Robust Real-Time-Constrained Estimation of Respiratory Motion for Interventional MRI on Mobile Organs

Abstract : Real time magnetic resonance (MR) imaging is a promising tool for image-guided interventions. For applications such as thermotherapy on moving organs, a fine image-based compensation of motion is required in real time to allow quantitative analysis, retro-control of the interventional device, or determination of the therapy endpoint. Since interventional procedures are usually restricted to a part of the organ/tissue under study, reduced FOV imaging represents a promising way to improve spatial and / or temporal resolution. However, it introduces new challenges for the target motion estimation since structures near the target may appear transiently due to the respiratory motion and the limited spatial coverage. In this paper, a new image based motion estimation method is proposed combining a global motion estimation with a novel optical flow approach extending the initial Horn & Schunck (H&S) method by an additional regularization term. This term integrates the displacement of physiological landmarks, which are obtained in a preparation step by pattern matching into the variational formulation of the optical flow problem. A smooth regulation of the constraint point influences is achieved using a spatial weighting function. The method was compared to the same registration pipeline employing the H&S approach. A first evaluation was performed on synthetic dataset where the accuracy of the motion estimated with the H&S method was improved by a factor of 2 using the proposed approach. An in vivo study was then realized on both the heart and the kidney of twelve volunteers. Compared to the H&S approach, a significant improvement (p<0.05) of the DICE similarity criterion computed between the reference and the registered organ positions was achieved.
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Sébastien Roujol, Jenny Benois-Pineau, Baudouin Denis de Senneville, Mario Ries, Bruno Quesson, et al.. Robust Real-Time-Constrained Estimation of Respiratory Motion for Interventional MRI on Mobile Organs. IEEE Transactions on Information Technology in Biomedicine, Institute of Electrical and Electronics Engineers, 2012, 16 (3), pp.365-374. ⟨10.1109/TITB.2012.2190366⟩. ⟨hal-01578191⟩



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