Purpose:To develop an experimental protocol to calculate the precision and accuracy of fractional anisotropy (FA), mean diffusivity (MD), and the orientation of the principal eigenvector (PEV) as a function of the signal-to-noise ratio (SNR) in vivo. Materials and Methods:A healthy male volunteer was scanned in three separate scanning sessions, yielding a total of 45 diffusion tensor imaging (DTI) scans. To provide FA, MD, and PEV as a function of SNR, sequential scans from a scan session were grouped into nonintersecting sets. Analysis of the accuracy and precision of the DTI-derived contrasts was done in both a voxel-wise and region of interest (ROI)-based manner.Results: An upward bias of FA and no significant bias in MD were present as SNR decreased, confirming results from simulation-based studies. Notably, while the precision of the PEV became worse at low SNR, no bias in the PEV orientation was observed. Overall, an accurate and precise quantification of FA values in GM requires substantially more SNR than the quantification of white matter (WM) FA values Conclusion:This study provides guidance for FA, MD, and PEV quantification and a means to investigate the minimal detectable differences within and across scan sessions as a function of SNR. DIFFUSION TENSOR IMAGING (DTI)is an MRI technique that measures the spatial diffusion characteristics of water and provides novel contrasts to study the fiber architecture of the central nervous system in vivo (1-7). The diffusion characteristics of water are dependent on the composition and architecture of the biological environment and can be quantified by scalar and vector contrasts. Prominent scalar quantities include fractional anisotropy (FA), which describes the degree of diffusion anisotropy, and mean diffusivity (MD), which is a measure of the average amount of diffusion in a voxel, both of which have found useful applications in human imaging in the clinic. FA in white matter (WM) arises in part due to axonal and myelin barriers to water diffusion and has been used to assess and monitor WM damage (2,8). MD has been particularly useful in the study of the temporal evolution of stroke (9,10). Recent reviews (2,8,(11)(12)(13)(14) outline the methodology and clinical applications of DTI.In addition to scalar quantities, DTI also provides vector contrasts such as the principal eigenvector (PEV) from which the predominant fiber orientation in a voxel can be inferred. The PEV has been used extensively to examine the architecture and connectivity of the brain with color-coded PEV orientation maps (5,15) and tractography methods (11,16 -22). DTI continues to grow in

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Modern MRI image processing methods have yielded quantitative, morphometric, functional, and structural assessments of the human brain. These analyses typically exploit carefully optimized protocols for specific imaging targets. Algorithm investigators have several excellent public data resources to use to test, develop, and optimize their methods. Recently, there has been an increasing focus on combining MRI protocols in multi-parametric studies. Notably, these have included innovative approaches for fusing connectivity inferences with functional and/or anatomical characterizations. Yet, validation of the reproducibility of these interesting and novel methods has been severely hampered by the limited availability of appropriate multi-parametric data. We present an imaging protocol optimized to include state-of-the-art assessment of brain function, structure, micro-architecture, and quantitative parameters within a clinically feasible 60 minute protocol on a 3T MRI scanner. We present scan-rescan reproducibility of these imaging contrasts based on 21 healthy volunteers (11 M/10 F, 22-61 y/o). The cortical gray matter, cortical white matter, ventricular cerebrospinal fluid, thalamus, putamen, caudate, cerebellar gray matter, cerebellar white matter, and brainstem were identified with mean volume-wise reproducibility of 3.5%. We tabulate the mean intensity, variability and reproducibility of each contrast in a region of interest approach, which is essential for prospective study planning and retrospective power analysis considerations. Anatomy was highly consistent on structural acquisition (~1-5% variability), while variation on diffusion and several other quantitative scans was higher (~<10%). Some sequences are particularly variable in specific structures (ASL exhibited variation of 28% in Corresponding author: Bennett A. Landman, PhD, Vanderbilt University EECS, 2301 Vanderbilt Pl., PO Box 351679 Station B, Nashville, TN 37235-1679, Work: 410-917-6166, bennett.landman@vanderbilt.edu. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access Author ManuscriptNeuroimage. Author manuscript; available in PMC 2012 February 14. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript the cerebral white matter) or in thin structures (quantitative T2 varied by up to 73% in the caudate) due, in large part, to variability in automated ROI placement. The richness of the joint distribution of intensities across imaging methods can be best assessed within the context of a particular analysis approach as opposed to a summary table. As such, all imagi...

