k-t SPARSE: High frame rate dynamic MRI exploiting spatio-temporal sparsity

By Michael Lustig, Juan M. Santos, David L. Donoho, John M. Pauly. 2006

391 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/031533a2fcb9b7b4e96a97b30f84881b33e9649c

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Citations in the corpus

k‐t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI (2009, methodology): “For example, k-t SPARSE (17) successfully employed the compressed sensing theory for cardiac imaging applications by transforming the time varying image using a wavelet transform along the spatial direction and Fourier transform along the temporal direction.”
Sparse MRI: The application of compressed sensing for rapid MR imaging (2007, result): “Dynamic sparsity is beyond our scope; some preliminary results of dynamic CS imaging are reported in (24,25).”
Highly Undersampled Magnetic Resonance Image Reconstruction via Homotopic $\ell_{0}$ -Minimization (2009, methodology): “In the past several y e rs, the practical performance of CS theory and, specifically, L 1minimization based techniques, has been successfully demo nstrated for a large range of clinical applications includin g nonCartesian and 3D MR angiography (MRA) [11], [12], and time-resolved or k-t SPARSE imagi”
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): “CS methods exploit spatial and temporal correlations by employing irregular undersampling schemes to create incoherent aliasing artifacts and using a non-linear reconstruction to enforce sparsity in a suitable transform domain (17-24).”
Compressed Sensing MRI (2008, background): “CS exhibits both significant resolution improvement over LR at the same scan time, and significant suppression of the aliasing artifacts compared to the linear reconstruction with the same undersampling.”
From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images (2009, methodology): “Figure 5 presents compressed sensing of dynamic MRI—real-time acquisition of heart motion—by Michael Lustig and coworkers at the Stanford MRI lab [112, 111].”
Learning Sparsifying Transforms (2013, methodology): “Therefore, we do not use such a constraint.”