Maturation of brain white matter pathways is an important factor in cognitive, behavioral, emotional and motor development during childhood and adolescence. In this study, we investigate white matter maturation as reflected by changes in anisotropy and white matter density with age. Thirty-four children and adolescents aged 6-19 years received diffusion-weighted magnetic resonance imaging scans. Among these, 30 children and adolescents also received high-resolution T1-weighed anatomical scans. A linear regression model was used to correlate fractional anisotropy (FA) values with age on a voxel-by-voxel basis. Within the regions that showed significant FA changes with age, a post hoc analysis was performed to investigate white matter density changes. With increasing age, FA values increased in prefrontal regions, in the internal capsule as well as in basal ganglia and thalamic pathways, the ventral visual pathways, and the corpus callosum. The posterior limb of the internal capsule, intrathalamic connections, and the corpus callosum showed the most significant overlaps between white matter density and FA changes with age. This study demonstrates that during childhood and adolescence, white matter anisotropy changes in brain regions that are important for attention, motor skills, cognitive ability, and memory. This typical developmental trajectory may be altered in individuals with disorders of development, cognition and behavior.

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We investigated the relation between cognitive processing speed and structural properties of white matter pathways via convergent imaging studies in healthy and brain-injured groups. Voxel-based morphometry (VBM) was applied to diffusion tensor imaging data from thirty-nine young healthy subjects in order to investigate the relation between processing speed, as assessed with the DigitSymbol subtest from WAIS-III, and fractional anisotropy, an index of microstructural organization of white matter. Digit-Symbol performance was positively correlated with fractional anisotropy of white matter in the parietal and temporal lobes bilaterally and in the left middle frontal gyrus. Fiber tractography indicated that these regions are consistent with the trajectories of the superior and inferior longitudinal fasciculi. In a second investigation, we assessed the effect of white matter damage on processing speed using voxel-based lesion symptom mapping (VLSM) analysis of data from seventy-two patients with left hemisphere strokes. Lesions in left parietal white matter, together with cortical lesions in supramarginal and angular gyri were associated with impaired performance. These findings suggest that cognitive processing speed, as assessed by the Digit-Symbol test, is closely related to the structural integrity of white matter tracts associated with parietal and temporal cortices and left middle frontal gyrus. Further, fiber tractography applied to VBM results and the patient findings suggest that the superior longitudinal fasciculus, a major tract subserving frontoparietal integration, makes a prominent contribution to processing speed.

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A fat-saturated twice-refocused spin echo sequence was implemented on a GE Signa 1.5-T whole-body system for diffusion-weighted imaging. Data were acquired using an analytically designed interleaved variable-density (VD) spiral readout trajectory. This flexible design algorithm allowed real-time prescription on the scanner. Each interleaf of the VD spiral oversampled the center of k-space. The oversampling provided an inherent motion compensation capability. The resultant diffusion-weighted images showed good quality without any retrospective motion correction. An iterated motion correction algorithm was developed to further reduce the signal cancellation artifact caused by motion-induced phase error. In this algorithm, a low-resolution phase map was estimated using the oversampled data in the center of k-space in order to correct for phase error in image space.In vivo diffusion tensor imaging (DTI) studies were performed on the brains of healthy volunteers. High-quality isotropic diffusion-weighted images, trace maps, and FA maps from axial, sagittal, and coronal slices were obtained using a VD spiral readout trajectory with matrix size 256 ؋ 256. To our knowl- Key words: magnetic resonance imaging; high resolution; diffusion; diffusion tensor imaging; variable density spiral; interleaved; motion correction Diffusion-weighted imaging (DWI) is a unique technique for studying random molecular motion in biologic tissues. Over the past decade, DWI has found routine applications in medical diagnosis, especially in detecting acute cerebral ischemia (1). Most diffusion-weighted images are currently acquired using a single-shot echo-planar imaging (EPI) technique. Single-shot EPI has the advantage of rapid image acquisition and insensitivity to phase error caused by subject motion because the entire k-space is acquired with a single rapid train of gradient echoes. Despite the rapid image formation, single-shot EPI lasts long enough that T 2 *-decay limits image resolution and off-resonant spins can still cause serious image degradation.To shorten the readout time, multishot sequences can be used (2,3); however, they generally suffer from view-toview phase variations caused by motion during the period when the diffusion-sensitizing gradients are turned on. One approach to correct these variations is to acquire additional navigator data that can be used to resolve the phase error (4 -7). The navigator can be implemented to correct for either one-dimensional or two-dimensional phase error. The navigator data are intended to provide a direct measure of the motion-induced phase variations. Under the assumption of rigid body motion, the data can be subsequently corrected for small amounts of motion.A few studies have recently explored the self-navigating capability of the spiral readout trajectory in multishot DWI (8,9). Magnetic resonance imaging (MRI) based on spiral readout has been found to be effective in various applications, including functional neuroimaging (10) and spectroscopy (11). The spiral trajectory has the me...

