کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6024652 | 1580885 | 2015 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes
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موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes](/preview/png/6024652.png)
چکیده انگلیسی
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 predicts what to expect. In this paper we describe a novel way to represent and make predictions about diffusion MRI data. It is based on a Gaussian process on one or several spheres similar to the Geostatistical method of “Kriging”. We present a choice of covariance function that allows us to accurately predict the signal even from voxels with complex fibre patterns. For multi-shell data (multiple non-zero b-values) the covariance function extends across the shells which means that data from one shell is used when making predictions for another shell.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 122, 15 November 2015, Pages 166-176
Journal: NeuroImage - Volume 122, 15 November 2015, Pages 166-176
نویسندگان
Jesper L.R. Andersson, Stamatios N. Sotiropoulos,