Gaussian signal relaxation around spin echoes

• Verfasst von: Zapp, Jascha [VerfasserIn]
• Verfasst von: Domsch, Sebastian [VerfasserIn]
• Verfasst von: Weingärtner, Sebastian [VerfasserIn]
• Verfasst von: Schad, Lothar R. [VerfasserIn]
• Titel: Gaussian signal relaxation around spin echoes
• Titelzusatz: implications for precise reversible transverse relaxation quantification of pulmonary tissue at 1.5 and 3 Tesla
• Verf.angabe: Jascha Zapp, Sebastian Domsch, Sebastian Weingärtner, and Lothar R. Schad
• Jahr des Originals: 2016
• Umfang: 8 S.
• Fussnoten: Published online 25 June 2016 ; Gesehen am 22.08.2018
• Titel Quelle: Enthalten in: Magnetic resonance in medicine
• Jahr Quelle: 2017
• Band/Heft Quelle: 77(2017), 5, S. 1938-1945
• ISSN Quelle: 1522-2594
• DOI: doi:10.1002/mrm.26280
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1580298532

Abstract

Purpose To characterize the reversible transverse relaxation in pulmonary tissue and to study the benefit of a quadratic exponential (Gaussian) model over the commonly used linear exponential model for increased quantification precision. Methods A point-resolved spectroscopy sequence was used for comprehensive sampling of the relaxation around spin echoes. Measurements were performed in an ex vivo tissue sample and in healthy volunteers at 1.5 Tesla (T) and 3 T. The goodness of fit using and the precision of the fitted relaxation time by means of its confidence interval were compared between the two relaxation models. Results The Gaussian model provides enhanced descriptions of pulmonary relaxation with lower by average factors of 4 ex vivo and 3 in volunteers. The Gaussian model indicates higher sensitivity to tissue structure alteration with increased precision of reversible transverse relaxation time measurements also by average factors of 4 ex vivo and 3 in volunteers. The mean relaxation times of the Gaussian model in volunteers are = (1.97 ± 0.27) msec at 1.5 T and = (0.83 ± 0.21) msec at 3 T. Conclusion Pulmonary signal relaxation was found to be accurately modeled as Gaussian, providing a potential biomarker with high sensitivity. Magn Reson Med 77:1938-1945, 2017. © 2016 International Society for Magnetic Resonance in Medicine