Pieter C. Vos

The anatomy of visuospatial construction revealed by lesion-symptom mapping.

Visuospatial construction is a complex cognitive operation that is composed of a purely constructional component (visuoconstruction proper), and visuoperceptive, attentional, and decision-making components. The anatomical correlates of visuospatial construction and its cognitive subcomponents are poorly understood. The purpose of the present study was to determine the anatomical correlates of visuospatial construction by applying lesion-symptom mapping in a cohort of 111 patients with first-ever ischemic stroke. We employed the Rey-Osterrieth Complex Figure (ROCF) copy test and the Judgment of Line Orientation (JLO); both tests measure visuoperception, while only the ROCF has a constructional component. We first performed assumption-free voxel-based lesion-symptom mapping, which revealed large shared right hemispheric correlates for the ROCF and JLO in the frontal lobe, superior temporal lobe, and supramarginal gyrus. These shared anatomical correlates reflect the visuoperceptive component of the ROCF and JLO. Anatomical correlates were discordant in the right superior parietal lobule, and angular and middle occipital gyri: lesions in these regions were associated with poor performance on the ROCF, but not the JLO. Secondly, these findings were reproduced with a region of interest-based analysis that yielded a statistically significant correlation between infarct volume in the right inferior and superior parietal, angular and middle occipital cortices, and poor performance on the ROCF, but not the JLO. This discordance in anatomical correlates of the ROCF and JLO reflects the visuoconstructive component of the ROCF. These findings provide new insights in the anatomical correlates of the visuoperceptive and visuoconstructive components of the ROCF and provide evidence for a crucial role of the right inferior and superior parietal, angular and middle occipital gyri in visuoconstruction proper.

Combined pixel classification and atlas-based segmentation of the ventricular system in brain CT Images

Accurate segmentation of the brain ventricular system in Computed Tomography (CT) images is useful in neurodiagnosis, providing quantitative measures on changes in ventricular size due to stroke. Manual segmentation, however, is a time-consuming, tedious task and is prone to large inter-observer variability. This study presents an automatic ventricle system segmentation method by combining the results of supervised pixel classification based on intensities with spatial information obtained from a multi-atlas-based segmentation method. The method is applied to follow-up brain CT images which were collected from a cohort of 20 patients with proven ischemic stroke. The automatic segmentation performance was evaluated in a leave-one-out strategy by comparing with manual segmentations. The results show that combining information obtained from pixel classification and multi-atlas-based segmentation significantly outperforms each method independently with a mean Dice coefficient index of 0.810.07.±

Improving head and neck CTA with hybrid and model-based iterative reconstruction techniques

AIM To compare image quality of head and neck computed tomography angiography (CTA) reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MIR) algorithms. MATERIALS AND METHODS The raw data of 34 studies were simultaneously reconstructed with FBP, HIR (iDose(4), Philips Healthcare, Best, the Netherlands), and with a prototype version of a MIR algorithm (IMR, Philips Healthcare). Objective (contrast-to-noise ratio [CNR], vascular contrast, automatic vessel analysis [AVA], stenosis grade) and subjective image quality (ranking at level of the circle of Willis, carotid bifurcation, and shoulder) of the five reconstructions were compared using repeated-measures analysis of variance (ANOVA) and post-hoc analysis. RESULTS Vascular contrast was significantly higher in both the circle of Willis and carotid bifurcation with both levels of MIR compared to the other reconstruction methods (all p<0.0001). The CNR was highest for high MIR, followed by low MIR, high HIR, mid HIR and FBP (p<0.001 except low MIR versus high HIR; p>0.33). AVA showed most complete carotids in both MIR-levels, followed by high HIR (p>0.08), mid HIR (p<0.023) and FBP (p<0.010), vertebral arteries completeness was similar (p=0.40 and p=0.06). Stenosis grade showed no significant differences (p=0.16). High HIR showed the best subjective image quality at the circle of Willis and carotid bifurcation level, followed by mid HIR. At shoulder level, low MIR and high HIR were ranked best, followed by high MIR. CONCLUSION Objectively, MIR significantly improved the overall image quality, reduced image noise, and improved automated vessel analysis, whereas FBP showed the lowest objective image quality. Subjectively, the highest level of HIR was considered superior at the level of the circle of Willis and the carotid bifurcation, and along with the lowest level of MIR for the origins of the neck arteries at shoulder level.

Computer-aided diagnosis of acute ischemic stroke based on cerebral hypoperfusion using 4D CT angiography

The presence of collateral blood flow is found to be a strong predictor of patient outcome after acute ischemic stroke. Collateral blood flow is defined as an alternative way to provide oxygenated blood to ischemic cerebral tissue. Assessment of collateral blood supply is currently performed by visual inspection of a Computed Tomography Angiogram (CTA) which introduces inter-observer variability and depends on the grading scale. Furthermore, variations in the arterial contrast arrival time may lead to underestimation of collateral blood supply in a CTA which exerts a negative influence on the prediction of patient outcome. In this study, the feasibility of a Computer-aided Diagnosis system is investigated capable of objectively predicting patient outcome. We present a novel automatic method for quantitative assessment of cerebral hypoperfusion in timing-invariant (i.e. delay insensitive) CTA (TI-CTA). The proposed Vessel Density Symmetry algorithm automatically generates descriptive maps based on hemispheric asymmetry of blood vessels. Intensity and symmetry based features are extracted from these descriptive maps and subjected to a best-first-search feature selection. Linear Discriminant Analysis is performed to combine selected features into a likelihood of good patient outcome. Receiver operating characteristic (ROC) analysis is conducted to evaluate the diagnostic performance of the CAD by leave-one- patient-out cross validation. A Positive Predicting Value of 1 was obtained at a sensitivity of 25% with an area under the ROC-curve of 0.86. The results show that the CAD is feasible to objectively predict patient outcome. The presented CAD could make an important contribution to acute ischemic stroke diagnosis and treatment.

