![]() ![]() ![]() ![]() Registration of DW-MRIs is a key step in population studies or brain atlases construction, among other important tasks. This information can be used to estimate the local orientation of fiber bundles, since water diffusion is favored along the fiber orientation, providing critical information for neuroscience and clinical studies ( Jones et al., 1999 Basser and Jones, 2002 Filler, 2009). In particular, we show that AI improves the registration accuracy in many cases over existing state-of-the-art algorithms, while providing registered raw DW-MRI data, which can be used for any subsequent analysis.ĭiffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive imaging technique that measures the diffusion of water molecules in biological tissues, such as the brain’s white matter, along several gradient directions. We demonstrate that AI registration of DW-MRIs is a powerful alternative to volume and tensor-based approaches. Moreover, we generalize the concept of angular interpolation (AI) to non-linear image registration, and implement it in the FMRIB Software Library (FSL). In this work, we first extensively compare the performance of registration algorithms based on (i) angular interpolation, (ii) non-diffusion-weighted scalar volume (b0), and (iii) diffusion tensor image (DTI). A new algorithm based on angular interpolation of the diffusion-weighted volumes was proposed for affine registration, and does not rely on any specific local diffusion model. ODF-based approaches are more recent and computationally challenging, but also better describe complex fiber configurations thereby potentially improving the accuracy of DW-MRI registration. Tensor-based registration methods rely on a model that does not completely capture the information contained in DW-MRIs, and largely depends on the accurate estimation of tensors. Models such as the diffusion tensor or orientation distribution function (ODF) have been used for this purpose. Alternatively, model-based registration algorithms have been proposed to exploit information on the preferred fiber orientation(s) at each voxel. Given the high dimensionality of the data, registration is usually performed by relying on scalar representative images, such as the fractional anisotropy (FA) and non-diffusion-weighted (b0) images, thereby ignoring much of the directional information conveyed by DW-MR datasets itself. Registration of diffusion-weighted magnetic resonance images (DW-MRIs) is a key step for population studies, or construction of brain atlases, among other important tasks. 2Electrical and Computer Engineering, Computer Science, and Biomedical Engineering, Duke University, Durham, NC, USA.1Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN, USA.Duarte-Carvajalino 1*, Guillermo Sapiro 2, ![]()
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