Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

© 2018, Springer Nature Switzerland AG. Tissue-type partial volume modelling is generally an ill-posed problem in single-shell diffusion MRI. On the other hand, T1w images are typically acquired along with the diffusion data, and allow for an accurate estimation of the tissue partial volume fractions (PVFs). We propose in this paper to compare different data driven approach to predict the T1w-derived PVFs from the diffusion data. The aim is to alleviate the within subject mis-registration between the two modalities. Our experiments show that the random forests is the most accurate and scalable method for predicting the tissue partial volume fractions. Additionally, such predictions can be used to inform the fitting of the two-compartment model to retrieve a diffusion tensor that is not biased by partial volume effects or constraints.

Original publication




Conference paper

Publication Date



11037 LNCS


42 - 51