Myocardial blood flow estimates from dynamic contrast-enhanced magnetic resonance imaging: three quantitative methods. (25th January 2018)

• Record Type: Journal Article
• Authors: Borrazzo, Cristian; Galea, Nicola; Pacilio, Massimiliano; Altabella, Luisa; Preziosi, Enrico; Carnì, Marco; Ciolina, Federica; Vullo, Francesco; Francone, Marco; Catalano, Carlo; Carbone, Iacopo
• Is Part Of: Physics in medicine & biology. Volume 63:Number 3(2018:Feb.)
• Publication Date: 2018-01-25
• Subjects: cold pressor test -- myocardial blood flow quantification -- cardiovascular magnetic resonance imaging -- deconvolution technique; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aaa2a8
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Dynamic contrast-enhanced cardiovascular magnetic resonance imaging can be used to quantitatively assess the myocardial blood flow (MBF), recovering the tissue impulse response function for the transit of a gadolinium bolus through the myocardium. Several deconvolution techniques are available, using various models for the impulse response. The method of choice may influence the results, producing differences that have not been deeply investigated yet. Three methods for quantifying myocardial perfusion have been compared: Fermi function modelling (FFM), the Tofts model (TM) and the gamma function model (GF), with the latter traditionally used in brain perfusion MRI. Thirty human subjects were studied at rest as well as under cold pressor test stress (submerging hands in ice-cold water), and a single bolus of gadolinium weighing 0.1 ± 0.05 mmol kg −1 was injected. Perfusion estimate differences between the methods were analysed by paired comparisons with Student's t -test, linear regression analysis, and Bland–Altman plots, as well as also using the two-way ANOVA, considering the MBF values of all patients grouped according to two categories: calculation method and rest/stress conditions. Perfusion estimates obtained by various methods in both rest and stress conditions were not significantly different, and were in good agreement with the literature. The results obtained during the first-pass transit time (20 s) yielded p -values in the range 0.20–0.28 for Student's t -test, linear regression analysis slopes between 0.98–1.03, and R values between 0.92–1.01. From the Bland–Altman plots, the paired comparisons yielded a bias (and a 95% CI)—expressed as ml/min/g—for FFM versus TM, −0.01 (−0.20, 0.17) or 0.02 (−0.49, 0.52) at rest or under stress respectively, for FFM versus GF, −0.05 (−0.29, 0.20) or −0.07 (−0.55, 0.41) at rest or under stress, and for TM versus GF, −0.03 (−0.30, 0.24) or −0.09 (−0.43, 0.26) at rest or under stress. With the two-way ANOVA, the results were p = 0.20 for the method effect (not significant), p < 0.0001 for the rest/stress condition effect (highly significant, as expected), whereas no interaction resulted between the rest/stress condition and method ( p = 0.70, not significant). Considering a wider time-frame (60 s), the estimates for both rest and stress conditions were 25%–30% higher ( p in the range 0.016–0.025) than those obtained in the 20 s time-frame. MBF estimates obtained by various methods under rest/stress conditions were not significantly different in the first-pass transit time, encouraging quantitative perfusion estimates in DCE-CMRI with the used methods.