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Sensitivity encoding for aligned multishot magnetic resonance reconstruction

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)266-280
Number of pages15
JournalIEEE Transactions on Computational Imaging
Issue number3
Early online date20 Apr 2016
Accepted/In press5 Apr 2016
E-pub ahead of print20 Apr 2016
PublishedSep 2016


  • Sensitivity encoding for aligned_CORDERO-GRANDE_Published 2016_GREEN AAM

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    Uploaded date:23 Jun 2016

    Version:Accepted author manuscript

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King's Authors


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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