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Registration of free-breathing abdominal 3D contrast-enhanced CT

Blandine Romain 1, 2, 3, * Veronique Letort 2 Olivier Lucidarme 4 Florence d'Alché-Buc 3, 5 Laurence Rouet 1 
* Corresponding author
5 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : CT perfusion imaging is used for the follow-up of abdominal tumors. A speci city of our work is that patients are breathing freely during image acquisition (5 minutes). We propose an automatic 3D image registration to compensate respiratory motion. The registration is computed in two main steps: global translation in the z-direction and 3D multiresolution blockmatching. Within this algorithm, the choice of similarity measure largely determines the algorithm robustness in presence of intensity shifts due to contrast di usion. We exploit a modi ed entropy-based similarity measure to improve the quality of registration. We also propose two relevant criteria allowing to quantify the registration quality: one based on the gradients of image volumes of di erences and one based on the smoothness of enhanced-intensity curves. Copyright Springer-Verlag Berlin Heidelberg. The original publication can be found at
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Blandine Romain, Veronique Letort, Olivier Lucidarme, Florence d'Alché-Buc, Laurence Rouet. Registration of free-breathing abdominal 3D contrast-enhanced CT. 4th International Workshop on Abdominal Imaging. Computational and Clinical Applications, Held in Conjunction with MICCAI 2012, Oct 2012, Nice, France. pp.274-282, ⟨10.1007/978-3-642-33612-6_29⟩. ⟨hal-00780707⟩



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