Normal cognitive development in infants follows a well-known temporal sequence, which is assumed to be correlated with the structural maturation of underlying functional networks. Postmortem studies and, more recently, structural MR imaging studies have described qualitatively the heterogeneous spatiotemporal progression of white matter myelination. However, in vivo quantification of the maturation phases of fiber bundles is still lacking. We used noninvasive diffusion tensor MR imaging and tractography in twenty-three 1-4-month-old healthy infants to quantify the early maturation of the main cerebral fascicles. A specific maturation model, based on the respective roles of different maturational processes on the diffusion phenomena, was designed to highlight asynchronous maturation across bundles by evaluating the time-course of mean diffusivity and anisotropy changes over the considered developmental period. Using an original approach, a progression of maturation in four relative stages was determined in each tract by estimating the maturation state and speed, from the diffusion indices over the infants group compared with an adults group on one hand, and in each tract compared with the average over bundles on the other hand. Results were coherent with, and extended previous findings in 8 of 11 bundles, showing the anterior limb of the internal capsule and cingulum as the most immature, followed by the optic radiations, arcuate and inferior longitudinal fascicles, then the spinothalamic tract and fornix, and finally the corticospinal tract as the most mature bundle. Thus, this approach provides new quantitative landmarks for further noninvasive research on brain-behavior relationships during normal and abnormal development.

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We present new diffusion phantoms dedicated to the study and validation of high-angular-resolution diffusion imaging (HARDI) models. The phantom design permits the application of imaging parameters that are typically employed in studies of the human brain. The phantoms were made of small-diameter acrylic fibers, chosen for their high hydrophobicity and flexibility that ensured good control of the phantom geometry. The polyurethane medium was filled under vacuum with an aqueous solution that was previously degassed, doped with gadoliniumtetraazacyclododecanetetraacetic acid (Gd-DOTA), and treated by ultrasonic waves. Two versions of such phantoms were manufactured and tested. The phantom's applicability was demonstrated on an analytical Q-ball model. Numerical simulations were performed to assess the accuracy of the phantom. The phantom data will be made accessible to the community with the objective of analyzing various HARDI models. During the last decade, diffusion-weighted (DW) imaging (DWI) has become an established technique for the diagnosis of ischemia (1) and investigations of the anatomical connectivity of the human brain (2). Presently, no manufacturer delivers any phantoms dedicated to diffusion imaging, due to the complexity of their design. However, diffusion phantoms have numerous applications. They include calibration, validation of tractography algorithms, and validation of diffusion models. The phantom design should comply with the concrete application. For example, calibration requires a large region of interest (ROI) with a specific apparent diffusion coefficient (ADC), fractional anisotropy (FA), and principal orientation(s) to reduce the impact of acquisition noise on the measurements. On the other hand, to validate tractography, one would typically use a phantom made up of long bundles, similar to those found in brain white matter. To circumvent the intrinsic limitations of diffusion tensor imaging (DTI; i.e., the inability to resolve multiple fiber populations), a number of high-angular-resolution diffusion imaging (HARDI) models were introduced (3-12). They were conceived with the aim of providing an unbiased estimate of the probability density function (PDF) describing the displacements of the water molecules during a predefined time interval. Some models deliver only the radial projections of the PDF, known as the orientation distribution function (ODF). The phantoms employed in the studies of HARDI models could be adjusted to different fiber configurations (crossing, kissing, merging, and splitting), and angular distribution. Several diffusion phantom designs were proposed, based on fibrous vegetables (13), biological tissues (14), plastic capillaries (15-17), or textile fibers (18 -20). In this work, we present a novel diffusion phantom dedicated to the validation of HARDI models. We developed two versions of this phantom corresponding to 45°and 90°fi ber crossings, and used them to test the analytical Q-ball model. MATERIALS AND METHODSThe design of diffusion phantoms dedicated to st...

