Quantitative water content mapping at clinically relevant field strengths: a comparative study at 1.5 T and 3 T.

PURPOSE: Quantitative water content mapping in vivo using MRI is a very valuable technique to detect, monitor and understand diseases of the brain. At 1.5 T, this technology has already been successfully used, but it has only recently been applied at 3T because of significantly increased RF field inhomogeneity at the higher field strength. To validate the technology at 3T, we estimate and compare in vivo quantitative water content maps at 1.5 T and 3T obtained with a protocol proposed recently for 3T MRI. METHODS: The proposed MRI protocol was applied on twenty healthy subjects at 1.5 T and 3T; the same post-processing algorithms were used to estimate the water content maps. The 1.5 T and 3T maps were subsequently aligned and compared on a voxel-by-voxel basis. Statistical analysis was performed to detect possible differences between the estimated 1.5 T and 3T water maps. RESULTS: Our analysis indicates that the water content values obtained at 1.5 T and 3T did not show significant systematic differences. On average the difference did not exceed the standard deviation of the water content at 1.5 T. Furthermore, the contrast-to-noise ratio (CNR) of the estimated water content map was increased at 3T by a factor of at least 1.5. CONCLUSIONS: Vulnerability to RF inhomogeneity increases dramatically with the increasing static magnetic field strength. However, using advanced corrections for the sensitivity profile of the MR coils, it is possible to preserve quantitative accuracy while benefiting from the increased CNR at the higher field strength. Indeed, there was no significant difference in the water content values obtained in the brain at 1.5 T and 3T.

Analysis of proton-density bias corrections based on T1 measurement for robust quantification of water content in the brain at 3 Tesla.

PURPOSE: Estimating tissue water content using high field MRI, such as 3 Tesla (T), is challenging due to the difficulty in dissociating the radio frequency inhomogeneity pattern from the signal arising from tissue intrinsic proton density (PD) variations. To overcome this problem the longitudinal relaxation time T1 can be combined with an initial guess of the PD to yield the desired PD bias correction. However, it is necessary to know whether T1 effects, i.e., any effect contributing to T1 while being independent of tissue hydration, influence the estimated correction. METHODS: Twenty-five healthy subjects underwent a quantitative 3T MRI protocol enabling acquisition of 64 slices with 1 mm in-plane resolution and 2 mm slice thickness in 14 min. Influence of T1 effects on the estimated water content map is evaluated using a dedicated method including T1 and T2 * information and region of interest-based water content values are compared with the literature. RESULTS: Our analysis indicates that the PD bias correction based on T1 is largely insensitive to T1 effects. Besides, water content results are in good agreement with literature values obtained at 1.5T. CONCLUSION: This study demonstrates the applicability of a PD bias correction based on T1 to yield tissue water content at 3T.

Characterizing cerebral oxygen metabolism employing oxygen-17 MRI/MRS at high fields.

This article provides a comprehensive overview of oxygen ((17)O) magnetic resonance spectroscopy and imaging, including the advantages and challenges offered by the different methods developed thus far. The physiological role and relevance of oxygen, and its participation in aerobic metabolism, are addressed to emphasize the importance of the investigations and the efforts related to these developments. Furthermore, a number of methods employed in the determination of the cerebral metabolic rate of oxygen in neural cells will be presented, focusing primarily on methodologies enabling absolute quantification.

Design and implementation of a simple multinuclear MRI system for ultra high-field imaging of animals.

Non-proton MRI has recently garnered gathering interest with the increased availability of ultra high-field MRI system. Assuming the availability of a broadband RF amplifier, performing multinuclear MR experiments essentially requires additional hardware, such as an RF resonator and a T/R switch for each nucleus. A double- or triple-resonant RF probe is typically constructed using traps or PIN-diode circuits, but this approach degrades the signal-to-noise ratio (SNR) and image quality compared to a single-resonant coil and this is a limiting factor. In this work, we have designed the required hardware for multinuclear MR imaging experiments employing six single-resonant coil sets and a purpose-built animal bed; these have been implemented into a home-integrated 9.4T preclinical MRI scanner. System capabilities are demonstrated by distinguishing concentration differences and sensitivity of X-nuclei imaging and spectroscopy without SNR penalty for any nuclei, no subject interruption and no degradation of the static shim conditions.

Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation.

Recently, several magnetic resonance imaging contrast mechanisms have been shown to distinguish cortical substructure corresponding to selected cortical layers. Here, we investigate cortical layer and area differentiation by automatized unsupervised clustering of high-resolution diffusion MRI data. Several groups of adjacent layers could be distinguished in human primary motor and premotor cortex. We then used the signature of diffusion MRI signals along cortical depth as a criterion to detect area boundaries and find borders at which the signature changes abruptly. We validate our clustering results by histological analysis of the same tissue. These results confirm earlier studies which show that diffusion MRI can probe layer-specific intracortical fiber organization and, moreover, suggests that it contains enough information to automatically classify architecturally distinct cortical areas. We discuss the strengths and weaknesses of the automatic clustering approach and its appeal for MR-based cortical histology.