ESPIRiT — An Eigenvalue Approach to Autocalibrating Parallel MRI: Where SENSE meets GRAPPA

By Martin Uecker, P. Lai, Mark Murphy, Patrick Virtue, Michael Elad, John M. Pauly, Shreyas S. Vasanawala, Michael Lustig. Magnetic Resonance in Medicine, 2014

1,557 citations recorded by the source corpus.

Parallel imaging allows the reconstruction of images from undersampled multicoil data. The two main approaches are: SENSE, which explicitly uses coil sensitivities, and GRAPPA, which makes use of learned correlations in k‐space. The purpose of this work is to clarify their relationship and to develop and evaluate an improved algorithm.

Subfields: Nuclear and High Energy Physics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f00db8376c229ddecd8b6d05a86f7dddf03c3391

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In Nuclear and High Energy Physics

Daniel K. Sodickson

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction (2021, methodology): “The network was initialized with a GRAPPA estimate [28] (pre-processed into reconstruction + residual), and coil sensitivities were estimated using ESPIRiT [40].”
Learning a Variational Network for Reconstruction of Accelerated MRI Data (2017, methodology): “36 × 3mm 3 Coil sensitivity maps were precomputed from a data block of size 24 × 24 at the center of k-space using ESPIRiT (33).”
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction (2017, background): “This is the fundamental assumption underlying parallel imaging [7].”
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction (2017, background): “This is the fundamental assumption underlying parallel imaging [24].”
MoDL: Model-Based Deep Learning Architecture for Inverse Problems (2017, methodology): “The coil sensitivity maps were estimated from the central k-space regions of each slice using ESPIRiT [42] and were assumed to be known during experiments.”
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI (2018, methodology): “One frequently-used method is the ESPIRiT approach [45], which harmonizes parallel imaging and compressed sensing in a uniﬁed framework.”
Assessment of the generalization of learned image reconstruction and the potential for transfer learning (2019, methodology): “Twenty-four reference lines at the center of k-space were used for the estimation of coil sensitivity maps in both cases, using ESPIRiT.”
Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks. (2018, methodology): “org n Radiology: Volume 289: Number 2—November 2018 (18) by using the Berkeley Advanced Reconstruction Toolbox (19).”
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2019, methodology): “Furthermore, SPIRiT has facilitated the connection between coil sensitivities used in image-domain parallel imaging methods and the convolutional kernels used in k-space methods via a subspace analysis in a method called ESPIRiT [5].”