Dual-energy CT and CT perfusion for improved CT stroke imaging

CT is often used as the modality of choice for the diagnosis of patients with suspicion of stroke. CT imaging allows visualization of occluded blood vessels and identification of location and volume of the infarction. Current CT technological innovations are aimed at increasing the diagnostic image quality, increasing the predictive value and quantitative accuracy of CT, and reducing the radiation dose. Modern CT techniques include CT perfusion (CTP) for infarct assessment and dual-energy CT (DECT) for improved tissue characterization. The goal of this thesis was to improve the diagnostic performance for infarct detection using novel DECT and improve CTP analysis with DECT and high-resolution CTP. In part one of this thesis, we showed that dual-layer detector CT, using a 120 kVp tube potential, can be routinely used in daily clinical practice to provide additional information without increasing radiation dose when compared with a conventional single-layer detector CT scanner. We also found that the image quality of conventional CT images acquired on a dual-layer CT scanner is similar to its counterpart on a conventional CT scanner for medium-sized phantoms, and slightly lower for (very) large phantoms at lower tube voltages. Next, we found that non-contrast DECT virtual monochromatic images (VMI) significantly improves the image quality of non-contrast brain CTs compared with conventional CT. In part two, we found that non-contrast head VMI at 80–90 keV more accurately detected and localized infarcts compared with conventional CT. Next, we found that virtual ischemia maps from non-contrast brain CT were more accurate than conventional CT in approximating infarct core volumes and the infarct location. This suggests that DECT more accurately differentiates between infarcted and healthy tissue than conventional CT. In addition, the added value of dual-energy VMI CTP scans to the quality of the perfusion maps was shown. We found that the image quality and visual quality of 50 keV CT perfusion maps is superior to that of conventional 80 and 120 kVp images. In part three, we found that increasing the acquisition interval may introduce a bias in the perfusion parameters, but this bias can be corrected by calibration of the perfusion maps and therefore still allow distinction between healthy and infarcted tissue. Infarct volumes can likewise be influenced by the acquisition interval, but visual inspection indicated minor differences in infarct volumes between acquisition intervals. For a commercial block-circulant singular value decomposition (bSVD) perfusion analysis package acquisition intervals up to 4 seconds could be achieved, and for a non-commercial bSVD and a non-linear regression model-based method intervals up to 5 seconds. We also shown the ability of thin-slice CTP to detect small-volume infarctions by noise reduction using two bilateral filters. We found that perfusion values are estimated more accurately and with higher contrast using guided bilateral filtering compared with time-intensity profile similarity (TIPS) bilateral filtering on thin-slice CTP. While the detection of small-volume infarctions remains difficult, infarcts could be detected with higher sensitivity and significantly higher diagnostic certainty and improved image quality using guided bilateral filtering than with the current state-of-the-art TIPS filter

Fully automated quantification method (FQM) of coronary calcium in an anthropomorphic phantom

Objective: Coronary 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 Methods: A 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 were acquired with four state-of-the-art CT systems using routine CAC scoring acquisition protocols. For interscan 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 vendor-specific 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. Results: Three 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 (1.000 [0.999-1.000] to 1.000 [1.000-1.000]). An excellent interplatform reliability of κ = 0.969 and κ = 0.973 was found. TTF results gave a maximum deviation of 3.8% and NPS results were comparable to imQuest. Conclusions: We developed a fully automated, open-source, robust method to quantify CAC on CT scans in a commercially available phantom. Also, the automated algorithm contains image quality assessment for fast comparison of differences in acquisition and reconstruction parameters.</p

Computed Tomography Perfusion Data for Acute Ischemic Stroke Evaluation Using Rapid Software : Pitfalls of Automated Postprocessing

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

Probability maps classify ischemic stroke regions more accurately than CT perfusion summary maps

