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Typically, magnetic resonance imaging (MRI) analysis is performed on magnitude data, and multiple echo T2 data consist of numerous images of the same slice taken with different echo spacing, giving voxel-wise temporal sampling of the noise as the signals decay according to T2 relaxation. Magnitude T2 decay data has Rician distributed noise which is characterized by a change in the noise distribution from Gaussian, through a transitional region, to Rayleigh as the signal to noise ratio decreases with increasing echo time. Non-Gaussian noise distributions may produce errors in the commonly applied non-negative least squares (NNLS) algorithm that is used to assess multiple echo decays for compartmentalized water environments through the creation of T2 distributions. Typically, Gaussian noise is sought by performing spatial-based phase correction on the MRI data however, these methods cannot capitalize on the temporal information available from multiple echo T2 acquisitions. Here we describe a temporal phase correction (TPC) algorithm that utilizes the temporal noise information available in multiple echo T2 acquisitions to put the relevant decay information in the Real portion of the decay data and leave only noise in the Imaginary portion. We apply this TPC algorithm to create real-valued multiple echo T2 data from human subjects measured at 1.5 T. We show that applying TPC causes changes in the T2 distribution estimates; notably the possible resolution of separate extracellular and intracellular water environments, and the disappearance of the commonly labeled cerebrospinal fluid peak, which might be an artefact observed in many previously published multiple echo T2 analyses., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Exponential decays are fundamental to magnetic resonance imaging, yet adequately sampling and analyzing multiexponential decays is rarely attempted. The advantage of multiexponential analysis is the quantification of sub-voxel structure caused by water compartmentalization, with application as a non-invasive imaging biomarker for myelin. We have developed AnalyzeNNLS, software designed specifically for multiexponential decay image analysis that has a user-friendly graphical user interface and can analyze data from many MR manufacturers. AnalyzeNNLS is a simple, platform independent analysis tool that was created using the extensive mathematical and visualization libraries in Matlab, and released as open source code allowing scientists to evaluate, scrutinize, improve, and expand., (Copyright © 2010 Elsevier Inc. All rights reserved.)