Segmentation-based blood flow parameter refinement in cerebrovascular structures using 4D arterial spin labeling MRA

Objective: Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Time-resolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and blood flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets. Methods: A previously published segmentation method, which uses blood flow data to improve its accuracy, is extended to estimate blood flow parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis. Results: The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated blood flow parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined blood flow estimation is more realistic compared to the raw hemodynamic parameters. Conclusion: The proposed method can provide accurate segmentations and blood flow parameter estimations in the cerebrovascular system using 4D ASL MRA datasets. Significance: The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively

Predicting Clinical Outcome of Stroke Patients with Tractographic Feature

The volume of stroke lesion is the gold standard for predicting the clinical outcome of stroke patients. However, the presence of stroke lesion may cause neural disruptions to other brain regions, and these potentially damaged regions may affect the clinical outcome of stroke patients. In this paper, we introduce the tractographic feature to capture these potentially damaged regions and predict the modified Rankin Scale (mRS), which is a widely used outcome measure in stroke clinical trials. The tractographic feature is built from the stroke lesion and average connectome information from a group of normal subjects. The tractographic feature takes into account different functional regions that may be affected by the stroke, thus complementing the commonly used stroke volume features. The proposed tractographic feature is tested on a public stroke benchmark Ischemic Stroke Lesion Segmentation 2017 and achieves higher accuracy than the stroke volume and the state-of-the-art feature on predicting the mRS grades of stroke patients. In addition, the tractographic feature also yields a lower average absolute error than the commonly used stroke volume feature.Comment: 12 pages, 4 figures, 3 tables. Accepted by MICCAI-BrainLesion 2019 as an oral presentatio

Prediction of Thrombectomy Functional Outcomes using Multimodal Data

Recent randomised clinical trials have shown that patients with ischaemic stroke {due to occlusion of a large intracranial blood vessel} benefit from endovascular thrombectomy. However, predicting outcome of treatment in an individual patient remains a challenge. We propose a novel deep learning approach to directly exploit multimodal data (clinical metadata information, imaging data, and imaging biomarkers extracted from images) to estimate the success of endovascular treatment. We incorporate an attention mechanism in our architecture to model global feature inter-dependencies, both channel-wise and spatially. We perform comparative experiments using unimodal and multimodal data, to predict functional outcome (modified Rankin Scale score, mRS) and achieve 0.75 AUC for dichotomised mRS scores and 0.35 classification accuracy for individual mRS scores.Comment: Accepted at Medical Image Understanding and Analysis (MIUA) 202

Association of stroke lesion shape with newly detected atrial fibrillation - Results from the MonDAFIS study

Paroxysmal Atrial fibrillation (AF) is often clinically silent and may be missed by the usual diagnostic workup after ischemic stroke. We aimed to determine whether shape characteristics of ischemic stroke lesions can be used to predict AF in stroke patients without known AF at baseline. Lesion shape quantification on brain MRI was performed in selected patients from the intervention arm of the Impact of standardized MONitoring for Detection of Atrial Fibrillation in Ischemic Stroke (MonDAFIS) study, which included patients with ischemic stroke or TIA without prior AF. Multiple morphologic parameters were calculated based on lesion segmentation in acute brain MRI data. Multivariate logistic models were used to test the association of lesion morphology, clinical parameters, and AF. A stepwise elimination regression was conducted to identify the most important variables. A total of 755 patients were included. Patients with AF detected within 2 years after stroke (n = 86) had a larger overall oriented bounding box (OBB) volume (p = 0.003) and a higher number of brain lesion components (p = 0.008) than patients without AF. In the multivariate model, OBB volume (OR 1.72, 95%CI 1.29–2.35, p < 0.001), age (OR 2.13, 95%CI 1.52–3.06, p < 0.001), and female sex (OR 2.45, 95%CI 1.41–4.31, p = 0.002) were independently associated with detected AF. Ischemic lesions in patients with detected AF after stroke presented with a more dispersed infarct pattern and a higher number of lesion components. Together with clinical characteristics, these lesion shape characteristics may help in guiding prolonged cardiac monitoring after stroke

