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Author: Callaghan, Martina F. × Publisher: Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik ×

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    hMRI - A toolbox for using quantitative MRI in neuroscience and clinical research
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2018) Balteau, Evelyne; Tabelow, Karsten; Ashburner, John; Callaghan, Martina F.; Draganski, Bogdan; Helms, Gunther; Kherif, Ferath; Leutritz, Tobias; Lutti, Antoine; Phillips, Christophe; Reimer, Enrico; Ruthotto, Lars; Seif, Maryam; Weiskopf, Nikolaus; Ziegler, Gabriel; Mohammad, Siawoosh
    Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration.We introduce the hMRI-toolbox, an easy-to-use tool openly available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R? 2, proton density PD and magnetisation transfer MT saturation) that can be used for accurate delineation of subcortical brain structures and calculation of standard and novel MRI biomarkers of tissue microstructure. Embedded in the Statistical Parametric Mapping (SPM) framework, it can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps, and it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences. The hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.
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    Simultaneous adaptive smoothing of relaxometry and quantitative magnetization transfer mapping
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2017) Mohammadi, Siawoosh; DAlonzo, Chiara; Ruthotto, Lars; Polzehl, Jörg; Ellerbrock, Isabel; Callaghan, Martina F.; Weiskopf, Nikolaus; Tabelow, Karsten
    Attempts for in-vivo histology require a high spatial resolution that comes with the price of a decreased signal-to-noise ratio. We present a novel iterative and multi-scale smoothing method for quantitative Magnetic Resonance Imaging (MRI) data that yield proton density, apparent transverse and longitudinal relaxation, and magnetization transfer maps. The method is based on the propagation-separation approach. The adaptivity of the procedure avoids the inherent bias from blurring subtle features in the calculated maps that is common for non-adaptive smoothing approaches. The characteristics of the methods were evaluated on a high-resolution data set (500 mym isotropic) from a single subject and quantified on data from a multi-subject study. The results show that the adaptive method is able to increase the signal-to-noise ratio in the calculated quantitative maps while largely avoiding the bias that is otherwise introduced by spatially blurring values across tissue borders. As a consequence, it preserves the intensity contrast between white and gray matter and the thin cortical ribbon.