k‐t BLAST and k‐t SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations

By J. Tsao, Peter Boesiger, K. Pruessmann. Magnetic Resonance in Medicine, 2003

896 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/d280712094138771be3aaf30f46a09b21af0a1e8

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In Atomic and Molecular Physics, and Optics

In Nuclear and High Energy Physics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Fast 3D Magnetic Resonance Fingerprinting (MRF) For a Whole Brain Coverage (2017, positive): “Specifically, this study applied a simple accelerated acquisition scheme similar to the k-t broad-use linear acquisition speed-up technique (21) for the 3D MRF scan, to allow a total factor of 144 acceleration as compared with the Nyquist rate, so that 3D T1, T2, and proton density (M0) maps can be ”
k‐t PCA: Temporally constrained k‐t BLAST reconstruction using principal component analysis (2009, methodology): “The signal covariance was calculated from the training data: Mx 2 diag wtrain,x 2 [10]”
k‐t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI (2009, methodology): “However, k-t BLAST/SENSE has a signal nulling problem at temporal DC frequency since the temporal average image is ﬁrst subtracted to avoid energy concentration on temporal DC frequency in y-f space (9,13).”
Highly constrained backprojection for time‐resolved MRI (2006, methodology): “Recently, the k-t BLAST technique has been proposed (6,7).”
Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction (2017, positive): “The raw multi-coil data was reconstructed using SENSE [29] with no undersampling and retrospective gating.”
4D flow MRI (2012, background): “New development based on multidimensional parallel imaging (such as k-t parallel imaging) or sparse sampling techniques (radial acquisitions, compressed sensing, and others) are particularly promising for 4D flow MRI (42,43,48,170,171).”
Compressed sensing in dynamic MRI (2008, positive): “In k-t broad-use linear acquisition speed-up technique (k-t BLAST) (13), a low resolution scan acquired at the full temporal bandwidth is performed either before (in the case of periodic motion) or interleaved with the actual data acquisition.”
Deep learning in bioinformatics (2016, background): “For example, Stollenga et al. [141] applied MD-RNNs to three-dimensional electron microscopy images and MRIs to segment neuronal structures.”
Parallel MR imaging (2012, background): “Techniques that exploit temporal information as well as coil sensitivity information, including k-t SENSE (82) and k-t GRAPPA (83), have moved into the clinic on some platforms.”
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): “The k-t acceleration methods, such as k-t BLAST/k-t SENSE (9), k-t GRAPPA (10), and SPEAR (11), are based on the fact that the representation of dynamic images in the combined spatial (x) and temporal Fourier (f) domain is typically sparse, which reduces the signal aliasing in x-f space due to regul”
Accelerating SENSE using compressed sensing (2009, neutral): “[6] is better regarded as regularization with a Gibbs prior, and the”
4D flow cardiovascular magnetic resonance consensus statement (2015, methodology): “Moreover, in addition to standard parallel imaging, more advanced acceleration techniques have shown promising results and there exists many options for reduction of 4D Flow CMR scan times [39, 49, 187–192].”