Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data

• Published in: NeuroImage (Orlando, Fla.) Vol. 103; pp. 411 - 426
• Main Authors: Jeurissen, Ben, Tournier, Jacques-Donald, Dhollander, Thijs, Connelly, Alan, Sijbers, Jan
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.12.2014; Elsevier; Elsevier Limited
• Subjects: Algorithms; Biological and medical sciences; Brain - anatomy & histology; Brain Mapping - methods; Brain research; Cerebrospinal fluid; Diffusion; Diffusion Magnetic Resonance Imaging - methods; Diffusion MRI; Estimates; Fibre orientation distribution functions; Fundamental and applied biological sciences. Psychology; Humans; Image Processing, Computer-Assisted - methods; Magnetic resonance imaging; Multi-shell acquisition; Multiple tissue types; Neurosciences; NMR; Nuclear magnetic resonance; Pattern Recognition, Automated - methods; Spherical deconvolution; Substantia alba; Substantia grisea; Tissue segmentation; Vertebrates: nervous system and sense organs; White Matter - anatomy & histology; Multi-shell acquisition; Diffusion MRI; Tissue segmentation; Spherical deconvolution; Fibre orientation distribution functions; Multiple tissue types; Segmentation; Distribution; Orientation; Nuclear magnetic resonance imaging
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2014.07.061

Constrained spherical deconvolution (CSD) has become one of the most widely used methods to extract white matter (WM) fibre orientation information from diffusion-weighted MRI (DW-MRI) data, overcoming the crossing fibre limitations inherent in the diffusion tensor model. It is routinely used to obtain high quality fibre orientation distribution function (fODF) estimates and fibre tractograms and is increasingly used to obtain apparent fibre density (AFD) measures. Unfortunately, CSD typically only supports data acquired on a single shell in q-space. With multi-shell data becoming more and more prevalent, there is a growing need for CSD to fully support such data. Furthermore, CSD can only provide high quality fODF estimates in voxels containing WM only. In voxels containing other tissue types such as grey matter (GM) and cerebrospinal fluid (CSF), the WM response function may no longer be appropriate and spherical deconvolution produces unreliable, noisy fODF estimates. The aim of this study is to incorporate support for multi-shell data into the CSD approach as well as to exploit the unique b-value dependencies of the different tissue types to estimate a multi-tissue ODF. The resulting approach is dubbed multi-shell, multi-tissue CSD (MSMT-CSD) and is compared to the state-of-the-art single-shell, single-tissue CSD (SSST-CSD) approach. Using both simulations and real data, we show that MSMT-CSD can produce reliable WM/GM/CSF volume fraction maps, directly from the DW data, whereas SSST-CSD has a tendency to overestimate the WM volume in voxels containing GM and/or CSF. In addition, compared to SSST-CSD, MSMT-CSD can substantially increase the precision of the fODF fibre orientations and reduce the presence of spurious fODF peaks in voxels containing GM and/or CSF. Both effects translate into more reliable AFD measures and tractography results with MSMT-CSD compared to SSST-CSD. •Constrained spherical deconvolution is extended to support multi-shell DW data.•We use the unique b-value dependency of each tissue to estimate a multi-tissue ODF.•We obtain reliable WM/GM/CSF volume fraction maps directly from the DW data.•We obtain more precise WM fibre orientation estimates at the tissue interfaces.•This leads to more accurate apparent fibre density and more reliable fibre tracking.