No abstract

A 5-year follow-up study was performed on 89 patients who had undergone brain magnetic resonance imaging (MRI) at presentation with an acute clinically isolated syndrome of the optic nerves, brainstem or spinal cord of a type suggestive of multiple sclerosis. At presentation, MRI was abnormal, revealing one or more asymptomatic cerebral white matter lesions in 57 (64%), and was normal in 32 (36%). At follow-up, progression to clinically definite multiple sclerosis had occurred in 37 out of 57 (65%) with an abnormal MRI and one out of 32 (3%) with normal MRI. Human leucocyte antigen (HLA) typing was performed in 70 patients and cerebrospinal fluid (CSF) was examined at presentation in 36. The presence of HLA-DR2 antigen or cerebrospinal fluid oligoclonal IgG bands were both associated with a significantly increased risk of progression to multiple sclerosis, but MRI was much more powerful in predicting outcome. The presence of four or more MRI lesions at presentation was associated with a higher rate of progression to multiple sclerosis, more frequent development of moderate or severe disabilities and a greater number of new MRI lesions at follow-up. The results indicate that brain MRI at presentation with a clinically isolated syndrome suggestive of multiple sclerosis is a powerful predictor of the clinical course over the next 5 years. This observation, combined with an ability to detect other sometimes treatable disorders which can also cause such syndromes, suggests that MRI is the investigation of choice in evaluating this group of patients.

Diffusion-Weighted MRI (DW-MRI) is subject to random noise yielding measures that are different from their real values, and thus biasing the subsequently estimated tensors. The Non-Local Means (NLMeans) filter has recently been proposed to denoise MRI with high signal-to-noise ratio (SNR). This filter has been shown to allow the best restoration of image intensities for the estimation of diffusion tensors (DT) compared to state-of-the-art methods. However, for DW-MR images with high b-values (and thus low SNR), the noise, which is strictly Rician-distributed, can no longer be approximated as additive white Gaussian, as implicitly assumed in the classical formulation of the NLMeans. High b-values are typically used in high angular resolution diffusion imaging (HARDI) or q-space imaging (QSI), for which an optimal restoration is critical. In this paper, we propose to adapt the NLMeans filter to Rician noise corrupted data. Validation is performed on synthetic data and on real data for both conventional MR images and DT images. Our adaptation outperforms the original NLMeans filter in terms of peak-signal-to-noise ratio (PSNR) for DW-MRI.