Low rank approximation methods for MR fingerprinting with large scale dictionaries.
Magn Reson Med
; 79(4): 2392-2400, 2018 04.
Article
en En
| MEDLINE
| ID: mdl-28804918
ABSTRACT
PURPOSE:
This work proposes new low rank approximation approaches with significant memory savings for large scale MR fingerprinting (MRF) problems. THEORY ANDMETHODS:
We introduce a compressed MRF with randomized singular value decomposition method to significantly reduce the memory requirement for calculating a low rank approximation of large sized MRF dictionaries. We further relax this requirement by exploiting the structures of MRF dictionaries in the randomized singular value decomposition space and fitting them to low-degree polynomials to generate high resolution MRF parameter maps. In vivo 1.5T and 3T brain scan data are used to validate the approaches.RESULTS:
T1 , T2 , and off-resonance maps are in good agreement with that of the standard MRF approach. Moreover, the memory savings is up to 1000 times for the MRF-fast imaging with steady-state precession sequence and more than 15 times for the MRF-balanced, steady-state free precession sequence.CONCLUSION:
The proposed compressed MRF with randomized singular value decomposition and dictionary fitting methods are memory efficient low rank approximation methods, which can benefit the usage of MRF in clinical settings. They also have great potentials in large scale MRF problems, such as problems considering multi-component MRF parameters or high resolution in the parameter space. Magn Reson Med 792392-2400, 2018. © 2017 International Society for Magnetic Resonance in Medicine.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Encéfalo
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Imagen por Resonancia Magnética
Tipo de estudio:
Clinical_trials
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Prognostic_studies
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Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Magn Reson Med
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2018
Tipo del documento:
Article
País de afiliación:
Estados Unidos