Spatiotemporal Imaging with Partially Separable Functions

By Zhi-Pei Liang. Joint Meeting of International Symposium on Noninvasive Functional Source Imaging of Brain and Heart and International Conference on Functional Biomedical Imaging, 2007

430 citations recorded by the source corpus.

Subfields: Computational Mechanics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/cad045d2b484c2ed1b308dd1da65757a6c38a0e4

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In Computational Mechanics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Magnetic resonance multitasking for motion-resolved quantitative cardiovascular imaging (2018, positive): “However, scan time can be heavily reduced by exploiting spatiotemporal correlation 10 , specifically by modelling the tensor as low-rank, such that the degrees of freedom in increase linearly rather than exponentially 7 .”
Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR (2011, neutral): “This decomposition implies that the spatio-temporal signal can be expressed as a weighted linear combination of temporal basis functions [8], [9]”
Low-Rank Modeling of Local k-Space Neighborhoods (LORAKS) for Constrained MRI (2014, methodology): “An incomplete list of constrained MR imaging methods that exploit linear dependence relationships includes: parallel imaging methods like GRAPPA [3], SPIRiT [4], and PRUNO [5] that exploit linear dependencies between -space samples from different receiver coils within local -space neighborhoods; aut”
Accelerating magnetic resonance imaging via deep learning (2016, background): “Sparsity is one commonly used prior information due to the emergence of Compressed sensing (CS) and there are also other priors being considered, such as low-rank [3, 4], generalized series (GS) model [5] and so on.”
ADMiRA: Atomic Decomposition for Minimum Rank Approximation (2009, background): “Continuing the analogy with the vector case, the remarkable fact is that the number of measurementssufficient for unique and stable recovery is roughly on the same order as the number of degrees of freedom in the unknown low rank matrix.”
Sparse Bayesian Methods for Low-Rank Matrix Estimation (2011, background): “Matrix completion fin ds application in many areas of engineering, including system identification [6], sensor networks [7], machine lea rning [8], computer vision [9], [10], and medical imaging [11].”