Abstract
In order to tackle the challenges caused by the variability in estimated MRI parameters (e.g., T2* and T2) 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 T2* and T2.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.
Highlights
Mapping of the transverse relaxation times T2* and T2 requires series of MR images with different echo times TE: The maps are obtained by fitting the exponential model curve S(TE) 1⁄4 S0 exp(ÀTE/T2(*)) to the signal intensities of each image pixel with increasing TE
This approach is inherently associated with decreasing signal-to-noise ratio (SNR) for the image volumes obtained for longer echo times
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
Summary
Mapping of the transverse relaxation times T2* and T2 (or relaxation rates R2* 1⁄4 1/T2* and R2 1⁄4 1/T2) requires series of MR images with different echo times TE: The maps are obtained by fitting the exponential model curve S(TE) 1⁄4 S0 exp(ÀTE/T2(*)) to the signal intensities of each image pixel with increasing TE. Acceleration techniques like parallel imaging [5, 6] or compressed sensing [7] allow for more signal averaging within the same time span Image postprocessing provides another alternative for reducing the variability of the data. The specific methods are based on different methodologies, like anisotropic diffusion [8–11], nonlocal means [12], penalization techniques [13, 14] wavelet filtering [15], model-based methods [16, 17], the propagation-separation approach [18, 19], or other local techniques [20, 21] Many of these techniques have been applied to diffusion-weighted MRI data but are applicable for relaxometry measurements. While the first is a straightforward block-wise implementation of the NLM filter, the second proposes a novel method exploiting the redundancy of information in MR relaxometry data (see Note 1 for implementation details) Both filters reduce the noise level in low SNR relaxometry data with version 2 offering slightly superior results (see Note 2 for a detailed example).
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