Diffusion tensor imaging (DTI) and immunohistochemistry were used to examine axon injury in the rat spinal cord after unilateral L 2 -L 4 dorsal root axotomy at multiple time points (from 16 h to 30 d after surgery). Three days after axotomy, DTI revealed a lesion in the ipsilateral dorsal column extending from the lumbar to the cervical cord. The lesion showed significantly reduced parallel diffusivity and increased perpendicular diffusivity at day 3 compared with the contralateral unlesioned dorsal column. These findings coincided with loss of phosphorylated neurofilaments, accumulation of nonphosphorylated neurofilaments, swollen axons and formation of myelin ovoids, and no clear loss of myelin (stained by Luxol fast blue and 2Ј-3Ј-cyclic nucleotide 3Ј-phosphodiesterase). At day 30, DTI of the lesion continued to show significantly decreased parallel diffusivity. There was a slow but significant increase in perpendicular diffusivity between day 3 and day 30, which correlated with gradual clearance of myelin without further significant changes in neurofilament levels. These results show that parallel diffusivity can detect axon degeneration within 3 d after injury. The clearance of myelin at later stages may contribute to the late increase in perpendicular diffusivity, whereas the cause of its early increase at day 3 may be related to changes associated with primary axon injury. These data suggest that there is an early imaging signature associated with axon transections that could be used in a variety of neurological disease processes.

High-resolution magnetic resonance phase- or frequency- shift images acquired at high field show contrast related to magnetic susceptibility differences between tissues. Such contrast varies with the orientation of the organ in the field, but the development of quantitative susceptibility mapping (QSM) has made it possible to reproducibly image the intrinsic tissue susceptibility contrast. However, recent studies indicate that magnetic susceptibility is anisotropic in brain white matter and, as such, needs to be described by a symmetric second-rank tensor (trueχ¯¯). To fully determine the elements of this tensor, it would be necessary to acquire frequency data at six or more orientations. Assuming cylindrical symmetry of the susceptibility tensor in myelinated white matter fibers, we propose a simplified method to reconstruct the susceptibility tensor in terms of a mean magnetic susceptibility, MMS = (χ∥ + 2χ⊥)/3 and a magnetic susceptibility anisotropy, MSA = χ∥ − χ⊥, where χ∥ and χ⊥ are susceptibility parallel and perpendicular to the white matter fiber direction, respectively. Computer simulations show that with a practical head rotation angle of around 20°–30°, four head orientations suffice to reproducibly reconstruct the tensor with good accuracy. We tested this approach on whole brain 1×1×1 mm3 frequency data acquired from five healthy subjects at 7 T. The frequency information from phase images collected at four head orientations was combined with the fiber direction information extracted from diffusion tensor imaging (DTI) to map the white matter susceptibility tensor. The MMS and MSA were quantified for regions in several large white matter fiber structures, including the corona radiata, posterior thalamic radiation and corpus callosum. MMS ranged from −0.037 to −0.053 ppm (referenced to CSF being about zero). MSA values could be quantified without the need for a reference and ranged between 0.004 and 0.029 ppm, in line with the expectation that the susceptibility perpendicular to the fiber is more diamagnetic than the one parallel to it.

