title: Quantification of pulmonary microcirculation by dynamic contrast-enhanced magnetic resonance imaging: comparison of four regularization methods.
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.
authors.affiliation: Department of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany. m.salehiravesh@dkfz-heidelberg.de
authors.forename: M
authors.initials: M
authors.lastname: Salehi Ravesh
authors.forename: G
authors.initials: G
authors.lastname: Brix
authors.forename: F B
authors.initials: FB
authors.lastname: Laun
authors.forename: T A
authors.initials: TA
authors.lastname: Kuder
authors.forename: M
authors.initials: M
authors.lastname: Puderbach
authors.forename: J
authors.initials: J
authors.lastname: Ley-Zaporozhan
authors.forename: S
authors.initials: S
authors.lastname: Ley
authors.forename: A
authors.initials: A
authors.lastname: Fieselmann
authors.forename: M F
authors.initials: MF
authors.lastname: Herrmann
authors.forename: W
authors.initials: W
authors.lastname: Schranz
authors.forename: W
authors.initials: W
authors.lastname: Semmler
authors.forename: F
authors.initials: F
authors.lastname: Risse
mesh_terms: D000328:Adult; D003287:Contrast Media; D005260:Female; D006801:Humans; D008168:Lung / Q000098:blood supply*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008833:Microcirculation / Q000502:physiology*; D008875:Middle Aged
chemical_list: D003287:Contrast Media