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Idiopathic inflammatory demyelinating lesions (IIDL) of the brain usually present with a morphologic pattern characteristic of multiple sclerosis (MS). Atypical appearances of IIDLs also exist, however, and can pose significant diagnostic problems and uncertainty regarding prognosis and adequate therapy. We attempted to improve upon this situation by reviewing the literature. We performed a PubMed search from January 1984 through December 2004 for articles in English reporting on IIDLs which had been considered as morphologically atypical (66 articles; 270 cases reported). From these publications 69 individual patient reports allowed the extraction of adequate information on magnetic resonance imaging (MRI) and associated disease characteristics. Reported atypical IIDLs most frequently manifested as large ring-like lesions (n = 27) which are now considered quite suggestive of an antibodymediated form of MS. Truly atypical IIDLs were less common and exhibited appearances which we termed megacystic (n = 8), Balolike (n = 11) and diffusely infiltrating (n = 11). Despite limitations imposed by the absence of original data the inter-rater agreement in defining these subtypes of atypical IIDLs was moderate to substantial (kappa 0.48–0.68) and we noted trends for their association with certain demographic, clinical and paraclinical variables. We suggest that IIDLs reported as atypical in the literature can be segregated into several distinct subtypes based on their MRI appearance. The recognition of these patterns may be useful for the differential diagnosis and for a future classification. Because of the limitations inherent in our review this will have to be confirmed by a prospective registry. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) plaques and age related white matter lesions (WML) are of similar morphological appearance on T2 weighted MRI. Therefore their differentiation is sometimes crucial. Proton magnetic resonance spectroscopy (1H–MRS) adds metabolic information to conventional imaging and may help to distinguish inflammatory MS plaques from vascular related WML. This study was performed to evaluate the metabolite pattern in MS plaques and WML. 15 MS patients, 14 elderly individuals with WML and 16 controls were investigated by conventional MRI and short echo quantitative 1H–MRS (TE: 30ms, TR: 3000ms). The mean metabolite concentrations in normal control white matter (NCWM), MS plaques and WML were: t–NAA: 8.96 mmol/l (SD:0.93) vs 6.79 mmol/l (SD: 1.99) vs 7.18 mmol/l (SD: 1.41); Cho: 1.66 mmol/l (SD: 0.4) vs 1.49 mmol/l (SD: 0.45) vs 1.46 mmol/l (SD: 0.34); PCr: 5.64 mmol/l (SD: 0.83) vs 4.9mmol/l (SD: 1.3) vs 4.95 mmol/l (SD: 0.86); myo– Ins: 4.57 mmol/l (SD:1.05) vs 6.34 mmol/l (SD: 2.03) vs 4.5 mmol/l (SD: 0.96). t–NAA reduction in MS plaques and WML was significant compared with controls (p ≤ 0.001). MS plaques showed significantly elevated myo– Ins concentrations compared with controls (p = 0.002) and to WML (p = 0.003). In summary MS plaques and WML show a decrease in their t–NAA concentrations compared with controls. Elevated concentrations of myo–Ins in MS plaques but not in WML makes this metabolite of special interest for their differentiation. [ABSTRACT FROM AUTHOR]

Background: Recent studies have created awareness that facial features can be reconstructed from high-resolution MRI. Therefore, data sharing in neuroimaging requires special attention to protect participants' privacy. Facial features removal (FFR) could alleviate these concerns. We assessed the impact of three FFR methods on subsequent automated image analysis to obtain clinically relevant outcome measurements in three clinical groups.Methods: FFR was performed using QuickShear, FaceMasking, and Defacing. In 110 subjects of Alzheimer's Disease Neuroimaging Initiative, normalized brain volumes (NBV) were measured by SIENAX. In 70 multiple sclerosis patients of the MAGNIMS Study Group, lesion volumes (WMLV) were measured by lesion prediction algorithm in lesion segmentation toolbox. In 84 glioblastoma patients of the PICTURE Study Group, tumor volumes (GBV) were measured by BraTumIA. Failed analyses on FFR-processed images were recorded. Only cases in which all image analyses completed successfully were analyzed. Differences between outcomes obtained from FFR-processed and full images were assessed, by quantifying the intra-class correlation coefficient (ICC) for absolute agreement and by testing for systematic differences using paired t tests.Results: Automated analysis methods failed in 0-19% of cases in FFR-processed images versus 0-2% of cases in full images. ICC for absolute agreement ranged from 0.312 (GBV after FaceMasking) to 0.998 (WMLV after Defacing). FaceMasking yielded higher NBV (p = 0.003) and WMLV (p ≤ 0.001). GBV was lower after QuickShear and Defacing (both p < 0.001).Conclusions: All three outcome measures were affected differently by FFR, including failure of analysis methods and both "random" variation and systematic differences. Further study is warranted to ensure high-quality neuroimaging research while protecting participants' privacy.Key Points: • Protecting participants' privacy when sharing MRI data is important. • Impact of three facial features removal methods on subsequent analysis was assessed in three clinical groups. • Removing facial features degrades performance of image analysis methods. [ABSTRACT FROM AUTHOR]