Sensitivity Encoding for Aligned Multishot Magnetic Resonance Reconstruction
Cordero-Grande L, Teixeira RPAG, Hughes EJ, Hutter J, Price AN, Hajnal JV (2016)
Publication Type: Journal article
Publication year: 2016
Journal
Book Volume: 2
Pages Range: 266-280
Article Number: 7457367
Journal Issue: 3
Abstract
This paper introduces a framework for the reconstruction of magnetic resonance images in the presence of rigid motion. The rationale behind our proposal is to make use of the partial k -space information provided by multiple receiver coils in order to estimate the position of the imaged object throughout the shots that contribute to the image. The estimated motion is incorporated into the reconstruction model in an iterative manner to obtain a motion-free image. The method is parameter-free, does not assume any prior model for the image to be reconstructed, avoids blurred images due to resampling, does not make use of external sensors, and does not require modifications in the acquisition sequence. Validation is performed using synthetically corrupted data to study the limits for full motion-recovered reconstruction in terms of the amount of motion, encoding trajectories, number of shots and availability of prior information, and to compare with the state of the art. Quantitative and visual results of its application to a highly challenging volumetric brain imaging cohort of 207 neonates are also presented, showing the ability of the proposed reconstruction to generally improve the quality of reconstructed images, as evaluated by both sparsity and gradient entropy based metrics.
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United Kingdom (GB)How to cite
APA:
Cordero-Grande, L., Teixeira, R.P.A.G., Hughes, E.J., Hutter, J., Price, A.N., & Hajnal, J.V. (2016). Sensitivity Encoding for Aligned Multishot Magnetic Resonance Reconstruction. IEEE Transactions on Computational Imaging, 2(3), 266-280. https://doi.org/10.1109/TCI.2016.2557069
MLA:
Cordero-Grande, Lucilio, et al. "Sensitivity Encoding for Aligned Multishot Magnetic Resonance Reconstruction." IEEE Transactions on Computational Imaging 2.3 (2016): 266-280.
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