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Nicolas Toussaint

Researcher at King's College London

Publications -  58
Citations -  1002

Nicolas Toussaint is an academic researcher from King's College London. The author has contributed to research in topics: Diffusion MRI & Imaging phantom. The author has an hindex of 14, co-authored 57 publications receiving 809 citations. Previous affiliations of Nicolas Toussaint include University of Strasbourg & St Thomas' Hospital.

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Proceedings Article

MedINRIA: Medical Image Navigation and Research Tool by INRIA

TL;DR: The MedINRIA software is a collection of tools that optimally exploit various types of data that provides state-of-the-art algorithms while keeping a user-friendly graph-ical interface and focuses on the features that make interactions with data even more intuitive.
Journal ArticleDOI

In vivo human cardiac fibre architecture estimation using shape-based diffusion tensor processing.

TL;DR: This article presents a method to analyse the fibre architecture of the left ventricle (LV) using shape-based transformation into a normalised Prolate Spheroidal coordinate frame and shows the advantages of using curvilinear coordinates both for the anaylsis and the interpolation of cardiac DTI information.
Book ChapterDOI

In vivo human 3D cardiac fibre architecture: reconstruction using curvilinear interpolation of diffusion tensor images

TL;DR: This work proposes a method for the complete 3D reconstruction of cardiac fibre architecture in the left ventricular myocardium from sparse in vivo DTI slices, which is believed to be the first reconstruction of in vivo human 3D cardiac fibre structure.
Journal ArticleDOI

ApoE influences regional white-matter axonal density loss in Alzheimer's disease

TL;DR: NODDI provides tissue-specific microstructural metrics ofwhite-matter tract damage in YOAD, including NDI which correlates with focal cognitive deficits, and APOEε4 status is associated with different patterns of white-matter neurodegeneration.
Journal ArticleDOI

Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging

TL;DR: The method is more consistent than human annotation and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions and is demonstrated by integrating shadow confidence maps into tasks such as ultrasound image classification, multi-view image fusion, and automated biometric measurements.