Magnetic resonance (MR) diffusion imaging provides a valuable tool used for inferring structural anisotropy of brain white matter connectivity from diffusion tensor imaging. Recently, several high angular resolution diffusion models were introduced in order to overcome the inadequacy of the tensor model for describing fibre crossing within a single voxel. Among them, q-ball imaging (QBI), inherited from the q-space method, relies on a spherical Radon transform providing a direct relationship between the diffusion-weighted MR signal and the orientation distribution function (ODF). Experimental validation of these methods in a model system is necessary to determine the accuracy of the methods and to optimize them. A diffusion phantom made up of two textile rayon fibre (comparable in diameter to axons) bundles, crossing at 90 degrees , was designed and dedicated to ex vivo q-ball validation on a clinical scanner. Normalized ODFs were calculated inside regions of interest corresponding to monomodal and bimodal configurations of underlying structures. Three-dimensional renderings of ODFs revealed monomodal shapes for voxels containing single-fibre population and bimodal patterns for voxels located within the crossing area. Principal orientations were estimated from ODFs and were compared with a priori structural fibre directions, validating efficiency of QBI for depicting fibre crossing. In the homogeneous regions, QBI detected the fibre angle with an accuracy of 19 degrees and in the fibre-crossing region with an accuracy of 30 degrees .

Abstract. Most of the approaches dedicated to fiber tracking from diffusionweighted MR data rely on a tensor model. However, the tensor model can only resolve a single fiber orientation within each imaging voxel. New emerging approaches have been proposed to obtain a better representation of the diffusion process occurring in fiber crossing. In this paper, we adapt a tracking algorithm to the q-ball representation, which results from a spherical Radon transform of high angular resolution data. This algorithm is based on a Monte-Carlo strategy, using regularized particle trajectories to sample the white matter geometry. The method is validated using a phantom of bundle crossing made up of haemodialysis fibers. The method is also applied to the detection of the auditory tract in three human subjects.

Purpose: To prove the feasibility of arterial spin labeling (ASL) to explore brain tumors by comparing dynamic susceptibility contrast-enhanced MRI to ASL at 3T MR. Materials and Methods: Twenty-seven patients were included presenting 9 gliomas, 10 metastases and 8 meningiomas. All were explored by a pseudo-continuous ASL and dynamic susceptibility contrast-enhanced T2* perfusion sequence. Two neuroradiologists analyzed the cerebral blood flow (CBF) maps to assess feasibility, examination quality and quantitative comparison. The Spearman nonparametric correlation test and the Bland-Altman graphic test were used to analyze our quantitative results. Results: 92% of ASL CBF maps were informative. ASL detected all lesions as well as dynamic susceptibility contrast-enhanced MRI. Both sequences provide relative quantitative CBF values closely correlated. Conclusion: On a 3T MR unit, ASL is a good alternative to dynamic susceptibility contrast-enhanced MRI when contrast medium is contraindicated or intravenous injection is not possible. Its results on relative CBF parameters are similar to contrast-injected perfusion.

This paper exploits the idea that each individual brain region has a specific connection profile to create parcellations of the cortical mantle using MR diffusion imaging. The parcellation is performed in two steps. First, the cortical mantle is split at a macroscopic level into 36 large gyri using a sulcus recognition system. Then, for each voxel of the cortex, a connection profile is computed using a probabilistic tractography framework. The tractography is performed from q fields using regularized particle trajectories. Fiber ODF are inferred from the q-balls using a sharpening process focusing the weight around the q-ball local maxima. A sophisticated mask of propagation computed from a T1-weighted image perfectly aligned with the diffusion data prevents the particles from crossing the cortical folds. During propagation, the particles father child particles in order to improve the sampling of the long fascicles. For each voxel, intersection of the particle trajectories with the gyri lead to a connectivity profile made up of only 36 connection strengths. These profiles are clustered on a gyrus by gyrus basis using a K-means approach including spatial regularization. The reproducibility of the results is studied for three subjects using spatial normalization.

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