R. M. Henkelman, “Measurement of signal intensities in the presence of noise in MR images,” Medical Physics, vol. 12, no. 2, pp. 232–233, 1985.
[9]
L. Kaufman, D. M. Kramer, L. E. Crooks, and D. A. Ortendahl, “Measuring signal-to-noise ratios in MR imaging,” Radiology, vol. 173, no. 1, pp. 265–267, 1989.
[10]
R. M. Sano, MRI: Acceptance Testing and Quality Control: The Role of the Clinical Medical Physicist, Medical Physics, Madison, Wis, USA, 1988.
[11]
B. W. Murphy, P. L. Carson, J. H. Ellis, Y. T. Zhang, R. J. Hyde, and T. L. Chenevert, “Signal-to-noise measures for magnetic resonance imagers,” Magnetic Resonance Imaging, vol. 11, no. 3, pp. 425–428, 1993.
[12]
J. Sijbers, A. J. den Dekker, D. Poot, et al., “Robust estimation of the noise variance from background MR data,” in Medical Imaging 2006: Image Processing, vol. 6144 of Proceedings of SPIE, pp. 1–11, San Diego, Calif, USA, February 2006.
[13]
J. Sijbers, A. J. den Dekker, J. Van Audekerke, M. Verhoye, and D. Van Dyck, “Estimation of the noise in magnitude MR images,” Magnetic Resonance Imaging, vol. 16, no. 1, pp. 87–90, 1998.
[14]
S. Mori, B. J. Crain, V. P. Chacko, and P. C. M. van Zijl, “Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging,” Annals of Neurology, vol. 45, no. 2, pp. 265–269, 1999.
[15]
T. E. Conturo, N. F. Lori, T. S. Cull, et al., “Tracking neuronal fiber pathways in the living human brain,” Proceedings of the National Academy of Sciences of the United States of America, vol. 96, no. 18, pp. 10422–10427, 1999.
[16]
P. J. Basser, S. Pajevic, C. Pierpaoli, J. Duda, and A. Aldroubi, “In vivo fiber tractography using DT-MRI data,” Magnetic Resonance in Medicine, vol. 44, no. 4, pp. 625–632, 2000.
[17]
A. Ogura, A. Miyai, F. Maeda, H. Fukutake, and R. Kikumoto, “Accuracy of signal-to-noise ratio measurement method for magnetic resonance images,” Japanese Journal of Radiological Technology, vol. 59, no. 4, pp. 508–513, 2003.
[18]
National Electrical Manufacturers Association (NEMA), Determination of Signal-to-Noise Ratio (SNR) in Diagnostic Magnetic Resonance Imaging, NEMA Standard Publication no. MS 1-2001, National Electrical Manufacturers Association, Rosslyn, Va, USA, 2001.
[19]
S. B. Reeder, B. J. Wintersperger, O. Dietrich, et al., “Practical approaches to the evaluation of signal-to-noise ratio performance with parallel imaging: application with cardiac imaging and a 32-channel cardiac coil,” Magnetic Resonance in Medicine, vol. 54, no. 3, pp. 748–754, 2005.
[20]
O. Dietrich, J. G. Raya, S. B. Reeder, M. F. Reiser, and S. O. Schoenberg, “Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters,” Journal of Magnetic Resonance Imaging, vol. 26, no. 2, pp. 375–385, 2007.
[21]
H. Imai, T. Miyati, A. Ogura, et al., “Signal-to-noise ratio measurement in parallel MRI with subtraction mapping and consecutive methods,” Nippon Hoshasen Gijutsu Gakkai Zasshi, vol. 64, no. 8, pp. 930–936, 2008.
[22]
D. Weber, “Quality Control Issues in MRI,” 1998.
[23]
J. Sijbers, D. Poot, A. J. den Dekker, and W. Pintjens, “Automatic estimation of the noise variance from the histogram of a magnetic resonance image,” Physics in Medicine and Biology, vol. 52, no. 5, pp. 1335–1348, 2007.
[24]
J. Ashburner and K. Friston, “Multimodal image coregistration and partitioning—a unified framework,” NeuroImage, vol. 6, no. 3, pp. 209–217, 1997.
[25]
P. J. Basser, J. Mattiello, and D. LeBihan, “MR diffusion tensor spectroscopy and imaging,” Biophysical Journal, vol. 66, no. 1, pp. 259–267, 1994.
[26]
M. Catani and M. Thiebaut de Schotten, “A diffusion tensor imaging tractography atlas for virtual in vivo dissections,” Cortex, vol. 44, no. 8, pp. 1105–1132, 2008.
[27]
E. Pagani, M. Rovaris, J. G. Hirsch, et al., “Optimising diffusion measurements for large-scale, multicentre multiple sclerosis trials: a pan-European study,” Journal of Neurology, vol. 255, no. 3, p. 342, 2008.
[28]
O. Ciccarelli, M. Catani, H. Johansen-Berg, C. Clark, and A. Thompson, “Diffusion-based tractography in neurological disorders: concepts, applications, and future developments,” The Lancet Neurology, vol. 7, no. 8, pp. 715–727, 2008.
[29]
D. K. Jones and P. J. Basser, ““Squashing peanuts and smashing pumpkins”: how noise distorts diffusion-weighted MR data,” Magnetic Resonance in Medicine, vol. 52, no. 5, pp. 979–993, 2004.
[30]
Y. Assaf and O. Pasternak, “Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review,” Journal of Molecular Neuroscience, vol. 34, no. 1, pp. 51–61, 2008.
[31]
D. S. Tuch, T. G. Reese, M. R. Wiegell, N. Makris, J. W. Belliveau, and V. J. Wedeen, “High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity,” Magnetic Resonance in Medicine, vol. 48, no. 4, pp. 577–582, 2002.
[32]
D. S. Tuch, T. G. Reese, M. R. Wiegell, and V. J. Wedeen, “Diffusion MRI of complex neural architecture,” Neuron, vol. 40, no. 5, pp. 885–895, 2003.
[33]
K. M. Jansons and D. C. Alexander, “Persistent Angular Structure: new insights from diffusion MRI data. Dummy version,” in Information Processing in Medical Imaging, vol. 2732, pp. 672–683, Springer, Berlin, Germany, 2003.
