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Diffusional KurtosisImaging in theDiffusion Imaging inPython Project Rafael Neto HenriquesMarta CorreiaMaurizio Marrale2021 год

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project
статья из журнала

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Аннотация: Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project—a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience.
Год издания: 2021
Авторы: Rafael Neto Henriques, Marta Correia, Maurizio Marrale, Elizabeth Huber, John Kruper, Serge Koudoro, Jason D. Yeatman, Eleftherios Garyfallidis, Ariel Rokem
Издательство: Frontiers Media
Источник: Frontiers in Human Neuroscience
Ключевые слова: Advanced Neuroimaging Techniques and Applications, Advanced MRI Techniques and Applications, MRI in cancer diagnosis
Другие ссылки: Frontiers in Human Neuroscience (PDF)
Frontiers in Human Neuroscience (HTML)
DOAJ (DOAJ: Directory of Open Access Journals) (HTML)
Apollo (University of Cambridge) (PDF)
Apollo (University of Cambridge) (HTML)
PubMed Central (HTML)
bioRxiv (Cold Spring Harbor Laboratory) (HTML)
PubMed (HTML)