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A robust adaptive sampling method for faster acquisition of MR images
Published in Elsevier Inc.
2015
PMID: 25602686
Volume: 33
   
Issue: 5
Pages: 635 - 643
Abstract
A robust adaptive k-space sampling method is proposed for faster acquisition and reconstruction of MR images. In this method, undersampling patterns are generated based on magnitude profile of a fully acquired 2-D k-space data. Images are reconstructed using compressive sampling reconstruction algorithm. Simulation experiments are done to assess the performance of the proposed method under various signal-to-noise ratio (SNR) levels. The performance of the method is better than non-adaptive variable density sampling method when k-space SNR is greater than 10 dB. The method is implemented on a fully acquired multi-slice raw k-space data and a quality assurance phantom data. Data reduction of up to 60% is achieved in the multi-slice imaging data and 75% is achieved in the phantom imaging data. The results show that reconstruction accuracy is improved over non-adaptive or conventional variable density sampling method. The proposed sampling method is signal dependent and the estimation of sampling locations is robust to noise. As a result, it eliminates the necessity of mathematical model and parameter tuning to compute k-space sampling patterns as required in non-adaptive sampling methods. © 2015 Elsevier Inc..
About the journal
JournalData powered by TypesetMagnetic Resonance Imaging
PublisherData powered by TypesetElsevier Inc.
ISSN0730725X
Open AccessNo
Concepts (31)
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    Accuracy
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    ADAPTIVE K-SPACE SAMPLING METHOD
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    Algorithm
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    Article
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    Image processing
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    Image reconstruction
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    IMAGING PHANTOM
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    Infrared spectroscopy
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    Intermethod comparison
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    KERNEL METHOD
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    Mathematical model
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    Neuroimaging
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    Noise reduction
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    Nuclear magnetic resonance imaging
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    Priority journal
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    SIGNAL NOISE RATIO
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    Validation process
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    Anatomy and histology
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    Artifact
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    Brain
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    Computer simulation
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    Human
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    Procedures
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    Theoretical model
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    Algorithms
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    Artifacts
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    Humans
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    Image processing, computer-assisted
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    Magnetic resonance imaging
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    Models, theoretical
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    Signal-to-noise ratio