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Boris Mailhe

Researcher at Siemens

Publications -  87
Citations -  2255

Boris Mailhe is an academic researcher from Siemens. The author has contributed to research in topics: Iterative reconstruction & Sparse approximation. The author has an hindex of 16, co-authored 83 publications receiving 1775 citations. Previous affiliations of Boris Mailhe include École Normale Supérieure & French Institute for Research in Computer Science and Automation.

Papers
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Journal ArticleDOI

The challenge of mapping the human connectome based on diffusion tractography

Klaus H. Maier-Hein, +76 more
TL;DR: The encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent) is reported, however, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups.
Journal ArticleDOI

Learning a Probabilistic Model for Diffeomorphic Registration

TL;DR: In this article, a conditional variational autoencoder network is proposed to learn a low-dimensional probabilistic deformation model from data which can be used for the registration and the analysis of deformations.
Proceedings ArticleDOI

Shift-invariant dictionary learning for sparse representations: Extending K-SVD

TL;DR: An unbiased extension of the method used in K-SVD is proposed and evaluated, i.e. a method able to exactly retrieve the original dictionary in a noiseless case.
Posted ContentDOI

Tractography-based connectomes are dominated by false-positive connections

Klaus H. Maier-Hein, +76 more
- 07 Nov 2016 - 
TL;DR: The results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.
Journal ArticleDOI

AIR-MRF: Accelerated iterative reconstruction for magnetic resonance fingerprinting

TL;DR: It is found that the AIR-MRF pipeline provided reduced parameter estimation errors compared to non-iterative and other iterative methods, particularly at shorter sequence lengths, and accelerated dictionary search methods incorporated into the iterative pipeline reduced the reconstruction time at little cost of quality.