BACKGROUND AND PURPOSE:Accurate detection and classification of purely intracortical lesions in multiple sclerosis (MS) are important in understanding their role in disease progression and impact on the clinical manifestations of the disease. However, detection of these lesions with conventional MR imaging remains a challenge. Although double inversion recovery (DIR) has been shown to improve the sensitivity of the detection of cortical lesions, this sequence has low signal-to-noise ratio (SNR), poor delineation of lesion borders, and is prone to image artifacts. We demonstrate that intracortical lesions can be identified and classified with greater confidence by the combination of DIR with phase-sensitive inversion recovery (PSIR) images.

Background-Gray matter lesions are known to be common in multiple sclerosis (MS), and are suspected to play an important role in disease progression and clinical disability. A combination of MRI techniques, double-inversion recovery (DIR) and phase-sensitive inversion recovery (PSIR), has been used for detection and classification of cortical lesions. We now demonstrate that highresolution three-dimensional (3D) magnetization-prepared rapid acquisition with gradient echo (MPRAGE) improves the classification of cortical lesions by allowing more accurate anatomic localization of lesion morphology.

Purpose:To develop an analytical formalism describing how noise and selection of diffusion-weighting scheme propagate through the diffusion tensor imaging (DTI) computational chain into variances of the diffusion tensor elements, and errors in the relative anisotropy (RA) and fractional anisotropy (FA) indices. Materials and Methods:Singular-value decomposition (SVD) was used to determine the tensor variances, with diffusion-weighting scheme and measurement noise incorporated into the design matrix. Anisotropy errors were then derived using propagation of error. To illustrate the applications of the model, 12 data sets were acquired from each human subject, over a range of b-values (500 -2500 seconds/mm 2 ) and diffusion-weighting gradient directions (N ϭ 6 -55). The mean RA and FA values and their respective errors were calculated within a region of interest (ROI) in the splenium. The RA and FA errors as a function of b-value and N were evaluated, and a number of diffusion-weighting schemes were assessed based on a new metric, sum of diffusion tensor variances.Results: When the acquisition time was held constant, the sum of the diffusion tensor variances decreased as N increased. The same trend was also observed for several diffusion-weighting schemes with constant condition number when noise in the diffusion-weighted (DW) images was assumed unity. Errors in both FA and RA increased with b-value and decreased with N. The FA error in the splenium was approximately threefold smaller than RA error, irrespective of b-value or N. Conclusion:The condition number may not adequately characterize the noise sensitivity for a given diffusionweighting scheme. Signal averaging may not be as effective as increasing N, especially when N is small (e.g., N Ͻ 13). Due to its smaller error, FA is preferred over RA for quantitative DTI applications.

This magnetic resonance (MR) imaging study was approved by the institutional review board and was HIPAA compliant. Written informed consent was obtained from all participants. The purpose of the study was to prospectively compare T1-weighted inversion recovery with short inversion time inversion recovery (STIR) and dual fast spin echo (FSE) for imaging cervical spinal cord lesions in patients with multiple sclerosis (MS). Twelve patients (eight men, four women; median age, 44 years) were imaged by using T1-weighted inversion recovery, STIR, and FSE. Contrast between lesions and normal cervical cord was measured for each sequence, and generalized estimating equation analysis was used to test statistical significance of the results. Normalized contrast between lesion and normal-appearing spinal cord was significantly higher for T1-weighted inversion recovery than for the other sequences (P < .0001). Use of phase-sensitive reconstruction improved lesion localization and boundary definition. These advantages of T1-weighted inversion recovery over STIR and dual-echo FSE suggest that it has potential in cervical spinal cord imaging of MS. (c) RSNA, 2007.

DIR and PSIR combined with nonlinear image registration allowed direct focal measurement of FA and MD in cortical lesions.

Purpose To validate adipose tissue magnetic resonance imaging (atMRI) for rapid, quantitative volumetry of visceral adipose tissue (VAT) and total adipose tissue (TAT). Materials and Methods Data was acquired on normal adults and clinically-overweight girls with IRB approval/parental consent using sagittal 6-echo 3D-SPGR (26-sec single-breath-hold) at 3T. Fat-fraction images were reconstructed with quantitative corrections, permitting measurement of a physiologically-based fat-fraction threshold in normals to identify adipose tissue, for automated measurement of TAT and semi-automated measurement of VAT. TAT accuracy was validated using oil phantoms and in vivo TAT/VAT measurements validated with manual segmentation. Group comparisons were performed between normals and overweight girls using TAT, VAT, VAT-TAT-ratio (VTR), body-mass-index (BMI), waist circumference, and waist-hip-ratio (WHR). Results Oil phantom measurements were highly accurate (< 3% error). The measured adipose fat-fraction threshold was 96% ± 2%. VAT and TAT correlated strongly to manual segmentation (normals r2 ≥ 0.96, overweight girls r2 ≥ 0.99). VAT segmentation required 30 ± 11 minutes/subject (14 ± 5 sec/slice) using atMRI, versus 216 ± 73 minutes/subject (99 ± 31 sec/slice) manually. Group discrimination was significant using WHR (p < 0.001) and VTR (p = 0.004). Conclusion The atMRI technique permits rapid, accurate measurements of TAT, VAT and VTR.

Background-Quantitative measures derived from MRI have been widely investigated as noninvasive biomarkers in multiple sclerosis (MS). However, the correlation of single measures with Expanded Disability Status Scale (EDSS) is poor, especially for studies with large population samples.

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