Adaptive reconstruction of phased array MR imagery

By D. O. Walsh, A. Gmitro, Michael W. Marcellin. Magnetic Resonance in Medicine, 2000

722 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/d4f196cf02cee61314e6f1db3e2689860aa0029f

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In Nuclear and High Energy Physics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Phase‐sensitive inversion recovery for detecting myocardial infarction using gadolinium‐delayed hyperenhancement † (2002, positive): “similar to that used for adaptive phased array combining (24), extended here to the case of phase-sensitive detection with a separate reference image.”
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction (2021, background): “Although there was some consensus on the drawbacks of RSS [46], [47], there was no consensus on a single best alternative.”
Low-rank and Sparse Matrix Decomposition for Accelerated Dynamic MRI with Separation of Background and Dynamic Components (2014, methodology): “Coil sensitivity maps were computed from the temporal average of the accelerated data using the adaptive coil combination technique (29).”
Gadgetron: An open source framework for medical image reconstruction (2013, methodology): “Additionally, the image space unmixing coefﬁcients for individual channels are combined in image space using a B1-weighted coil combination procedure as per Walsh et al. (18).”
Golden-Angle Radial Sparse Parallel MRI: Combination of Compressed Sensing, Parallel Imaging, and Golden-Angle Radial Sampling for Fast and Flexible Dynamic Volumetric MRI (2013, methodology): “Coil sensitivity maps are computed with the adaptive array-combination technique (33,34) using coil-reference data from the temporal average of all acquired spokes, which is usually fully sampled as shown in Figure 2a.”
Model-based analysis of multi-shell diffusion MR data for tractography: How to get over fitting problems (2012, methodology): “In this article, we have used the adaptive-combined reconstruction method (44), as opposed to the more standard sum-of-squares method, and we fitted the data using a Rician noise assumption.”
Generalized autocalibrating partially parallel acquisitions (GRAPPA) (2002, methodology): “(21) could be used to further improve the image quality in GRAPPA reconstructions.”
ESPIRiT — An Eigenvalue Approach to Autocalibrating Parallel MRI: Where SENSE meets GRAPPA (2014, methodology): “This local computation of the sensitivities has some similarity to a previously published method for the estimation of the sensitivities from a local correlation matrix in image space [35, 20] and could also be thought of as a generalization of the subspace-based method presented in [36].”
XD-GRASP: Golden-Angle Radial MRI with Reconstruction of Extra Motion-State Dimensions Using Compressed Sensing (2015, methodology): “Coil sensitivity maps were computed from a fully sampled reference given by NUFFT reconstruction of the whole dataset, using the adaptive array combination method (39).”
Parallel MR imaging (2012, methodology): “This can be performed using a sum-of-squares operation (9), or using other techniques that result in a homogeneous signal after combination (see, eg, (10)).”
SMASH, SENSE, PILS, GRAPPA: How to Choose the Optimal Method (2004, background): “(4) an adaptive sensitivity assessment based on the correlation between the component coil images.(13) In an additional numerical process, these raw-sensitivity maps need to be refined using smoothing (ie, minimizing the propagation of additional noise from the calibration scan into the reconstructe”