Tractography based on non-invasive diffusion imaging is central to the study of human brain
connectivity. To date, the approach has not been systematically validated in ground truth …

… The latter can be achieved for example by projecting B-spline displacement estimations in
the space of a … (b) Middle and coarse scale predictions of our multi-scale method (Our S3). …

Shift-invariant dictionaries are generated by taking all the possible shifts of a few short patterns.
They are helpful to represent long signals where the same pattern can appear several …

We propose a deformable registration algorithm based on unsupervised learning of a low-dimensional
probabilistic parameterization of deformations. We model registration in a …

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity
studies of the human brain. To date, the approach has not been systematically validated in …

Existing approaches for reconstruction of multiparametric maps with magnetic resonance
fingerprinting (MRF) are currently limited by their estimation accuracy and reconstruction time. …

This work considers the problem of learning an incoherent dictionary that is both adapted to
a set of training data and incoherent so that existing sparse approximation algorithms can …

Objectives The aim of this study was to leverage volumetric quantification of airspace disease
(AD) derived from a superior modality (computed tomography [CT]) serving as ground truth…

We propose a variant of orthogonal matching pursuit (OMP), called LoCOMP, for scalable
sparse signal approximation. The algorithm is designed for shift-invariant signal dictionaries …

Real-world phenomena involve complex interactions between multiple signal modalities. As
a consequence, humans are used to integrate at each instant perceptions from all their …