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Author: Tabelow, Karsten × DDC: 570 ×

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    Denoising for Improved Parametric MRI of the Kidney: Protocol for Nonlocal Means Filtering
    (Totowa, NJ : Humana Press, 2021) Starke, Ludger; Tabelow, Karsten; Niendorf, Thoralf; Pohlmann, Andreas; Pohlmann, Andreas; Niendorf, Thoralf
    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.
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    Example dataset for the hMRI toolbox
    (Amsterdam [u.a.] : Elsevier, 2019) Callaghan, Martina F.; Lutti, Antoine; Ashburner, John; Balteau, Evelyne; Corbin, Nadège; Draganski, Bogdan; Helms, Gunther; Kherif, Ferath; Leutritz, Tobias; Mohammadi, Siawoosh; Phillips, Christophe; Reimer, Enrico; Ruthotto, Lars; Seif, Maryam; Tabelow, Karsten; Ziegler, Gabriel; Weiskopf, Nikolaus
    The hMRI toolbox is an open-source toolbox for the calculation of quantitative MRI parameter maps from a series of weighted imaging data, and optionally additional calibration data. The multi-parameter mapping (MPM) protocol, incorporating calibration data to correct for spatial variation in the scanner's transmit and receive fields, is the most complete protocol that can be handled by the toolbox. Here we present a dataset acquired with such a full MPM protocol, which is made freely available to be used as a tutorial by following instructions provided on the associated toolbox wiki pages, which can be found at http://hMRI.info, and following the theory described in: hMRI – A toolbox for quantitative MRI in neuroscience and clinical research [1].