Multi-parametric representation of voxel-based quantitative magnetic resonance imaging.

The aim of the study was to explore the possibilities of multi-parametric representations of voxel-wise quantitative MRI data to objectively discriminate pathological cerebral tissue in patients with brain disorders. For this purpose, we recruited 19 patients with Multiple Sclerosis (MS) as benchmark samples and 19 age and gender matched healthy subjects as a reference group. The subjects were examined using quantitative Magnetic Resonance Imaging (MRI) measuring the tissue structure parameters: relaxation rates, R(1) and R(2), and proton density. The resulting parameter images were normalized to a standard template. Tissue structure in MS patients was assessed by voxel-wise comparisons with the reference group and with correlation to a clinical measure, the Expanded Disability Status Scale (EDSS). The results were visualized by conventional geometric representations and also by multi-parametric representations. Data showed that MS patients had lower R(1) and R(2), and higher proton density in periventricular white matter and in wide-spread areas encompassing central and sub-cortical white matter structures. MS-related tissue abnormality was highlighted in posterior white matter whereas EDSS correlation appeared especially in the frontal cortex. The multi-parameter representation highlighted disease-specific features. In conclusion, the proposed method has the potential to visualize both high-probability focal anomalies and diffuse tissue changes. Results from voxel-based statistical analysis, as exemplified in the present work, may guide radiologists where in the image to inspect for signs of disease. Future clinical studies must validate the usability of the method in clinical practice.

Application of quantitative MRI for brain tissue segmentation at 1.5 T and 3.0 T field strengths.

BACKGROUND: Brain tissue segmentation of white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) are important in neuroradiological applications. Quantitative Mri (qMRI) allows segmentation based on physical tissue properties, and the dependencies on MR scanner settings are removed. Brain tissue groups into clusters in the three dimensional space formed by the qMRI parameters R1, R2 and PD, and partial volume voxels are intermediate in this space. The qMRI parameters, however, depend on the main magnetic field strength. Therefore, longitudinal studies can be seriously limited by system upgrades. The aim of this work was to apply one recently described brain tissue segmentation method, based on qMRI, at both 1.5 T and 3.0 T field strengths, and to investigate similarities and differences. METHODS: In vivo qMRI measurements were performed on 10 healthy subjects using both 1.5 T and 3.0 T MR scanners. The brain tissue segmentation method was applied for both 1.5 T and 3.0 T and volumes of WM, GM, CSF and brain parenchymal fraction (BPF) were calculated on both field strengths. Repeatability was calculated for each scanner and a General Linear Model was used to examine the effect of field strength. Voxel-wise t-tests were also performed to evaluate regional differences. RESULTS: Statistically significant differences were found between 1.5 T and 3.0 T for WM, GM, CSF and BPF (p<0.001). Analyses of main effects showed that WM was underestimated, while GM and CSF were overestimated on 1.5 T compared to 3.0 T. The mean differences between 1.5 T and 3.0 T were -66 mL WM, 40 mL GM, 29 mL CSF and -1.99% BPF. Voxel-wise t-tests revealed regional differences of WM and GM in deep brain structures, cerebellum and brain stem. CONCLUSIONS: Most of the brain was identically classified at the two field strengths, although some regional differences were observed.

Brain characterization using normalized quantitative magnetic resonance imaging.

OBJECTIVES: To present a method for generating reference maps of typical brain characteristics of groups of subjects using a novel combination of rapid quantitative Magnetic Resonance Imaging (qMRI) and brain normalization. The reference maps can be used to detect significant tissue differences in patients, both locally and globally. MATERIALS AND METHODS: A rapid qMRI method was used to obtain the longitudinal relaxation rate (R1), the transverse relaxation rate (R2) and the proton density (PD). These three tissue properties were measured in the brains of 32 healthy subjects and in one patient diagnosed with Multiple Sclerosis (MS). The maps were normalized to a standard brain template using a linear affine registration. The differences of the mean value ofR1, R2 and PD of 31 healthy subjects in comparison to the oldest healthy subject and in comparison to an MS patient were calculated. Larger anatomical structures were characterized using a standard atlas. The vector sum of the normalized differences was used to show significant tissue differences. RESULTS: The coefficient of variation of the reference maps was high at the edges of the brain and the ventricles, moderate in the cortical grey matter and low in white matter and the deep grey matter structures. The elderly subject mainly showed significantly lower R1 and R2 and higher PD values along all sulci. The MS patient showed significantly lower R1 and R2 and higher PD values at the edges of the ventricular system as well as throughout the periventricular white matter, at the internal and external capsules and at each of the MS lesions. CONCLUSION: Brain normalization of rapid qMRI is a promising new method to generate reference maps of typical brain characteristics and to automatically detect deviating tissue properties in the brain.