Low-rank approximations for dynamic imaging
2011 IEEE international symposium on biomedical imaging: From Nano …, 2011•ieeexplore.ieee.org
This paper describes a framework for dynamic imaging based on the representation of a
spatiotemporal image as a low-rank matrix. This kind of image modeling is flexible enough
to accurately and parsimoniously represent a wide range of dynamic imaging data.
Representation using a low-rank model leads to new schemes for data acquisition and
image reconstruction, enabling reconstruction from highly-undersampled datasets.
Theoretical considerations and algorithms are discussed, and empirical results are provided …
spatiotemporal image as a low-rank matrix. This kind of image modeling is flexible enough
to accurately and parsimoniously represent a wide range of dynamic imaging data.
Representation using a low-rank model leads to new schemes for data acquisition and
image reconstruction, enabling reconstruction from highly-undersampled datasets.
Theoretical considerations and algorithms are discussed, and empirical results are provided …
This paper describes a framework for dynamic imaging based on the representation of a spatiotemporal image as a low-rank matrix. This kind of image modeling is flexible enough to accurately and parsimoniously represent a wide range of dynamic imaging data. Representation using a low-rank model leads to new schemes for data acquisition and image reconstruction, enabling reconstruction from highly-undersampled datasets. Theoretical considerations and algorithms are discussed, and empirical results are provided to illustrate the performance of the approach.
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