Diffusion tensor imaging (DTI) is known to have a limited capability of resolving multiple fiber orientations within one voxel. This is mainly because the probability density function (PDF) for random spin displacement is non-Gaussian in the confining environment of biological tissues and, thus, the modeling of self-diffusion by a second-order tensor breaks down. The statistical property of a non-Gaussian diffusion process is characterized via the higher-order tensor (HOT) coefficients by reconstructing the PDF of the random spin displacement. Those HOT coefficients can be determined by combining a series of complex diffusion-weighted measurements. The signal equation for an MR diffusion experiment was investigated theoretically by generalizing Fick's law to a higher-order partial differential equation ( Key words: magnetic resonance imaging; diffusion; diffusion tensor imaging; high angular resolution; fiber; probability density function; cumulants Diffusion anisotropy in biological tissues has been the subject of extensive studies following the discovery of anisotropic water diffusion in the cat central nervous system by Moseley et al. in early 1990 (1,2). The diffusion tensor formalism for biological tissues introduced by Basser et al. (3-5) provides a method for characterizing diffusion anisotropy. However, a second-order diffusion tensor, as used for diffusion tensor imaging (DTI), is based on the assumption that water molecules obey Gaussian diffusion in biological tissues (e.g., gray matter and white matter). This assumption limits the information that can be gained from DTI. When DTI is used to study the connectivity of white matter tracts, difficulty is often encountered in regions where the fibers cross or merge (6), and with current MR resolution, voxel averaging of different fiber tracts is frequent and unavoidable (7).New methods such as q-space spectral imaging (8 -13) and more recently high angular resolution diffusionweighted imaging (HARD) (14 -17) have been proposed to overcome this problem. Both methods offer higher resolution of the directionally dependent spin diffusion, and therefore provide better connectivity information for white matter tracts in regions where multiple fiber orientations exist. Theoretically, q-space imaging is a correct way to image the probability density function (PDF) of spin displacement in general, because it does not, in contrast to DTI, assume a particular diffusion model (10,18). Unfortunately, the gradient strength and RF pulse duration requirements for q-space imaging (i.e., both need to approximate a Dirac function) cannot currently be satisfied on a whole-body MR system, or they are precluded by patient safety guidelines that limit the maximum time rate of change of magnetic fields (19).In this study, we investigated the signal equation of an MR diffusion experiment by treating the diffusion process as a first-order Markov process. We derived the relationship between the complex MR signal and the higher-order statistics of the spin displacement based on stat...

Nonuniformities of magnetic field gradients can cause serious artifacts in diffusion imaging. While it is well known that nonlinearities of the imaging gradients lead to image warping, those imperfections can also cause spatially dependent errors in the direction and magnitude of the diffusion encoding. This study shows that the potential errors in diffusion imaging are considerable. Further, we show that retrospective corrections can be applied to reduce these errors. A general mathematical framework was formulated to characterize the contribution of gradient nonuniformities to diffusion experiments. The gradient field was approximated using spherical harmonic expansion, and this approximation was employed (after geometric distortions were eliminated) to predict and correct the errors in diffusion encoding. Before the corrections were made, the experiments clearly revealed marked deviations of the calculated diffusivity for fields of view (FOVs) generally used in diffusion experiments. These deviations were most significant farther away from the magnet's isocenter. For an FOV of 25 cm, the resultant errors in absolute diffusivity ranged from approximately -10% to ؉20%.

Objective-Outcome prediction is challenging in comatose post-cardiac arrest survivors. We assessed the feasibility and prognostic utility of brain diffusion-weighted MRI (DWI) during the first week.Corresponding Author Christine AC Wijman, MD, PhD, Stanford Stroke Center, 701 Welch Road, B325, Palo Alto, CA 94304, Fax: (650) Tel: (650) NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptMethods-Consecutive comatose post-cardiac arrest patients were prospectively enrolled. MRI data of patients who met predefined specific prognostic criteria were used to determine distinguishing ADC thresholds. Group 1: death at 6 months and absent motor response or absent pupillary reflexes or bilateral absent cortical responses at 72 hours, or vegetative at 1 month. Group 2A: Glasgow outcome scale (GOS) score of 4 or 5 at 6 months. Group 2B: GOS of 3 at 6 months. The percentage of voxels below different apparent diffusion coefficient (ADC) thresholds was calculated at 50 × 10 −6 mm 2 /sec intervals.Results-Overall, 86% of patients underwent MR imaging. Fifty-one patients with 62 brain MRIs were included in the analyses. Forty patients met the specific prognostic criteria. The percentage of brain volume with an ADC value below 650-700 × 10 −6 mm 2 /sec best differentiated between group 1 and groups 2A and 2B combined (p<0.001), while the 400-450 × 10 −6 mm 2 /sec threshold best differentiated between groups 2A and 2B (p=0.003). The ideal time window for prognostication using DWI was between 49 to 108 hours after the arrest. When comparing MRI in this time window with the 72 hour neurological examination MRI improved the sensitivity for predicting poor outcome by 38% while maintaining 100% specificity (p=0.021).Interpretation-Quantitative DWI in comatose post-cardiac arrest survivors holds great promise as a prognostic adjunct.Approximately 350,000 cardiac arrests occur annually in the United States1. Up to half of these patients are successfully resuscitated. In the past, only 10% to 30% of comatose postcardiac arrest patients had good functional recovery. These numbers will likely improve with the increasing use of therapeutic hypothermia2 , 3.Post-cardiac arrest brain injury is a common cause of morbidity and mortality. Many comatose post-cardiac arrest patients die or survive with severe disability after a prolonged intensive care unit stay associated with a tremendous cost burden4 , 5. Conversely, the potential for premature withdrawal of life support from patients who may have a chance of functional recovery represents an additional ethical dilemma. Thus, early accurate identification of patients who have no likelihood of meaningful recovery is a very important health care issue.Although several prognostic variables have been studied in comatose post-cardiac arrest patients, the currently accepted variables (neurological examination, neurophysiologic tests, and serum markers) have substantive limitations. First, they identify only a subset of poor outcome patients with high specificity. Se...