Marcel T. H. Oei

4D-CTA in Neurovascular Disease: A Review

SUMMARY: CT angiography is a widely used technique for the noninvasive evaluation of neurovascular pathology. Because CTA is a snapshot of arterial contrast enhancement, information on flow dynamics is limited. Dynamic CTA techniques, also referred to as 4D-CTA, have become available for clinical practice in recent years. This article provides a description of 4D-CTA techniques and a review of the available literature on the application of 4D-CTA for the evaluation of intracranial vascular malformations and hemorrhagic and ischemic stroke. Most of the research performed to date consists of observational cohort studies or descriptive case series. These studies show that intracranial vascular malformations can be adequately depicted and classified by 4D-CTA, with DSA as the reference standard. In ischemic stroke, 4D-CTA better estimates thrombus burden and the presence of collateral vessels than conventional CTA. In intracranial hemorrhage, 4D-CTA improves the detection of the “spot” sign, which represents active ongoing bleeding.

Quantitative Dose Dependency Analysis of Whole-Brain CT Perfusion Imaging

PURPOSE To quantitatively assess whether decreasing total radiation dose of the image acquisition protocol has an effect on cerebral CT perfusion values in patients with acute stroke. MATERIALS AND METHODS This retrospective study was approved by the institutional ethics committee, and informed consent was waived. Twenty consecutive patients with ischemic stroke who underwent CT perfusion imaging with a 320-detector row CT scanner were included. A standard acquisition protocol was used, which was started 5 seconds after injection of a contrast agent, with a scan at 200 mAs, followed after 4 seconds by 13 scans, one every 2 seconds, at 100 mAs, and then five scans, one every 5 seconds, at 75 mAs. The total examination had an average effective dose of 5.0 mSv. For each patient, a patient-specific digital perfusion phantom was constructed to simulate the same protocol at a lower total dose (0.5-5.0 mSv, with stepped doses of 0.5 mSv). The lowest setting for which the maximum mean difference remained within 5% of the reference standard (at 5.0 mSv) was marked as the optimal setting. At the optimal setting, Pearson correlation coefficients were calculated to assess correlations with the reference values, and paired t tests were performed to compare the means. RESULTS At 2.5 mSv, the maximum mean differences in values from those of the reference standard were 4.5%, 5.0%, and 1.9%, for cerebral blood flow, cerebral blood volume, and mean transit time, respectively. Pearson correlation coefficients of perfusion values for white matter and gray matter were 0.864-0.917, and all differences were significant (P < .0001). Paired t tests showed no significant differences between the reference standard and optimal settings (P = .089-.923). CONCLUSION The total dose of a clinical cerebral CT perfusion protocol can be lowered to 2.5 mSv, with only minor quantitative effects on perfusion values. Dose reduction beyond this point resulted in overestimation of perfusion values.

White Matter and Gray Matter Segmentation in 4D Computed Tomography

Modern Computed Tomography (CT) scanners are capable of acquiring contrast dynamics of the whole brain, adding functional to anatomical information. Soft tissue segmentation is important for subsequent applications such as tissue dependent perfusion analysis and automated detection and quantification of cerebral pathology. In this work a method is presented to automatically segment white matter (WM) and gray matter (GM) in contrast- enhanced 4D CT images of the brain. The method starts with intracranial segmentation via atlas registration, followed by a refinement using a geodesic active contour with dominating advection term steered by image gradient information, from a 3D temporal average image optimally weighted according to the exposures of the individual time points of the 4D CT acquisition. Next, three groups of voxel features are extracted: intensity, contextual, and temporal. These are used to segment WM and GM with a support vector machine. Performance was assessed using cross validation in a leave-one-patient-out manner on 22 patients. Dice coefficients were 0.81 ± 0.04 and 0.79 ± 0.05, 95% Hausdorff distances were 3.86 ± 1.43 and 3.07 ± 1.72 mm, for WM and GM, respectively. Thus, WM and GM segmentation is feasible in 4D CT with good accuracy.

Brain tissue segmentation in 4D CT using voxel classification

A method is proposed to segment anatomical regions of the brain from 4D computer tomography (CT) patient data. The method consists of a three step voxel classification scheme, each step focusing on structures that are increasingly difficult to segment. The first step classifies air and bone, the second step classifies vessels and the third step classifies white matter, gray matter and cerebrospinal fluid. As features the time averaged intensity value and the temporal intensity change value were used. In each step, a k-Nearest-Neighbor classifier was used to classify the voxels. Training data was obtained by placing regions of interest in reconstructed 3D image data. The method has been applied to ten 4D CT cerebral patient data. A leave-one-out experiment showed consistent and accurate segmentation results.

