The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain
Open accessAbstract
Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition, which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.
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Cites
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Singular value decomposition and least squares solutions
Golub, G. H., & Reinsch, C. (1970) · Numerische Mathematik · doi:10.1007/bf02163027 · Cited by 2,929 · cited 1×
“The singular value decomposition (SVD) [2] of a matrix is a useful tool that can provide information about the properties of a matrix and can be applied to a variety of problems, including the solution of linear least squares problems [3] and dimensionality reduction through principal component analysis (PCA) [4], [5].”
BackgroundRank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion
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Singular value decomposition for genome-wide expression data processing and modeling
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Magnetic resonance fingerprinting
Ma, D., Gulani, V., Seiberlich, N., Liu, K., Sunshine, J. L., Duerk, J. L., & Griswold, M. A. (2013) · Nature · doi:10.1038/nature11971 · Cited by 1,577
Cited by
Fast 3D magnetic resonance fingerprinting for a whole‐brain coverage
Ma, D., Jiang, Y., Chen, Y., McGivney, D., Mehta, B., Gulani, V., & Griswold, M. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26886 · Cited by 158
Self-citationRieger, B., Akçakaya, M., Pariente, J. C., Llufriu, S., Martinez-Heras, E., Weingärtner, S., & Schad, L. R. (2018) · Scientific Reports · doi:10.1038/s41598-018-24920-z · Cited by 39
Amortized Neural Networks for Low-Latency Speech Recognition
Macoskey, J., Strimel, G. P., Su, J., & Rastrow, A. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-712 · Cited by 12
Metadata
- Authors
- McGivney, D. F., Pierre, E., Ma, D., Jiang, Y., Saybasili, H., Gulani, V., & Griswold, M. A.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2014-12
- Volume
- 33
- Issue
- 12
- Pages
- 2311-2322
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2061708033
- Cited by
- 314
- References
- 25
- FWCI
- 6.49
- Type
- journal article
- Language
- en
- Open access
- green
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