48 research outputs found

    Soluble urokinase plasminogen activator receptor (suPAR) levels predict damage accrual in patients with recent-onset systemic lupus erythematosus

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    © 2019 The Authors Objective: The soluble urokinase plasminogen activator receptor (suPAR) has potential as a prognosis and severity biomarker in several inflammatory and infectious diseases. In a previous cross-sectional study, suPAR levels were shown to reflect damage accrual in cases of systemic lupus erythematosus (SLE). Herein, we evaluated suPAR as a predictor of future organ damage in recent-onset SLE. Methods: Included were 344 patients from the Systemic Lupus International Collaborating Clinics (SLICC) Inception Cohort who met the 1997 American College of Rheumatology classification criteria with 5-years of follow-up data available. Baseline sera from patients and age- and sex-matched controls were assayed for suPAR. Organ damage was assessed annually using the SLICC/ACR damage index (SDI). Results: The levels of suPAR were higher in patients who accrued damage, particularly those with SDI≥2 at 5 years (N = 32, 46.8% increase, p = 0.004), as compared to patients without damage. Logistic regression analysis revealed a significant impact of suPAR on SDI outcome (SDI≥2; OR = 1.14; 95% CI 1.03–1.26), also after adjustment for confounding factors. In an optimized logistic regression to predict damage, suPAR persisted as a predictor, together with baseline disease activity (SLEDAI-2K), age, and non-Caucasian ethnicity (model AUC = 0.77). Dissecting SDI into organ systems revealed higher suPAR levels in patients who developed musculoskeletal damage (SDI≥1; p = 0.007). Conclusion: Prognostic biomarkers identify patients who are at risk of acquiring early damage and therefore need careful observation and targeted treatment strategies. Overall, suPAR constitutes an interesting biomarker for patient stratification and for identifying SLE patients who are at risk of acquiring organ damage during the first 5 years of disease

    Zero and First-Order Phase Shift Correction for Field Map Estimation with Dual-Echo GRE Using Bipolar Gradients

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    A simple phase error correction technique used for field map estimation with a generally available dual-echo gradient-echo (GRE) sequence is presented. Magnetic field inhomogeneity maps estimated using two separate GRE volume acquisitions at different echo times are prone to dynamic motion errors between acquisitions. By using the dual-echo sequence, the data are collected during two back-to-back readout gradients in opposite polarity after a single radio frequency pulse, and interecho motion artifacts and alignment errors in field map estimation can be factored out. Residual phase error from the asymmetric readout pulses is modeled as an affine term in the readout direction. Results from phantom and human data suggest that the first-order phase correction term stays constant over time and, hence, can be applied to different data acquired with the same protocol over time. The zero-order phase correction term may change with time and is estimated empirically for different scans.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85843/1/Fessler31.pd

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

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    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

