Calibrationless parallel magnetic resonance imaging: a joint sparsity model

State-of-the-art parallel MRI techniques either explicitly or implicitly require certain parameters to be estimated, e.g., the sensitivity map for SENSE, SMASH and interpolation weights for GRAPPA, SPIRiT. Thus all these techniques are sensitive to the calibration (parameter estimation) stage. In th...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2013-12, Vol.13 (12), p.16714-16735
Main Authors: Majumdar, Angshul, Chaudhury, Kunal Narayan, Ward, Rabab
Format: Article
Language:English
Subjects:
Algorithms
Brain - physiology
Calibration
compressed sensing
Computational mathematics
Convex analysis
Image Processing, Computer-Assisted - methods
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Methods
Models, Theoretical
Optimization
Parameter estimation
Sensors
Sparsity
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Summary:State-of-the-art parallel MRI techniques either explicitly or implicitly require certain parameters to be estimated, e.g., the sensitivity map for SENSE, SMASH and interpolation weights for GRAPPA, SPIRiT. Thus all these techniques are sensitive to the calibration (parameter estimation) stage. In this work, we have proposed a parallel MRI technique that does not require any calibration but yields reconstruction results that are at par with (or even better than) state-of-the-art methods in parallel MRI. Our proposed method required solving non-convex analysis and synthesis prior joint-sparsity problems. This work also derives the algorithms for solving them. Experimental validation was carried out on two datasets-eight channel brain and eight channel Shepp-Logan phantom. Two sampling methods were used-Variable Density Random sampling and non-Cartesian Radial sampling. For the brain data, acceleration factor of 4 was used and for the other an acceleration factor of 6 was used. The reconstruction results were quantitatively evaluated based on the Normalised Mean Squared Error between the reconstructed image and the originals. The qualitative evaluation was based on the actual reconstructed images. We compared our work with four state-of-the-art parallel imaging techniques; two calibrated methods-CS SENSE and l1SPIRiT and two calibration free techniques-Distributed CS and SAKE. Our method yields better reconstruction results than all of them.
ISSN:1424-8220
1424-8220
DOI:10.3390/s131216714