Virtual monochromatic dual-energy CT reconstructions improve detection of cerebral infarct in patients with suspicion of stroke. Neuroradiology van Ommen, F., Dankbaar, J. W., Zhu, G., Wolman, D. N., Heit, J. J., Kauw, F., Bennink, E., de Jong, H. W., Wintermark, M. 2020


PURPOSE: Early infarcts are hard to diagnose on non-contrast head CT. Dual-energy CT (DECT) may potentially increase infarct differentiation. The optimal DECT settings for differentiation were identified and evaluated.METHODS: One hundred and twenty-five consecutive patients who presented with suspected acute ischemic stroke (AIS) and underwent non-contrast DECT and subsequent DWI were retrospectively identified. The DWI was used as reference standard. First, virtual monochromatic images (VMI) of 25 patients were reconstructed from 40 to 140keV and scored by two readers for acute infarct. Sensitivity, specificity, positive, and negative predictive values for infarct detection were compared and a subset of VMI energies were selected. Next, for a separate larger cohort of 100 suspected AIS patients, conventional non-contrast CT (NCT) and selected VMI were scored by two readers for the presence and location of infarct. The same statistics for infarct detection were calculated. Infarct location match was compared per vascular territory. Subgroup analyses were dichotomized by time from last-seen-well to CT imaging.RESULTS: A total of 80-90keV VMI were marginally more sensitive (36.3-37.3%) than NCT (32.4%; p>0.680), with marginally higher specificity (92.2-94.4 vs 91.1%; p>0.509) for infarct detection. Location match was superior for VMI compared with NCT (28.7-27.4 vs 19.5%; p<0.010). Within 4.5h from last-seen-well, 80keV VMI more accurately detected infarct (58.0 vs 54.0%) and localized infarcts (27.1 vs 11.9%; p=0.004) than NCT, whereas after 4.5h, 90keV VMI was more accurate (69.3 vs 66.3%).CONCLUSION: Non-contrast 80-90keV VMI best differentiates normal from infarcted brain parenchyma.

View details for DOI 10.1007/s00234-020-02492-y

View details for PubMedID 32728777