SPIRiT: Iterative Self-consistent Parallel Imaging Reconstruction from Arbitrary k-Space

By Michael Lustig, John M. Pauly. Magnetic Resonance in Medicine, 2010

926 citations recorded by the source corpus.

A new approach to autocalibrating, coil‐by‐coil parallel imaging reconstruction, is presented. It is a generalized reconstruction framework based on self‐consistency. The reconstruction problem is formulated as an optimization that yields the most consistent solution with the calibration and acquisition data. The approach is general and can accurately reconstruct images from arbitrary k‐space sampling patterns. The formulation can flexibly incorporate additional image priors such as off‐resonance correction and regularization terms that appear in compressed sensing. Several iterative strategies to solve the posed reconstruction problem in both image and k‐space domain are presented. These are based on a projection over convex sets and conjugate gradient algorithms. Phantom and in vivo studies demonstrate efficient reconstructions from undersampled Cartesian and spiral trajectories. Reconstructions that include off‐resonance correction and nonlinear ℓ1‐wavelet regularization are also demonstrated. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.

Subfields: Atomic and Molecular Physics, and Optics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3446e98505da7fc1390e87c9a6500481b55e6c5c

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In Atomic and Molecular Physics, and Optics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Accelerating magnetic resonance imaging via deep learning (2016, background): “Magnetic resonance imaging (MRI) is an indispensable tool for medical diagnosis, disease staging and clinical research due to its strong capability in providing rich anatomical and functional information and its non-radiation and non-ionizing nature.”
Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks (2018, methodology): “For example, l1- iterative self-consistent parallel imaging reconstruction (l1-SPIRiT) [30] utilizes additional GRAPPA type constraints for the CS problem”
Coil Compression for Accelerated Imaging with Cartesian Sampling (2012, methodology): “The second dataset with random undersampling was reconstructed by 1-SPIRiT both with and without GCC.”
Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction (2013, neutral): “The optimization is typically constrained by a penalty term expressed as `0 or `1 norms, which are exploited to enable the sparsity of the assumed image priors [21], [22], [23], [20].”
Low-Rank Modeling of Local k-Space Neighborhoods (LORAKS) for Constrained MRI (2014, methodology): “To the best of our knowledge, no previous phase-constrained MRI reconstruction methods have used relationships of this form.”
Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator (2014, methodology): “PPI has been widely adopted clinically and Poisson-disk acquisition can dramatically reduce the g-factor in 3D PPI (Lustig and Pauly, 2010; Vasanawala et al., 2010).”
ESPIRiT — An Eigenvalue Approach to Autocalibrating Parallel MRI: Where SENSE meets GRAPPA (2014, neutral): “© 2013 Wiley Periodicals, Inc. Key words: autocalibration; parallel imaging; GRAPPA; SENSE; compressed sensing”
Fast ℓ1-SPIRiT Compressed Sensing Parallel Imaging MRI: Scalable Parallel Implementation and Clinically Feasible Runtime (2012, methodology): “As discussed in [17], the operator can be implemented as a convolution in -space or as multiplication in image space.”
Low-rank and Sparse Matrix Decomposition for Accelerated Dynamic MRI with Separation of Background and Dynamic Components (2014, background): “[ ] 2 d S L E E S L g −       =             , (8)”