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Alexandre Bousse

Researcher at University College London

Publications -  70
Citations -  796

Alexandre Bousse is an academic researcher from University College London. The author has contributed to research in topics: Iterative reconstruction & Motion estimation. The author has an hindex of 15, co-authored 66 publications receiving 659 citations. Previous affiliations of Alexandre Bousse include University of Western Brittany & French Institute of Health and Medical Research.

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Journal ArticleDOI

PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography

TL;DR: The PETPVC toolbox described in this paper comprises a suite of methods, both classic and more recent approaches, for the purposes of applying PVC to PET data, and the recovery of both GM and a simulated lesion was improved by combining two PVC techniques together.
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Maximum-Likelihood Joint Image Reconstruction/Motion Estimation in Attenuation-Corrected Respiratory Gated PET/CT Using a Single Attenuation Map

TL;DR: This work provides an insight into positron emission tomography joint image reconstruction/motion estimation (JRM) by maximization of the likelihood, where the probabilistic model accounts for warped attenuation and suggests that a breath-held μ-map can be used.
Journal ArticleDOI

What approach to brain partial volume correction is best for PET/MRI?

TL;DR: Seven novel approaches to partial volume correction were evaluated, including several post-reconstruction methods and several reconstruction methods that incorporate anatomical information, which provided the best recovery with ideal segmentation but were particularly sensitive to mis-registration.
Proceedings ArticleDOI

GPU accelerated rotation-based emission tomography reconstruction

TL;DR: This work proposes an accelerated implementation of a reconstruction algorithm specifically designed to take advantage of the architecture of a General Purpose Graphics Processing Unit (GPGPU) for quantitative reconstruction of radio-tracer activity distribution.
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Markov random field and Gaussian mixture for segmented MRI-based partial volume correction in PET

TL;DR: The proposed segmented magnetic resonance imaging prior-based maximum penalized likelihood deconvolution technique for positron emission tomography (PET) images outperforms the VC algorithm and outperforms SG and RBV corrections when the segmented MRI is inconsistent.