Automatic detection and segmentation of ischemic lesions in computed tomography images of stroke patients

Stroke is the third most common cause of death in developed countries. Clinical trials are currently investigating whether advanced Computed Tomography can be of benefit for diagnosing stroke at the acute phase. These trials are based on large patients cohorts that need to be manually annotated to obtain a reference standard of tissue loss at follow-up, resulting in extensive workload for the radiologists. Therefore, there is a demand for accurate and reliable automatic lesion segmentation methods. This paper presents a novel method for the automatic detection and segmentation of ischemic lesions in CT images. The method consists of multiple sequential stages. In the initial stage, pixel classification is performed using a naive Bayes classifier in combination with a tissue homogeneity algorithm in order to localize ischemic lesion candidates. In the next stage, the candidates are segmented using a marching cubes algorithm. Regional statistical analysis is used to extract features based on local information as well as contextual information from the contra-lateral hemisphere. Finally, the extracted features are summarized into a likelihood of ischemia by a supervised classifier. An area under the Receiver Operating Characteristic curve of 0.91 was obtained for the identification of ischemic lesions. The method performance on lesion segmentation reached a Dice similarity coeficient (DSC) of 0.74±0.09, whereas an independent human observer obtained a DSC of 0.79±0.11 in the same dataset. The experiments showed that it is feasible to automatically detect and segment ischemic lesions in CT images, obtaining a comparable performance as human observers.

Automated prediction of tissue outcome after acute ischemic stroke in computed tomography perfusion images

Assessment of the extent of cerebral damage on admission in patients with acute ischemic stroke could play an important role in treatment decision making. Computed tomography perfusion (CTP) imaging can be used to determine the extent of damage. However, clinical application is hindered by differences among vendors and used methodology. As a result, threshold based methods and visual assessment of CTP images has not yet shown to be useful in treatment decision making and predicting clinical outcome. Preliminary results in MR studies have shown the benefit of using supervised classifiers for predicting tissue outcome, but this has not been demonstrated for CTP. We present a novel method for the automatic prediction of tissue outcome by combining multi-parametric CTP images into a tissue outcome probability map. A supervised classification scheme was developed to extract absolute and relative perfusion values from processed CTP images that are summarized by a trained classifier into a likelihood of infarction. Training was performed using follow-up CT scans of 20 acute stroke patients with complete recanalization of the vessel that was occluded on admission. Infarcted regions were annotated by expert neuroradiologists. Multiple classifiers were evaluated in a leave-one-patient-out strategy for their discriminating performance using receiver operating characteristic (ROC) statistics. Results showed that a RandomForest classifier performed optimally with an area under the ROC of 0.90 for discriminating infarct tissue. The obtained results are an improvement over existing thresholding methods and are in line with results found in literature where MR perfusion was used.

Computed tomography perfusion evaluation after extracranial-intracranial bypass surgery

OBJECTIVE Perfusion imaging is increasingly used for postoperative evaluation of extracranial to intracranial (EC-IC) bypass surgery. Altered hemodynamics and delayed arrival of the contrast agent in the area fed by the bypass can influence perfusion measurement. We compared perfusion asymmetry obtained with different algorithms in EC-IC bypass surgery patients. METHODS We retrospectively identified all patients evaluated with computed tomography perfusion (CTP) between May 2007 and May 2011 after EC-IC bypass surgery at our institution. CTP images were analyzed with three perfusion algorithms that differ among their ability to anticipate for delayed arrival time of contrast material: the delay-sensitive first-moment mean transit time (fMTT), the semi-delay-sensitive standard singular value decomposition (sSVD) and the delay-insensitive block-circulant SVD (bSVD). The interhemispheric difference in bolus arrival time (ΔBAT) was determined to confirm altered hemodynamics. Interhemispheric asymmetry in perfusion values (mean transit time (MTT) difference, cerebral blood flow (CBF) ratio and cerebral blood volume (CBV) ratio) was compared between the three algorithms. Presence of a new infarct in the treated hemisphere was evaluated on follow-up imaging and perfusion asymmetry was compared between patients with and without infarction. RESULTS Twenty-two patients were included. The median interhemispheric difference in ΔBAT was 0.98 s. The median MTT difference was significantly smaller when calculated with the delay-insensitive algorithm than with the other algorithms (0.44 s versus 0.90 s and 0.93 s, p<0.01). The CBF ratio was similar for all algorithms (111.98 versus 112.59 and 112.60). The CBV ratio was similar for all algorithms (113.20 versus 111.95 and 113.97). There was a significant difference in MTT asymmetry between patients with and without infarction with the delay-insensitive algorithm only (1.57 s versus 0.38 s, p=0.04). CONCLUSION In patients with EC-IC bypass surgery, delay-sensitive algorithms showed larger MTT asymmetry than delay-insensitive algorithms. Furthermore, only the delay-insensitive method seems to differentiate between patients with and without infarction on follow-up.