Sparsity-Promoting Calibration for GRAPPA Accelerated Parallel MRI Reconstruction
Daniel S. Weller,Jonathan R. Polimeni,Leo Grady,Lawrence L. Wald,Elfar Adalsteinsson,Vivek K Goyal +5 more
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TLDR
In this article, a sparsity-promoting regularized calibration method was proposed to find a GRAPPA kernel consistent with the ACS fit equations that yields jointly sparse reconstructed coil channel images.Abstract:
The amount of calibration data needed to produce images of adequate quality can prevent auto-calibrating parallel imaging reconstruction methods like generalized autocalibrating partially parallel acquisitions (GRAPPA) from achieving a high total acceleration factor. To improve the quality of calibration when the number of auto-calibration signal (ACS) lines is restricted, we propose a sparsity-promoting regularized calibration method that finds a GRAPPA kernel consistent with the ACS fit equations that yields jointly sparse reconstructed coil channel images. Several experiments evaluate the performance of the proposed method relative to unregularized and existing regularized calibration methods for both low-quality and underdetermined fits from the ACS lines. These experiments demonstrate that the proposed method, like other regularization methods, is capable of mitigating noise amplification, and in addition, the proposed method is particularly effective at minimizing coherent aliasing artifacts caused by poor kernel calibration in real data. Using the proposed method, we can increase the total achievable acceleration while reducing degradation of the reconstructed image better than existing regularized calibration methods.read more
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Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging.
TL;DR: An improved k‐space reconstruction method using scan‐specific deep learning that is trained on autocalibration signal (ACS) data is developed.
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DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
TL;DR: DeepcomplexMRI as discussed by the authors proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network, which takes advantage of the availability of a large number of existing multichannel groudtruth images and uses them as target data to train the deep residual convolution neural network offline.
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Exponential Wavelet Iterative Shrinkage Thresholding Algorithm for compressed sensing magnetic resonance imaging
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References
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Journal ArticleDOI
Sparse MRI: The application of compressed sensing for rapid MR imaging.
TL;DR: Practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference and demonstrate improved spatial resolution and accelerated acquisition for multislice fast spin‐echo brain imaging and 3D contrast enhanced angiography.
PatentDOI
SENSE: Sensitivity Encoding for fast MRI
TL;DR: The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil configurations and k‐space sampling patterns and special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density.
Journal ArticleDOI
Generalized autocalibrating partially parallel acquisitions (GRAPPA).
Mark A. Griswold,Peter M. Jakob,Robin M. Heidemann,Mathias Nittka,Vladimir Jellus,Jianmin Wang,Berthold Kiefer,Axel Haase +7 more
TL;DR: This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD‐AUTO‐SMASH reconstruction techniques and provides unaliased images from each component coil prior to image combination.
Journal ArticleDOI
The NMR phased array.
TL;DR: Methods for simultaneously acquiring and subsequently combining data from a multitude of closely positioned NMR receiving coils are described, conceptually similar to phased array radar and ultrasound and hence the techniques are called the “NMR phased array.”
Journal ArticleDOI
Constrained restoration and the recovery of discontinuities
Donald Geman,G. Reynolds +1 more
TL;DR: The authors examine prior smoothness constraints of a different form, which permit the recovery of discontinuities without introducing auxiliary variables for marking the location of jumps and suspending the constraints in their vicinity.