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Novel structured low-rank algorithm to recover spatially smooth exponential image time series
Conference proceeding

Novel structured low-rank algorithm to recover spatially smooth exponential image time series

Arvind Balachandrasekaran and Mathews Jacob
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Vol.2017, pp.1-4
04/2017
DOI: 10.1109/ISBI.2017.7950454
PMID: 33763179
url
https://www.ncbi.nlm.nih.gov/pmc/articles/7985823View
Open Access

Abstract

We propose a structured low rank matrix completion algorithm to recover a time series of images consisting of linear combination of exponential parameters at every pixel, from undersampled Fourier measurements. The spatial smoothness of these parameters is exploited along with the exponential structure of the time series at every pixel, to derive an annihilation relation in the k - t domain. This annihilation relation translates into a structured low rank matrix formed from the k - t samples. We demonstrate the algorithm in the parameter mapping setting and show significant improvement over state of the art methods.
Three-dimensional displays Convolution Finite impulse response filters Time series analysis Toeplitz Estimation smoothness penalty structured low rank Indexes Acceleration parameter mapping

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