Efficient deformable motion correction for 3-D abdominal MRI using manifold regression

Chen, Xin and Balfour, Daniel R. and Marsden, Paul K. and Reader, Andrew J. and Prieto, Claudia and King, Andrew P. (2017) Efficient deformable motion correction for 3-D abdominal MRI using manifold regression. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2017), 10-14 Sept 2017, Quebec City, Quebec, Canada.

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Abstract

We present a novel framework for efficient retrospective respiratory motion correction of 3-D abdominal MRI using manifold regression. K-space data are continuously acquired under free breathing using the stack-of-stars radial gold-en-angle trajectory. The stack-of-profiles (SoP) from all temporal positions are embedded into a common manifold, in which SoPs that were acquired at similar respiratory states are close together. Next, the SoPs in the manifold are clustered into groups using the k-means algorithm. One 3-D volume is reconstructed at the central SoP position of each cluster (a.k.a. key-volumes). Motion fields are estimated using deformable image registration between each of these key-volumes and a reference end-exhale volume. Subsequently, the motion field at any other SoP position in the manifold is derived using manifold regression. The regressed motion fields for each of the SoPs are used to deter-mine a final motion-corrected MRI volume. The method was evaluated on realistic synthetic datasets which were generated from real MRI data and also tested on an in vivo dataset. The framework enables more accurate motion correction compared to the conventional binning-based approach, with high computational efficiency.

Item Type: Conference or Workshop Item (Paper)
Additional Information: The final publication, Chen X., Balfour D.R., Marsden P.K., Reader A.J., Prieto C., King A.P. (2017) Efficient Deformable Motion Correction for 3-D Abdominal MRI Using Manifold Regression. In: Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S. (eds) Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science, vol 10434. Springer, Cham is available at Springer via https://link.springer.com/chapter/10.1007/978-3-319-66185-8_31
Keywords: 3D abdominal MRI, Manifold learning, Manifold regression, Motion correction
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Related URLs:
Depositing User: Chen, Xin
Date Deposited: 05 Oct 2017 10:07
Last Modified: 14 Oct 2017 08:41
URI: http://eprints.nottingham.ac.uk/id/eprint/46866

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