Abstract
To explore the feasibility of using correlation-based time-delay (CTD) maps produced from time-resolved MR angiography (TRMRA) to diagnose perfusion abnormalities in patients suspected to have steno-occlusive lesions in the craniocervical arteries. Twenty-seven patients who were suspected to have steno-occlusive lesions in the craniocervical arteries underwent both TRMRA and brain single-photon emission computed tomography (SPECT). TRMRA was performed on the supra-aortic area after intravenous injection of a 0.03 mmol/kg gadolinium-based contrast agent. Time-to-peak (TTP) maps and CTD maps of the brain were automatically generated from TRMRA data, and their quality was assessed. Detection of perfusion abnormalities was compared between CTD maps and the time-series maximal intensity projection (MIP) images from TRMRA and TTP maps. Correlation coefficients between quantitative changes in SPECT and parametric maps for the abnormal perfusion areas were calculated. The CTD maps were of significantly superior quality than TTP maps (p < 0.01). For perfusion abnormality detection, CTD maps (kappa 0.84, 95% confidence interval [CI] 0.67-1.00) showed better agreement with SPECT than TTP maps (0.66, 0.46-0.85). For perfusion deficit detection, CTD maps showed higher accuracy (85.2%, 95% CI 66.3-95.8) than MIP images (66.7%, 46-83.5), with marginal significance (p = 0.07). In abnormal perfusion areas, correlation coefficients between SPECT and CTD (r = 0.74, 95% CI 0.34-0.91) were higher than those between SPECT and TTP (r = 0.66, 0.20-0.88). CTD maps generated from TRMRA were of high quality and offered good diagnostic performance for detecting perfusion abnormalities associated with steno-occlusive arterial lesions in the craniocervical area. • Generation of perfusion parametric maps from time-resolved MR angiography is clinically useful. • Correlation-based delay maps can be used to detect perfusion abnormalities associated with steno-occlusive craniocervical arteries. • Estimation of correlation-based delay is robust for low signal-to-noise 4D MR data.
Published Version
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