Osteopontin and disease activity in patients with recent-onset systemic lupus erythematosus:results from the SLICC Inception Cohort

Objective. In cross-sectional studies, elevated osteopontin (OPN) levels have been proposed to reflect, and/or precede, progressive organ damage and disease severity in systemic lupus erythematosus (SLE). We aimed, in a cohort of patients with recent-onset SLE, to determine whether raised serum OPN levels precede damage and/or are associated with disease activity or certain disease phenotypes. Methods. We included 344 patients from the Systemic Lupus International Collaborating Clinics (SLICC) Inception Cohort who had 5 years of followup data available. All patients fulfilled the 1997 American College of Rheumatology (ACR) criteria. Baseline sera from patients and from age- and sex-matched population-based controls were analyzed for OPN using ELISA. Disease activity and damage were assessed at each annual followup visit using the SLE Disease Activity Index 2000 (SLEDAI-2K) and the SLICC/ACR damage index (SDI), respectively. Results. Compared to controls, baseline OPN was raised 4-fold in SLE cases (p < 0.0001). After relevant adjustments in a binary logistic regression model, OPN levels failed to significantly predict global damage accrual defined as SDI ≥ 1 at 5 years. However, baseline OPN correlated with SLEDAI-2K at enrollment into the cohort (r = 0.27, p < 0.0001), and patients with high disease activity (SLEDAI-2K ≥ 5) had raised serum OPN (p < 0.0001). In addition, higher OPN levels were found in patients with persistent disease activity (p = 0.0006), in cases with renal involvement (p < 0.0001) and impaired estimated glomerular filtration rate (p = 0.01). Conclusion. The performance of OPN to predict development of organ damage was not impressive. However, OPN associated significantly with lupus nephritis and with raised disease activity at enrollment, as well as over time

Rapid measurement of intravoxel incoherent motion (IVIM) derived perfusion fraction for clinical magnetic resonance imaging

Objective This study aimed to investigate the reliability of intravoxel incoherent motion (IVIM) model derived parameters D and f and their dependence on b value distributions with a rapid three b value acquisition protocol. Materials and methods Diffusion models for brain, kidney, and liver were assessed for bias, error, and reproducibility for the estimated IVIM parameters using b values 0 and 1000, and a b value between 200 and 900, at signal-to-noise ratios (SNR) 40, 55, and 80. Relative errors were used to estimate optimal b value distributions for each tissue scenario. Sixteen volunteers underwent brain DW-MRI, for which bias and coefficient of variation were determined in the grey matter. Results Bias had a large influence in the estimation of D and f for the low-perfused brain model, particularly at lower b values, with the same trends being confirmed by in vivo imaging. Significant differences were demonstrated in vivo for estimation of D (P = 0.029) and f (P < 0.001) with [300,1000] and [500,1000] distributions. The effect of bias was considerably lower for the high-perfused models. The optimal b value distributions were estimated to be brain500,1000, kidney300,1000, and liver200,1000. Conclusion IVIM parameters can be estimated using a rapid DW-MRI protocol, where the optimal b value distribution depends on tissue characteristics and compromise between bias and variability

Diffusion Weighted Image Denoising using overcomplete Local PCA

Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.This work has been supported by the Spanish grant TIN2011-26727 from Ministerio de Ciencia e Innovacion. 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