We describe a technique to simultaneously estimate a local neural fiber model and trace out its path. Existing techniques estimate the local fiber orientation at each voxel indepen...
James G. Malcolm, Martha Elizabeth Shenton, Yogesh...
Abstract. We propose a novel method, fMRI-Informed Regional Estimation (FIRE), which utilizes information from fMRI in E/MEG source reconstruction. FIRE takes advantage of the spat...
A general-purpose deformable registration algorithm referred
to as ”DRAMMS” is presented in this paper. DRAMMS adds to the
literature of registration methods that bridge betw...
Many recent single-shell high angular resolution diffusion imaging reconstruction techniques have been introduced to reconstruct orientation distribution functions (ODF) that only ...
Maxime Descoteaux, Rachid Deriche, Denis Le Bihan,...
Abstract. A new method is introduced for estimating single-trial magnetoor electro-encephalography (M/EEG), based on a non-linear fit of timefrequency atoms. The method can be appl...
This paper presents a novel statistical fuzzy-segmentation method for diffusion tensor (DT) images and magnetic resonance (MR) images. Typical fuzzy-segmentation schemes, e.g. thos...
We introduce Localized Components Analysis (LoCA) for describing surface shape variation in an ensemble of biomedical objects using a linear subspace of spatially localized shape c...
Dan A. Alcantara, Owen T. Carmichael, Eric Delson,...
A fully learning-based framework has been presented for deformable registration of MR brain images. In this framework, the entire brain is first adaptively partitioned into a numbe...
Abstract. We describe a new approach for estimating the posterior probability of tissue labels. Conventional likelihood models are combined with a curve length prior on boundaries,...