AIMS: we investigated the performance of quantitative stress perfusion magnetic resonance imaging (MRI) as a basis for identifying and characterizing the area-at-risk subtending a chronic coronary artery (CA) stenosis. METHODS AND RESULTS: pigs underwent a percutaneous copper-coated stent implantation in the circumflex CA (n = 11) or a sham operation (n = 5). After 6 weeks, angiography and MRI were performed including cine (rest, low- and high-dose dobutamine stress), dual-bolus first-pass perfusion (rest and adenosine stress), and contrast-enhanced imaging to quantify myocardial infarction (MI). Myocardial blood flow (MBF) was quantified based on Fermi-model deconvolution and compared with microsphere measurements. On the basis of Evan's blue staining, MBF thresholds to define the area-at-risk were determined by receiver-operating characteristic (ROC) analysis. CA stenosis was 94 ± 7% and infarct size (IS) 7.3 ± 3.1% of left ventricular mass. Segmental thresholds of hyperaemic MBF yielded the best performance for detecting area-at-risk. There was a good correlation between MRI and microsphere perfusion (r(2) = 0.84, P < .0001). The area-at-risk presented a mixed substrate of non-infarcted (non-MI), <50% infarcted (MI+), and >50% infarcted (MI++) segments. MBF was reduced in at-risk vs. remote segments at rest (non-MI, 0.50 ± 0.21; MI+, 0.47 ± 0.14; MI++, 0.42 ± 0.14; remote, 0.84 ± 0.25 mL/min/g) and during stress (non-MI, 0.69 ± 0.09; MI+, 0.66 ± 0.14; MI++, 0.51 ± 0.11; remote, 1.70 ± 0.36 mL/min/g). Segmental wall thickening showed different responses to stress (remote, progressive increase during incremental stress; non-MI, increase at low-dose and discontinued at high-dose; MI+, initial increase and decrease at high-dose; MI++, progressive decrease). CONCLUSION: quantitative hyperaemic perfusion MRI accurately defines segments in the area-at-risk in chronic ischaemia, which present with different functional response to stress related to segmental IS.
AIMS: we investigated the performance of quantitative stress perfusion magnetic resonance imaging (MRI) as a basis for identifying and characterizing the area-at-risk subtending a chronic coronary artery (CA) stenosis. METHODS AND RESULTS:pigs underwent a percutaneous copper-coated stent implantation in the circumflex CA (n = 11) or a sham operation (n = 5). After 6 weeks, angiography and MRI were performed including cine (rest, low- and high-dose dobutamine stress), dual-bolus first-pass perfusion (rest and adenosine stress), and contrast-enhanced imaging to quantify myocardial infarction (MI). Myocardial blood flow (MBF) was quantified based on Fermi-model deconvolution and compared with microsphere measurements. On the basis of Evan's blue staining, MBF thresholds to define the area-at-risk were determined by receiver-operating characteristic (ROC) analysis. CA stenosis was 94 ± 7% and infarct size (IS) 7.3 ± 3.1% of left ventricular mass. Segmental thresholds of hyperaemic MBF yielded the best performance for detecting area-at-risk. There was a good correlation between MRI and microsphere perfusion (r(2) = 0.84, P < .0001). The area-at-risk presented a mixed substrate of non-infarcted (non-MI), <50% infarcted (MI+), and >50% infarcted (MI++) segments. MBF was reduced in at-risk vs. remote segments at rest (non-MI, 0.50 ± 0.21; MI+, 0.47 ± 0.14; MI++, 0.42 ± 0.14; remote, 0.84 ± 0.25 mL/min/g) and during stress (non-MI, 0.69 ± 0.09; MI+, 0.66 ± 0.14; MI++, 0.51 ± 0.11; remote, 1.70 ± 0.36 mL/min/g). Segmental wall thickening showed different responses to stress (remote, progressive increase during incremental stress; non-MI, increase at low-dose and discontinued at high-dose; MI+, initial increase and decrease at high-dose; MI++, progressive decrease). CONCLUSION: quantitative hyperaemic perfusion MRI accurately defines segments in the area-at-risk in chronic ischaemia, which present with different functional response to stress related to segmental IS.
Authors: Jaroslaw Zalewski; Piet Claus; Jan Bogaert; Nina Vanden Driessche; Ronald B Driesen; Diogo T Galan; Karin R Sipido; Piotr Buszman; Krzysztof Milewski; Frans Van de Werf Journal: Basic Res Cardiol Date: 2015-02-27 Impact factor: 17.165
Authors: Olivier Gheysens; Vamsidhar Akurathi; Rufael Chekol; Tom Dresselaers; Sofie Celen; Michel Koole; Dieter Dauwe; Bernard J Cleynhens; Piet Claus; Stefan Janssens; Alfons M Verbruggen; Johan Nuyts; Uwe Himmelreich; Guy M Bormans Journal: EJNMMI Res Date: 2013-01-15 Impact factor: 3.138