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 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
IEEE
01.12.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 0278-0062, 1558-254X, 1558-254X |
| On-line přístup: | Získat plný text |
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| Abstract | 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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| AbstractList | 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. 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.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. Magnetic resonance 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 (SVD), 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. |
| Author | Saybasili, Haris Dan Ma Griswold, Mark A. McGivney, Debra F. Pierre, Eric Gulani, Vikas Yun Jiang |
| Author_xml | – sequence: 1 givenname: Debra F. surname: McGivney fullname: McGivney, Debra F. email: debra.mcgivney@case.edu organization: Dept. of Radiol., Case Western Reserve Univ., Cleveland, OH, USA – sequence: 2 givenname: Eric surname: Pierre fullname: Pierre, Eric organization: Dept. of Biomed. Eng., Case Western Reserve Univ., Cleveland, OH, USA – sequence: 3 surname: Dan Ma fullname: Dan Ma organization: Dept. of Biomed. Eng., Case Western Reserve Univ., Cleveland, OH, USA – sequence: 4 surname: Yun Jiang fullname: Yun Jiang organization: Dept. of Biomed. Eng., Case Western Reserve Univ., Cleveland, OH, USA – sequence: 5 givenname: Haris surname: Saybasili fullname: Saybasili, Haris organization: Siemens Healthcare USA, Inc., Chicago, IL, USA – sequence: 6 givenname: Vikas surname: Gulani fullname: Gulani, Vikas organization: Dept. of Radiol., Case Western Reserve Univ., Cleveland, OH, USA – sequence: 7 givenname: Mark A. surname: Griswold fullname: Griswold, Mark A. organization: Dept. of Radiol., Case Western Reserve Univ., Cleveland, OH, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25029380$$D View this record in MEDLINE/PubMed |
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| Snippet | Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue... Magnetic resonance fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue... |
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| SubjectTerms | 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 |
| Title | SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain |
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