    Diffusion Weighted Image Denoising using overcomplete Local PCA

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    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. This work has been also partially supported by the French grant "HR-DTI" ANR-10-LABX-57 funded by the TRAIL from the French Agence Nationale de la Recherche within the context of the Investments for the Future program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Manjón Herrera, JV.; Coupé, P.; Concha, L.; Buades, A.; Collins, L.; Robles Viejo, M. (2013). Diffusion Weighted Image Denoising using overcomplete Local PCA. PLoS ONE. 8(9):1-12. https://doi.org/10.1371/journal.pone.0073021S11289Sundgren, P. C., Dong, Q., Gómez-Hassan, D., Mukherji, S. K., Maly, P., & Welsh, R. (2004). Diffusion tensor imaging of the brain: review of clinical applications. Neuroradiology, 46(5), 339-350. doi:10.1007/s00234-003-1114-xJohansen-Berg, H., & Behrens, T. E. (2006). Just pretty pictures? What diffusion tractography can add in clinical neuroscience. Current Opinion in Neurology, 19(4), 379-385. doi:10.1097/01.wco.0000236618.82086.01Jones DK, Basser PJ (2004) Squashing peanuts and smashing pumpkins: how noise distorts diffusion-weighted MR data. Magnetic Resonance in Medicine 52, 979–993.Chen, B., & Hsu, E. W. (2005). Noise removal in magnetic resonance diffusion tensor imaging. Magnetic Resonance in Medicine, 54(2), 393-401. doi:10.1002/mrm.20582Aja-Fernandez, S., Niethammer, M., Kubicki, M., Shenton, M. E., & Westin, C.-F. (2008). Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Transactions on Medical Imaging, 27(10), 1389-1403. doi:10.1109/tmi.2008.920609Basu S, Fletcher T, Whitaker R (2006) Rician noise removal in diffusion tensor MRI. MICCAI2006: 9,117–25.Hamarneh, G., & Hradsky, J. (2007). Bilateral Filtering of Diffusion Tensor Magnetic Resonance Images. IEEE Transactions on Image Processing, 16(10), 2463-2475. doi:10.1109/tip.2007.904964Xu, Q., Anderson, A. W., Gore, J. C., & Ding, Z. (2010). Efficient anisotropic filtering of diffusion tensor images. Magnetic Resonance Imaging, 28(2), 200-211. doi:10.1016/j.mri.2009.10.001Parker, G. J. M., Schnabel, J. A., Symms, M. R., Werring, D. J., & Barker, G. J. (2000). Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging. Journal of Magnetic Resonance Imaging, 11(6), 702-710. doi:10.1002/1522-2586(200006)11:63.0.co;2-aWeickert J, Brox T (2002) Diffusion and regularization of vector and matrix valued images. Saarland Department of Mathematics, Saarland University. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.195Wang, Z., Vemuri, B. C., Chen, Y., & Mareci, T. H. (2004). A Constrained Variational Principle for Direct Estimation and Smoothing of the Diffusion Tensor Field From Complex DWI. IEEE Transactions on Medical Imaging, 23(8), 930-939. doi:10.1109/tmi.2004.831218Reisert, M., & Kiselev, V. G. (2011). Fiber Continuity: An Anisotropic Prior for ODF Estimation. IEEE Transactions on Medical Imaging, 30(6), 1274-1283. doi:10.1109/tmi.2011.2112769Fillard, P., Pennec, X., Arsigny, V., & Ayache, N. (2007). Clinical DT-MRI Estimation, Smoothing, and Fiber Tracking With Log-Euclidean Metrics. IEEE Transactions on Medical Imaging, 26(11), 1472-1482. doi:10.1109/tmi.2007.899173Poon PK, Wei-Ren Ng, Sridharan V (2009) Image Denoising with Singular Value Decompositon and Principal Component Analysis. http://www.u.arizona.edu/~ppoon/ImageDenoisingWithSVD.pdfZhang, L., Dong, W., Zhang, D., & Shi, G. (2010). Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recognition, 43(4), 1531-1549. doi:10.1016/j.patcog.2009.09.023Deledalle C, Salmon J, Dalalyan A (2011) Image denoising with patch based PCA: local versus global. BMVC2011.Manjón JV, Thacker N, Lull JJ, Garcia-Martí G, Martí-Bonmatí L, et al.. (2009) Multicomponent MR Image Denoising. International Journal of Biomedical imaging, Article ID 756897.Bao, L., Robini, M., Liu, W., & Zhu, Y. (2013). Structure-adaptive sparse denoising for diffusion-tensor MRI. Medical Image Analysis, 17(4), 442-457. doi:10.1016/j.media.2013.01.006Strang G (1976) Linear Algebra and Its Applications Academic. New York,19802.Jolliffe IT (1986) Principal component analysis (Vol. 487). New York: Springer-Verlag.Manjón, J. V., Coupé, P., Buades, A., Louis Collins, D., & Robles, M. (2012). New methods for MRI denoising based on sparseness and self-similarity. Medical Image Analysis, 16(1), 18-27. doi:10.1016/j.media.2011.04.003Coifman R, Donoho DL (1995) Translation Invariant Denoising, Wavelets and Statistics. Anestis Antoniadis, ed. Springer Verlag Lecture Notes.Nowak, R. D. (1999). Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Transactions on Image Processing, 8(10), 1408-1419. doi:10.1109/83.791966Koay CG, Basser PJ (2006) Analytically exact correction scheme for signal extraction from noisy magnitude MR signals. J Magn Reson, 179,317–322.Coupé, P., Manjón, J. V., Gedamu, E., Arnold, D., Robles, M., & Collins, D. L. (2010). Robust Rician noise estimation for MR images. Medical Image Analysis, 14(4), 483-493. doi:10.1016/j.media.2010.03.001Close, T. G., Tournier, J.-D., Calamante, F., Johnston, L. A., Mareels, I., & Connelly, A. (2009). A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms. NeuroImage, 47(4), 1288-1300. doi:10.1016/j.neuroimage.2009.03.077Coupe, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., & Barillot, C. (2008). An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images. IEEE Transactions on Medical Imaging, 27(4), 425-441. doi:10.1109/tmi.2007.906087Manjón, J. V., Coupé, P., Martí-Bonmatí, L., Collins, D. L., & Robles, M. (2009). Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192-203. doi:10.1002/jmri.22003Coupé P, Hellier P, Prima S, Kervrann C, Barillot C (2008) 3D Wavelet Subbands Mixing for Image Denoising. International Journal of Biomedical Imaging. Article ID 590183.Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., … Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208-S219. doi:10.1016/j.neuroimage.2004.07.051Basser, P. J., Mattiello, J., & Lebihan, D. (1994). Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo. Journal of Magnetic Resonance, Series B, 103(3), 247-254. doi:10.1006/jmrb.1994.103
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