Nonlinear diffusion constraints for reconstructing subsampled rotational angiography data

Hélène Langet 1, 2 Aymeric Reshef 2, 3 Cyril Riddell 4 Yves Trousset 4 Arthur Tenenhaus 5 Elisabeth Lahalle 5 Gilles Fleury 5 Nikos Paragios 6, 7, 8, 9, 10, 11
1 Medisys - MedisysResearch Lab
2 GET - GET
3 ENS Paris - École normale supérieure - Paris
4 GE Healthcare - General Electric Medical Systems [Buc]
5 E3S - Supélec Sciences des Systèmes [Gif-sur-Yvette]
6 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
Inria Saclay - Ile de France, Ecole Centrale Paris
7 MAS - Mathématiques Appliquées aux Systèmes - EA 4037
8 IMAGINE [Marne-la-Vallée]
CSTB - Centre Scientifique et Technique du Bâtiment, LIGM - Laboratoire d'Informatique Gaspard-Monge, ENPC - École des Ponts ParisTech
9 LIGM - Laboratoire d'Informatique Gaspard-Monge
10 UPEM - Université Paris-Est Marne-la-Vallée
11 ENPC - École des Ponts ParisTech
Abstract : Interventional imaging with cone-beam C-arm CT often lacks sufficient sampling. Compressed sensing based reconstruction algorithms have shown promising results to improve image quality in this context using sparsity constraints. Compressed sensing theory by itself assumes random measurements and l1 penalties. In practice, benefits are seen with uniform subsampling patterns. Here, we investigate substituting l1 total variation with a nonlinear diffusion constraint and show on a clinical data set that image quality is also improved. This result adds flexibility to the design of CS-based algorithms as C-arm CT images may not be so well approximated by piecewise constant functions.
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Conference papers
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https://hal-supelec.archives-ouvertes.fr/hal-00933983
Contributor : Alexandra Siebert <>
Submitted on : Tuesday, January 21, 2014 - 2:11:41 PM
Last modification on : Thursday, April 25, 2019 - 11:12:22 AM

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  • HAL Id : hal-00933983, version 1

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Hélène Langet, Aymeric Reshef, Cyril Riddell, Yves Trousset, Arthur Tenenhaus, et al.. Nonlinear diffusion constraints for reconstructing subsampled rotational angiography data. Fully3D 2013, Jun 2013, Lake Tahoe, California, United States. pp.38-41. ⟨hal-00933983⟩

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