Tensor Regression with Applications in Neuroimaging Data Analysis

Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data.Comment: 27 pages, 4 figure

Denoising for improved parametric MRI of the kidney: protocol for nonlocal means filtering

In order to tackle the challenges caused by the variability in estimated MRI parameters (e.g., T(2)* and T(2)) due to low SNR a number of strategies can be followed. One approach is postprocessing of the acquired data with a filter. The basic idea is that MR images possess a local spatial structure that is characterized by equal, or at least similar, noise-free signal values in vicinities of a location. Then, local averaging of the signal reduces the noise component of the signal. In contrast, nonlocal means filtering defines the weights for averaging not only within the local vicinity, bur it compares the image intensities between all voxels to define "nonlocal" weights. Furthermore, it generally compares not only single-voxel intensities but small spatial patches of the data to better account for extended similar patterns. Here we describe how to use an open source NLM filter tool to denoise 2D MR image series of the kidney used for parametric mapping of the relaxation times T(2)* and T(2).This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers

Correlation of histopathology and symptoms in allergic and non-allergic patients with chronic rhinosinusitis

The aim of this study was to estimate and compare some histopathologic predictors of efficacy of endoscopic sinus surgery (ESS) in allergic and non-allergic patients with chronic rhinosinusitis (CRS). Symptomatology was rated in 50 allergic and 50 non-allergic patients prior to as well as 12 and 24 months after surgery. Specimens taken during the procedure were scored for goblet cells, subepithelial thickening, mast cells and eosinophils. The correlation between histopathology and symptoms was evaluated. Goblet cells and subepithelial thickening were the best predictors in both groups of patients. These parameters are significant global outcome predictors for allergic, but not for non-allergic patients. It is concluded that certain histopathologic parameters in CRS correlate with certain symptoms. Certain histopathologic changes are predictable for persistence of some bothersome symptoms after ESS. Pathologic evaluation might give prediction of response to ESS in allergic and non-allegic patients with CRS