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Compressive manifold learning: estimating one-dimensional respiratory motion directly from undersampled k-space data.
Magn Reson Med. 2014 Oct; 72(4):1130-40.MR

Abstract

PURPOSE

To present and validate a manifold learning (ML)-based method that estimates the respiratory signal directly from undersampled k-space data and that can be applied for respiratory self-gated liver MRI.

METHODS

ML methods embed high-dimensional space data in a low-dimensional space while preserving their characteristic properties. These methods have been used to estimate one-dimensional respiratory motion (low-dimensional manifold) from a set of high-dimensional free-breathing abdominal MR images. These approaches require MR images to be reconstructed first from the acquired undersampled data. Recently, the concept of compressive manifold learning (CML) has been introduced that combines compressed sensing with ML by learning low-dimensional manifolds directly from a partial set of compressed measurements, provided that the sampling satisfies the restricted isometry property. We propose to use the CML concept to extract the respiratory signal directly from undersampled k-space data.

RESULTS

Simulation results from free-breathing abdominal MR data show that CML can accurately estimate respiratory motion from highly retrospectively undersampled k-space (up to 25-fold acceleration under ideal assumptions). Prospective free-breathing golden-angle radial two-dimensional (2D) acquisitions further demonstrate the feasibility of the CML method for respiratory self-gating acquisition, estimating the respiratory motion from up to 15-fold accelerated MR data.

CONCLUSION

The proposed method performs accurate respiratory signal estimation from highly undersampled k-space data and can be used for respiratory self-navigated 2D liver MRI.

Authors+Show Affiliations

King's College London, Division of Imaging Sciences and Biomedical Engineering, British Heart Foundation (BHF) Centre of Excellence, Medical Engineering Centre of Research Excellence, London, United Kingdom.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

24357143

Citation

Usman, Muhammad, et al. "Compressive Manifold Learning: Estimating One-dimensional Respiratory Motion Directly From Undersampled K-space Data." Magnetic Resonance in Medicine, vol. 72, no. 4, 2014, pp. 1130-40.
Usman M, Vaillant G, Atkinson D, et al. Compressive manifold learning: estimating one-dimensional respiratory motion directly from undersampled k-space data. Magn Reson Med. 2014;72(4):1130-40.
Usman, M., Vaillant, G., Atkinson, D., Schaeffter, T., & Prieto, C. (2014). Compressive manifold learning: estimating one-dimensional respiratory motion directly from undersampled k-space data. Magnetic Resonance in Medicine, 72(4), 1130-40. https://doi.org/10.1002/mrm.25010
Usman M, et al. Compressive Manifold Learning: Estimating One-dimensional Respiratory Motion Directly From Undersampled K-space Data. Magn Reson Med. 2014;72(4):1130-40. PubMed PMID: 24357143.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Compressive manifold learning: estimating one-dimensional respiratory motion directly from undersampled k-space data. AU - Usman,Muhammad, AU - Vaillant,Ghislain, AU - Atkinson,David, AU - Schaeffter,Tobias, AU - Prieto,Claudia, Y1 - 2013/11/11/ PY - 2013/04/19/received PY - 2013/09/06/revised PY - 2013/10/02/accepted PY - 2013/12/21/entrez PY - 2013/12/21/pubmed PY - 2015/5/23/medline KW - compressed sensing KW - compressive manifold learning KW - manifold learning KW - respiratory gating KW - undersampling SP - 1130 EP - 40 JF - Magnetic resonance in medicine JO - Magn Reson Med VL - 72 IS - 4 N2 - PURPOSE: To present and validate a manifold learning (ML)-based method that estimates the respiratory signal directly from undersampled k-space data and that can be applied for respiratory self-gated liver MRI. METHODS: ML methods embed high-dimensional space data in a low-dimensional space while preserving their characteristic properties. These methods have been used to estimate one-dimensional respiratory motion (low-dimensional manifold) from a set of high-dimensional free-breathing abdominal MR images. These approaches require MR images to be reconstructed first from the acquired undersampled data. Recently, the concept of compressive manifold learning (CML) has been introduced that combines compressed sensing with ML by learning low-dimensional manifolds directly from a partial set of compressed measurements, provided that the sampling satisfies the restricted isometry property. We propose to use the CML concept to extract the respiratory signal directly from undersampled k-space data. RESULTS: Simulation results from free-breathing abdominal MR data show that CML can accurately estimate respiratory motion from highly retrospectively undersampled k-space (up to 25-fold acceleration under ideal assumptions). Prospective free-breathing golden-angle radial two-dimensional (2D) acquisitions further demonstrate the feasibility of the CML method for respiratory self-gating acquisition, estimating the respiratory motion from up to 15-fold accelerated MR data. CONCLUSION: The proposed method performs accurate respiratory signal estimation from highly undersampled k-space data and can be used for respiratory self-navigated 2D liver MRI. SN - 1522-2594 UR - https://www.unboundmedicine.com/medline/citation/24357143/Compressive_manifold_learning:_estimating_one_dimensional_respiratory_motion_directly_from_undersampled_k_space_data_ L2 - https://doi.org/10.1002/mrm.25010 DB - PRIME DP - Unbound Medicine ER -