Joint reconstruction of image and motion in MRI: implicit regularization using an adaptive 3D mesh.
Fiche publication
Date publication
janvier 2012
Journal
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Auteurs
Membres identifiés du Cancéropôle Est :
Pr FELBLINGER Jacques, Dr VUISSOZ Pierre-André
Tous les auteurs :
Menini A, Vuissoz PA, Felblinger J, Odille F
Lien Pubmed
Résumé
Magnetic resonance images are affected by motion artefacts due to breathing and cardiac beating that occur during the acquisition. Methods for joint reconstruction of image and motion have been proposed recently. Such optimization problems are ill-conditioned, therefore regularization methods are required such as motion smoothness constraints using the Tikhonov method. However with Tikhonov methods the solution often relies on a good choice of the regularization parameter micron, especially in large parameter search spaces (e.g., in 3D reconstructions). In this paper, we propose an adaptive, implicit regularization method which results in subject-specific, spatially varying smoothness constraints on the motion model. It is based on the idea of solving for motion only in certain key points that form a mesh. A practical algorithm is proposed for generating this mesh automatically. The proposed method is shown to have a better convergence rate than the Tikhonov method, both in silico and in vivo. The accuracy of the reconstructed image and motion is also improved.
Mots clés
Algorithms, Computer Simulation, Humans, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, methods, Joints, pathology, Liver, pathology, Magnetic Resonance Imaging, methods, Models, Statistical, Motion, Reproducibility of Results, Respiration
Référence
Med Image Comput Comput Assist Interv. 2012 ;15(Pt 1):264-71