SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain

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 eq...

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Vydáno v:IEEE transactions on medical imaging Ročník 33; číslo 12; s. 2311 - 2322
Hlavní autoři: McGivney, Debra F., Pierre, Eric, Dan Ma, Yun Jiang, Saybasili, Haris, Gulani, Vikas, Griswold, Mark A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.12.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Approximation methods
Brain - anatomy & histology
Compressing
Data Compression - methods
Dictionaries
Dimensionality reduction
Fingerprinting
Humans
Image Processing, Computer-Assisted - methods
Image reconstruction
Magnetic resonance
magnetic resonance imaging
Magnetic Resonance Imaging - methods
Mathematical analysis
Mathematical model
Mathematical models
Pattern recognition
pattern recognition and classification
Phantoms, Imaging
Signal to noise ratio
singular value decomposition
Vectors
ISSN:0278-0062, 1558-254X, 1558-254X
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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2014.2337321