Computed tomography perfusion (CTP) is increasingly used to determine treatment eligibility for acute ischemic stroke patients. Automated postprocessing of raw CTP data is routinely used, but it can fail. In reviewing 176 consecutive acute ischemic stroke patients, failures occurred in 20 patients (11%) during automated postprocessing by the RAPID software. Failures were caused by motion (n = 11, 73%), streak artifacts (n = 2, 13%), and poor contrast bolus arrival (n = 2, 13%). Stroke physicians should review CTP results with care before they are being integrated in their decision-making process.

ObjectiveCoronary artery calcium (CAC) score is a strong predictor for future adverse cardiovascular events.Anthropomorphic phantoms are often used for CAC studies on computed tomography (CT) to allow for evaluation or variation of scanning or reconstruction parameters within or across scanners against a reference standard. This often results in large number of datasets. Manual assessment of these large datasets is time consuming and cumbersome. Therefore, this study aimed to develop and validate a fully automated, open-source quantification method (FQM) for coronary calcium in a standardized phantom. Materials and MethodsA standard, commercially available anthropomorphic thorax phantom was used with an insert containing nine calcifications with different sizes and densities. To simulate two different patient sizes, an extension ring was used. Image data was acquired with four state-of-the-art CT systems using routine CAC scoring acquisition protocols. For inter-scan variability, each acquisition was repeated five times with small translations and/or rotations. Vendor-specific CAC scores (Agatston, volume, and mass) were calculated as reference scores using vendor-specific software. Both the international standard CAC quantification methods as well as vendorspecific adjustments were implemented in FQM. Reference and FQM scores were compared using Bland-Altman analysis, intraclass correlation coefficients, risk reclassifications, and Cohen's kappa. Also, robustness of FQM was assessed using varied acquisitions and reconstruction settings and validation on a dynamic phantom. Further, image quality metrics were implemented: noise power spectrum, task transfer function, and contrast-and signal-to-noise ratio among others. Results were validated using imQuest software. ResultsThree parameters in CAC scoring methods varied among the different vendor-specific software packages: the Hounsfield unit (HU) threshold, the minimum area used to designate a group of voxels as calcium, and the usage of isotropic voxels for the volume score. The FQM was in high agreement with vendor-specific scores and ICC's (median [95% CI]) were excellent (1.

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 140 keV 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 80-90 keV 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 4.5 h from last-seen-well, 80 keV 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 4.5 h, 90 keV VMI was more accurate (69.3 vs 66.3%). Conclusion Non-contrast 80-90 keV VMI best differentiates normal from infarcted brain parenchyma.