Author Correction: White Matter and Gray Matter Segmentation in 4D Computed Tomography

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Observer variability of reference tissue selection for relativecerebral blood volume measurements in glioma patients

ObjectivesTo assess observer variability of different reference tissues used for relative CBV (rCBV) measurements in DSC-MRI of glioma patients.MethodsIn this retrospective study, three observers measured rCBV in DSC-MR images of 44 glioma patients on two occasions. rCBV is calculated by the CBV in the tumour hotspot/the CBV of a reference tissue at the contralateral side for normalization. One observer annotated the tumour hotspot that was kept constant for all measurements. All observers annotated eight reference tissues of normal white and grey matter. Observer variability was evaluated using the intraclass correlation coefficient (ICC), coefficient of variation (CV) and Bland-Altman analyses.ResultsFor intra-observer, the ICC ranged from 0.50–0.97 (fair–excellent) for all reference tissues. The CV ranged from 5.1–22.1 % for all reference tissues and observers. For inter-observer, the ICC for all pairwise observer combinations ranged from 0.44–0.92 (poor–excellent). The CV ranged from 8.1–31.1 %. Centrum semiovale was the only reference tissue that showed excellent intra- and inter-observer agreement (ICC>0.85) and lowest CVs (<12.5 %). Bland-Altman analyses showed that mean differences for centrum semiovale were close to zero.ConclusionSelecting contralateral centrum semiovale as reference tissue for rCBV provides the lowest observer variability.Key Points• Reference tissue selection for rCBV measurements adds variability to rCBV measurements.• rCBV measurements vary depending on the choice of reference tissue.• Observer variability of reference tissue selection varies between poor and excellent.• Centrum semiovale as reference tissue for rCBV provides the lowest observer variability.

Automated artery and vein detection in 4D-CT data with an unsupervised classification algorithm of the time intensity curves

In this work a fully automated detection method for artery input function (AIF) and venous output function (VOF) in 4D-computer tomography (4D-CT) data is presented based on unsupervised classification of the time intensity curves (TIC) as input data. Bone and air voxels are first masked out using thresholding of the baseline measurement. The TICs for each remaining voxel are converted to time-concentration-curves (TCC) by subtracting the baseline value from the TIC. Then, an unsupervised K-means classifier is applied to each TCC with an area under the curve (AUC) larger than 95% of the maximum AUC of all TCCs. The results are three clusters, which yield average TCCs for vein and artery voxels in the brain, respectively. A third cluster generally represents a vessel outside the brain. The algorithm was applied to five 4D-CT patient data who were scanned on the suspicion of ischemic stroke. For all _ve patients, the algorithm yields reasonable classification of arteries and veins as well as reasonable and reproducible AIFs and VOF. To our knowledge, this is the first application of an unsupervised classification method to automatically identify arteries and veins in 4D-CT data. Preliminary results show the feasibility of using K-means clustering for the purpose of artery-vein detection in 4D-CT patient data.

A pattern recognition framework for vessel segmentation in 4D CT of the brain

In this study, a pattern recognition-based framework is presented to automatically segment the complete cerebral vasculature from 4D Computed Tomography (CT) patient data. Ten consecutive patients whom were admitted to our hospital on a suspicion of ischemic stroke were included in this study. A background mask and bone mask were calculated based on intensity thresholding and morphological operations, and the following six image features were proposed: 1) a subtraction image of a subtraction image consisting of timing-invariant CTA and non-constrast CT, 2) the area under the curve of a gamma variate function fitted to the tissue curves, 3-5) three optimized parameter values of this gamma variate function, and 6) a vessel likeliness function. After masking bone and background, these features were used to train a linear discriminant voxel classifier (LDC) on regions of interest (ROIs), which were annotated in soft tissue (white matter and gray matter) and vessels by an expert observer. The LDC was trained in a leave-one-out manner in which 9 patients tissue ROIs were used for training and the remaining patient tissue ROIs were used for testing the classifier. To evaluate the frame work, for each training cycle the accuracy was calculated by dividing the true positives and negatives by the true positives and negatives and false positives and negatives. The resulting averaged accuracy was 0:985±0:014 with a range of 0:957 to 0:999.