Reduction of vibration-induced signal loss by matching mechanical vibrational states: Application in high b-value diffusion-weighted MRS.
Magn Reson Med
; 84(1): 39-51, 2020 07.
Article
em En
| MEDLINE
| ID: mdl-31872934
PURPOSE: Diffusion encoding gradients are known to yield vibrations of the typical clinical MR scanner hardware with a frequency of 20 to 30 Hz, which may lead to signal loss in diffusion-weighted MR measurements. This work proposes to mitigate vibration-induced signal loss by introducing a vibration-matching gradient (VMG) to match vibrational states during the 2 diffusion gradient pulses. THEORY AND METHODS: A theoretical description of displacements induced by gradient switching was introduced and modeled by a 2-mass-spring-damper system. An additional preceding VMG mimicking timing and properties of the diffusion encoding gradients was added to a high b-value diffusion-weighted MR spectroscopy sequence. Laser interferometry was employed to measure 3D displacements of a phantom surface. Lipid ADC was assessed in water-fat phantoms and in vivo in the tibial bone marrow of 3 volunteers. RESULTS: The modeling and the laser interferometer measurements revealed that the displacement curves are more similar during the 2 diffusion gradients with the VMG compared to the standard sequence, resulting in less signal loss of the diffusion-weighted signal. Phantom results showed lipid ADC overestimation up to 119% with the standard sequence and an error of 5.5% with the VMG. An 18% to 35% lower coefficient of variation was obtained for in vivo lipid ADC measurement when employing the VMG. CONCLUSION: The application of the VMG reduces the signal loss introduced by hardware vibrations in a high b-value diffusion-weighted MRS sequence in phantoms and in vivo. Reference measurements based on laser interferometry and mechanical modelling confirmed the findings.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Vibração
/
Imagem de Difusão por Ressonância Magnética
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article