Objectives: To compare single parameter thresholding with multivariable probabilistic classification of ischemic stroke regions in the analysis of computed tomography perfusion (CTP) parameter maps. Methods: Patients were included from two multicenter trials and were divided into two groups based on their modified arterial occlusive lesion grade. CTP parameter maps were generated with three methods—a commercial method (ISP), block-circulant singular value decomposition (bSVD), and non-linear regression (NLR). Follow-up non-contrast CT defined the follow-up infarct region. Conventional thresholds for individual parameter maps were established with a receiver operating characteristic curve analysis. Probabilistic classification was carried out with a logistic regression model combining the available CTP parameters into a single probability. Results: A total of 225 CTP data sets were included, divided into a group of 166 patients with successful recanalization and 59 with persistent occlusion. The precision and recall of the CTP parameters were lower individually than when combined into a probability. The median difference [interquartile range] in mL between the estimated and follow-up infarct volume was 29/23/23 [52/50/52] (ISP/bSVD/NLR) for conventional thresholding and was 4/6/11 [31/25/30] (ISP/bSVD/NLR) for the probabilistic classification. Conclusions: Multivariable probability maps outperform thresholded CTP parameter maps in estimating the infarct lesion as observed on follow-up non-contrast CT. A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions. Key Points: • Combining CTP parameters with a logistic regression model increases the precision and recall in estimating ischemic stroke regions. • Volumes following from a probabilistic analysis predict follow-up infarct volumes better than volumes following from a threshold-based analysis. • A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions

Probability maps classify ischemic stroke regions more accurately than CT perfusion summary maps

Objectives: To compare single parameter thresholding with multivariable probabilistic classification of ischemic stroke regions in the analysis of computed tomography perfusion (CTP) parameter maps. Methods: Patients were included from two multicenter trials and were divided into two groups based on their modified arterial occlusive lesion grade. CTP parameter maps were generated with three methods—a commercial method (ISP), block-circulant singular value decomposition (bSVD), and non-linear regression (NLR). Follow-up non-contrast CT defined the follow-up infarct region. Conventional thresholds for individual parameter maps were established with a receiver operating characteristic curve analysis. Probabilistic classification was carried out with a logistic regression model combining the available CTP parameters into a single probability. Results: A total of 225 CTP data sets were included, divided into a group of 166 patients with successful recanalization and 59 with persistent occlusion. The precision and recall of the CTP parameters were lower individually than when combined into a probability. The median difference [interquartile range] in mL between the estimated and follow-up infarct volume was 29/23/23 [52/50/52] (ISP/bSVD/NLR) for conventional thresholding and was 4/6/11 [31/25/30] (ISP/bSVD/NLR) for the probabilistic classification. Conclusions: Multivariable probability maps outperform thresholded CTP parameter maps in estimating the infarct lesion as observed on follow-up non-contrast CT. A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions. Key Points: • Combining CTP parameters with a logistic regression model increases the precision and recall in estimating ischemic stroke regions. • Volumes following from a probabilistic analysis predict follow-up infarct volumes better than volumes following from a threshold-based analysis. • A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions

Early detection of small volume stroke and thromboembolic sources with computed tomography: Rationale and design of the ENCLOSE study

Background: Computed tomography is the most frequently used imaging modality in acute stroke imaging protocols. Detection of small volume infarcts in the brain and cardioembolic sources of stroke is difficult with current computed tomography protocols. Furthermore, the role of computed tomography findings to predict recurrent ischemic stroke is unclear. With ENCLOSE, we aim to improve (1) the detection of small volume infarcts with thin slice computed tomography perfusion (CTP) images and thromboembolic source with cardiac computed tomography techniques in the acute stage of ischemic stroke and (2) prediction of recurrent ischemic stroke with computed tomography-derived predictors. Methods/design: ENCLOSE is a prospective multicenter observational cohort study, which will be conducted in three Dutch stroke centers (ClinicalTrials.gov Identifier: NCT04019483). Patients (≥18 years) with suspected acute ischemic stroke who undergo computed tomography imaging within 9 h after symptom onset are eligible. Computed tomography imaging includes non-contrast CT, CTP, and computed tomography angiography (CTA) from base of the heart to the top of the brain. Dual-energy CT data will be acquired when possible, and thin-slice CTP reconstructions will be obtained in addition to standard 5 mm CTP data. CTP data will be processed with commercially available software and locally developed model-based methods. The post-processed thin-slice CTP images will be compared to the standard CTP images and to magnetic resonance diffusion-weighted imaging performed within 48 h after admission. Detection of cardioembolic sources of stroke will be evaluated on the CTA images. Recurrence will be evaluated 90 days and two years after the index event. The added value of imaging findings to prognostic models for recurrent ischemic stroke will be evaluated. Conclusion: The aim of ENCLOSE is to improve early detection of small volume stroke and thromboembolic sources and to improve prediction of recurrence in patients with acute ischemic stroke