These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Trimmed autocalibrating k-space estimation based on structured matrix completion.
    Author: Bydder M, Rapacchi S, Girard O, Guye M, Ranjeva JP.
    Journal: Magn Reson Imaging; 2017 Nov; 43():88-94. PubMed ID: 28716683.
    Abstract:
    PURPOSE: Parallel imaging allows the reconstruction of undersampled data from multiple coils. This provides a means to reject and regenerate corrupt data (e.g. from motion artefact). The purpose of this work is to approach this problem using the SAKE parallel imaging method. THEORY AND METHODS: Parallel imaging methods typically require calibration by fully sampling the center of k-space. This is a challenge in the presence of corrupted data, since the calibration data may be corrupted which leads to an errors-in-variables problem that cannot be solved by least squares or even iteratively reweighted least squares. The SAKE method, based on matrix completion and structured low rank approximation, was modified to detect and trim these errors from the data. RESULTS: Simulated and actual corrupted datasets were reconstructed with SAKE, the proposed approach and a more standard reconstruction method (based on solving a linear equation) with a data rejection criterion. The proposed approach was found to reduce artefacts considerably in comparison to the other two methods. CONCLUSION: SAKE with data trimming improves on previous methods for reconstructing images from grossly corrupted data.
    [Abstract] [Full Text] [Related] [New Search]