Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes

Diffusion MRI offers great potential in studying the human brain microstructure and connectivity. However, diffusion images are marred by technical problems, such as image distortions and spurious signal loss. Correcting for these problems is non-trivial and relies on having a mechanism that predict...

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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Fla.), 2015-11, Vol.122, p.166-176
Hauptverfasser:	Andersson, Jesper L.R., Sotiropoulos, Stamatios N.
Format:	Artikel
Sprache:	eng
Schlagworte:	Brain - anatomy & histology Brain - physiology Data Interpretation, Statistical Diffusion Diffusion Magnetic Resonance Imaging - methods Diffusion MRI Diffusion Tensor Imaging - methods Estimates Gaussian process Humans Image Processing, Computer-Assisted - methods Models, Neurological Multi-shell Non-parametric representation Normal Distribution Registration Signal Processing, Computer-Assisted
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