Advances in sensitivity encoding with arbitrary k‐space trajectories

By K. Pruessmann, M. Weiger, P. Börnert, Peter Boesiger. Magnetic Resonance in Medicine, 2001

1,334 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/06f8d11724ef3963d18453f70d5cac0ec239170b

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In Atomic and Molecular Physics, and Optics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

MODEL-BASED IMAGE RECONSTRUCTION FOR MRI (2010, background): “For differentiable regularizers such as (25), the conjugate gradient algorithm is a natural choice [26] [30].”
Complex diffusion-weighted image estimation via matrix recovery under general noise models (2018, positive): “This generates an equivalent inverse problem where the channel noise is decorrelated and standardized (Pruessmann et al., 2001), so we can write Λz = CN (0KLN , IKLN ), with 0KLN and IKLN denoting respectively the null vector and identity matrix of size KLN , where K is the number of spectral sample”
Parallel MR imaging (2012, methodology): “Luckily, many methods have been proposed to reconstruct images from undersampled non-Cartesian data, including CG SENSE (85), radial GRAPPA (86,87), spiral GRAPPA (88,89), zig-zag GRAPPA (90), 1D non-Cartesian GRAPPA (91), pseudo-Cartesian GRAPPA (92), through-time non-Cartesian GRAPPA (93,94), PARS”
Second order total generalized variation (TGV) for MRI (2011, neutral): “All results were also compared to an iterative parallel imaging method of CG-SENSE type (18), using the same coil sensitivities as in the TV experiments.”
A tour of accelerated parallel MR imaging from a linear systems perspective (2005, background): “[4], E(n, ky) = e−jkyn, where each row of E corresponds to a particular phase encode ky.”
Image reconstruction by domain-transform manifold learning (2017, methodology): “…Technique 15 ; 2) Spiral-trajectory k -space (rapid acquisition with non-Cartesian sampling) and Conjugate-Gradient SENSE reconstruction employing NUFFT regridding 13 ; 3) Poisson-disc undersampled k -space (incoherent sparse acquisition) and compressed sensing reconstruction with…”
Assessment of the generalization of learned image reconstruction and the potential for transfer learning (2019, background): “This can be related to the influence of 2 design parameters in image reconstruction, which are usually tuned by hand in a conventional image reconstruction approach: the number of iterations in an iterative reconstruction 26 and the regularization parameter in compressed sensing.”
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2019, background): “as a follow up of the original SENSE paper [6].”
Accelerating SENSE using compressed sensing (2009, methodology): “For arbitrary trajectories, the general SENSE equation is (18)”
Nonuniform fast Fourier transforms using min-max interpolation (2003, background): “These iterative algorithms require repeated calculation of the “forward problem” (from object space to frequency space and the adjoint thereof) [18]–[20], [28], [56].”