Q-space analysis is an alternative analysis technique for diffusion-weighted imaging (DWI) data in which the probability density function (PDF) for molecular diffusion is estimated without the need to assume a Gaussian shape. Although used in the human brain, q-space DWI has not yet been applied to study the human spinal cord in vivo. Here we demonstrate the feasibility of performing q-space imaging in the cervical spinal cord of eight healthy volunteers and four patients with multiple sclerosis. The PDF was computed and water displacement and zerodisplacement probability maps were calculated from the width and height of the PDF, respectively. In the dorsal column white matter, q-space contrasts showed a significant (P < 0.01) increase in the width and a decrease in the height of the PDF in lesions, the result of increased diffusion. These q-space contrasts, which are sensitive to the slow diffusion component, exhibited improved detection of abnormal diffusion compared to perpendicular apparent diffusion constant measurements. In white matter (WM) the axonal membrane and myelin sheath present barriers to water displacement, resulting in anisotropic diffusion (1-4). WM damage is known to affect tissue microstructure and diffusion-weighted MRI (DWI) has been used to measure changes in diffusion properties (both parallel and perpendicular to WM fiber bundles) in a number of WM diseases in humans (5) as well as animal models of myelin deficiency (6,7). In general, however, conclusive assignment of diffusion changes observed with DWI to axonal and/or myelin damage is not straightforward, in part because the biophysics of diffusion in vivo is not fully understood and because axonal and myelin loss are histopathologically related. Additionally, the technique selected to analyze diffusion-weighted images (DWIs) is an important consideration and has an impact on the quantitative interpretation of diffusion experiments. DWIs are typically analyzed with a monoexponential tensor model that characterizes the observed signal decay according to the Stejskal-Tanner equation (8):where S/S 0 is the normalized signal intensity, ␥ is the proton gyromagnetic ratio, ␦, G, and ⌬ are the duration, magnitude, and leading edge separation time of the diffusion weighting gradient vector, respectively, and D is the diffusion tensor. Diffusion tensor imaging (DTI) has been applied in the brain (5,9 -12) and spinal cord (10,(13)(14)(15) and is typically performed in the low b-value (Ͻ1500 s/mm 2 ) regime where the signal decay is, to a reasonable approximation, monoexponential. The degree to which diffusion is reduced in the CNS, compared to free water, is the result of microstructural barriers, which generally includes multiple compartments in vivo and the diffusion time that molecules have to explore their environment. If restrictions between compartments are sufficiently large so that exchange is slow on the MR timescale, the signal attenuation will become non-monoexponential. This effect becomes apparent at higher b-values (Ͼ1500 s/mm 2 )...

Multiparametric MRI allows rapid detection, localization, and characterization of tract-specific abnormalities in multiple sclerosis. Tract profiles bridge the gap between whole-brain imaging of neurological disease and the interrogation of individual, functionally relevant subsystems.

T 1 and T 2 were measured for white matter (WM) and gray matter (GM) in the human cervical spinal cord at 3T. T 1 values were calculated using an inversion-recovery (IR) and B 1 -corrected double flip angle gradient echo (GRE) and show significant differences (p ‫؍‬ 0.002) between WM (IR ‫؍‬ 876 ؎ 27 ms, GRE ‫؍‬ 838 ؎ 54 ms) and GM (IR ‫؍‬ 973 ؎ 33 ms, GRE ‫؍‬ 994 ؎ 54 ms Image contrast in conventional MRI relies on the distinct relaxation behavior of water spins residing in different tissue environments. Quantitative determination of the relaxation time constants is important for the derivation of experimental parameters that optimize image contrast. Furthermore, understanding the nature of relaxivity in different tissues facilitates the development of new imaging methods. As the use of higher field whole body MRI systems (i.e., Ͼ1.5T) is becoming more widespread, it should be recognized that tissue relaxation rates are fielddependent and that experimental parameters must be reoptimized to take full advantage of the benefits of higher field strength. In vivo human tissue relaxation parameters have recently been measured in the brain (1,2) and in blood (3) at 3T, but to our knowledge no studies of the human spinal cord have been reported at any clinical field strength. The small size and mobile nature of the spinal cord hamper quantitative measurements, and it has been necessary to assume that spinal cord white matter (WM) and gray matter (GM) relaxation rates will mimic those in the brain, in spite of histological indications that spinal cord tissues differ from brain tissue (4). The same difficulties that have deterred measurement of relaxation behavior have also slowed the development of spinal cord imaging in general (5,6). Recently, a number of techniques for highresolution imaging have been applied to the spinal cord (7-9), yielding important clinical information about several pathologies (10,11), most notably multiple sclerosis (MS). Further development (and thereby, widespread adoption) of these methodologies may be facilitated by quantitative measures of the relaxation times, as they allow optimization of imaging parameters, potentially yielding improvements in sensitivity and contrast.Several other approaches require knowledge of water relaxation times. For example, in the field of in vivo spectroscopy, it is necessary to quantify metabolite concentrations from signal intensities that are functions of concentration, relaxation rates and experimental parameters. Since the intensity of the unsuppressed water signal is often used as an internal standard (12), accurate quantification of the relaxation parameters of water is critical. In the case of magnetization transfer imaging, knowledge of the relaxation times is important for the quantification of magnetization transfer effects which can provide parameters that reflect macromolecular interactions with the water signal (e.g., bound pool fraction, exchange rate) (13,14), as well as the optimization of imaging sequences at higher field strength (15) In th...