Abstract
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.
Highlights
In parallel MRI, the object under study is scanned by multiple receiver coils
(for GRAPPA, SPIRiT and related techniques). All these methods are sensitive to the calibration stage
In recent times there is a concerted effort in developing calibration free reconstruction techniques
Summary
In parallel MRI (pMRI), the object under study is scanned by multiple receiver coils. The calibration in turn depends on the choice of certain parameters, e.g., the window size—size of the central K-space region that has been fully sampled (for all the aforementioned methods) and the kernel size for estimating the interpolation weights (for GRAPPA and SPIRiT). These parameters are manually tuned and the best results are reported. Our reconstruction method is based on the fact that the position of the high valued transform coefficients in the different sensitivity encoded coil images remain the same. Since algorithms for solving such optimization problems do not exist, in this work, we derive fast but simple algorithms to solve the non-convex synthesis and analysis prior problems
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