Quantification of pulmonary microcirculation by dynamic contrast-enhanced magnetic resonance imaging

• Verfasst von: Salehi Ravesh, Mona [VerfasserIn]
• Verfasst von: Brix, G. [VerfasserIn]
• Verfasst von: Laun, Frederik B. [VerfasserIn]
• Verfasst von: Kuder, Tristan Anselm [VerfasserIn]
• Verfasst von: Puderbach, Michael [VerfasserIn]
• Verfasst von: Ley-Zaporozhan, Julia [VerfasserIn]
• Verfasst von: Ley, S. [VerfasserIn]
• Verfasst von: Fieselmann, A. [VerfasserIn]
• Verfasst von: Herrmann, M. F. [VerfasserIn]
• Verfasst von: Schranz, W. [VerfasserIn]
• Verfasst von: Semmler, W. [VerfasserIn]
• Verfasst von: Risse, Frank [VerfasserIn]
• Titel: Quantification of pulmonary microcirculation by dynamic contrast-enhanced magnetic resonance imaging
• Titelzusatz: comparison of four regularization methods
• Verf.angabe: M. Salehi Ravesh, G. Brix, F.B. Laun, T.A. Kuder, M. Puderbach, J. Ley-Zaporozhan, S. Ley, A. Fieselmann, M.F. Herrmann, W. Schranz, W. Semmler, and F. Risse
• Jahr: 2013
• Umfang: 12 S.
• Fussnoten: Published online 1 March 2012 ; Gesehen am 16.12.2021
• Titel Quelle: Enthalten in: Magnetic resonance in medicine
• Ort Quelle: New York, NY [u.a.] : Wiley-Liss, 1984
• Jahr Quelle: 2013
• Band/Heft Quelle: 69(2013), 1 vom: Jan., Seite 188-199
• ISSN Quelle: 1522-2594
• DOI: doi:10.1002/mrm.24220
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: lung perfusion
• Sach-SW: model-free algebraic deconvolution
• Sach-SW: quantitative analysis
• Sach-SW: Tikhonov regularization
• K10plus-PPN: 178235929X

Abstract

Tissue microcirculation can be quantified by a deconvolution analysis of concentration-time curves measured by dynamic contrast-enhanced magnetic resonance imaging. However, deconvolution is an ill-posed problem, which requires regularization of the solutions. In this work, four algebraic deconvolution/regularization methods were evaluated: truncated singular value decomposition and generalized Tikhonov regularization (GTR) in combination with the L-curve criterion, a modified LCC (GTR-MLCC), and a response function model that takes a-priori knowledge into account. To this end, dynamic contrast-enhanced magnetic resonance imaging data sets were simulated by an established physiologically reference model for different signal-to-noise ratios and measured on a 1.5-T system in the lung of 10 healthy volunteers and 20 patients. Analysis of both the simulated and measured dynamic contrast-enhanced magnetic resonance imaging datasets revealed that GTR in combination with the L-curve criterion does not yield reliable and clinically useful results. The three other deconvolution/regularization algorithms resulted in almost identical microcirculatory parameter estimates for signal-to-noise ratios > 10. At low signal-to-noise ratios levels (<10) typically occurring in pathological lung regions, GTR in combination with a modified L-curve criterion approximates the true response function much more accurately than truncated singular value decomposition and GTR in combination with response function model with a difference in accuracy of up to 76%. In conclusion, GTR in combination with a modified L-curve criterion is recommended for the deconvolution of dynamic contrast-enhanced magnetic resonance imaging curves measured in the lung parenchyma of patients with highly heterogeneous signal-to-noise ratios. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.