A methodology for generating four-dimensional arterial spin labeling MR angiography virtual phantoms

Four-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is a non-invasive medical imaging modality that can be used for anatomical and hemodynamic analysis of the cerebrovascular system. However, it generates a considerable amount of data, which is tedious to analyze visually. As an alternative, medical image processing methods can be used to process the data and present measurements of the geometry and blood flow in the cerebrovascular system to the user, such as vessel radius, tortuosity, blood flow volume, and transit time. Nevertheless, evaluating medical image processing methods developed for this modality requires annotated data, which can be time-consuming and expensive to obtain. Alternatively, virtual simulations are a faster and less expensive option that can be used for initial evaluation of image processing methods. The present work proposes a methodology for generating annotated 4D ASL MRA virtual phantoms, in different scenarios with different acquisition parameter settings. In each scenario, the phantoms are generated using real cerebrovascular geometries of healthy volunteers, where blood flow is simulated according to a mathematical model specifically designed to describe the signal observed in 4D ASL MRA images. Realistic noise is added using an homomorphic approach, designed to replicate noise characteristic of multi-coil acquisitions. In order to exemplify the utility of the phantoms, they are used to evaluate the accuracy of a method to estimate blood flow parameter values, such as relative blood volume and transit time, in different scenarios. The estimated values are then compared to its corresponding virtual ground-truth values. The accuracy of the results is ranked according to the average absolute error. The results of the experiments show that blood flow parameters can be more accurately estimated when blood is magnetically labeled for longer periods of time and when the datasets are acquired with higher temporal resolution. In summary, the present work describes a methodology to create annotated virtual phantoms, which represent a useful alternative for initial evaluation of medical image processing methods for 4D ASL MRA images

Hemodynamic analysis and classification of vessel structures of patients with cerebral arterioveneous malformations

Background: The cerebral arteriovenous malformation (AVM) is a congenital disorder of blood vessels within the brain. An AVM represents an abnormal direct connection between arteries and veins, without normal capillaries between them. Thus, blood flow in other areas of the brain may decrease. Abnormal flow conditions in veins increases the risk of a hemorrhagic stroke and neurological deficit. Objective: For therapy planning information about localization and quantification of the AVM, detection of feeding arteries (Feeders) and draining veins, and the evaluation of the haemodynamics are required. In this paper we present a method for the automatic detection of the nidus of arterioveneous malformations, its feeding arteries, draining veins and "en-passage" vessels as well as parameters describing the haemodynamics. Spatiotemporal 4D magnetic resonance angiography (MRA) image datasets and 3D MRA datasets with high spatial resolution were acquired for analyzing AVMs. Methods: Initially the vessel system of a 3D MRA dataset is segmented. Then using a new method characterizing the haemodynamics by definition of a time point of inflow based on curve fitting the temporal intensity curves of 4D MRA image sequences are analyzed voxelwise. Additionally the velocity of the blood flow is approximated. Based on a non-linear registration method the haemodynamic information can be transferred automatically to the segmented vessel system. Different vessel structures can be characterised automatically by a combined analysis of the intensity, velocity and a relative time point of blood inflow. Results: 19 datasets of patients with a diagnosed AVM were available for evaluation of the proposed method. Artefacts in terms of strong temporal leaps between the time points of inflow of two neighbouring voxels were significantly reduced after the new method extracting the time point of inflow has been applied. The automatic detection of the nidus was validated on the basis of manual segmentation. Experimental results showed a mean volume similarity of approx. 88%. Draining veins and feeding arteries were automatically detected with an accuracy of 95%. Conclusion: The proposed method allows a robust and fully automatic detection of the AVM nidus as well as a characterization of vessels. A visual rating by neuroradiology experts showed that the proposed method describing a time point of inflow resulted in a better presentation of the blood flow than by the results achieved by the usage of conventional parameters. The detection of feeding arteries and draining veins is supporting the physicians in their spatial evaluation of arterioveneous malformations. The detection of the "en-passage" vessels is especially helpful for the planning of surgical resections.Hintergrund: Eine zerebrale arteriovenöse Malformation (AVM) ist eine Gefäßmissbildung im Gehirn, die sich durch das Fehlen eines kapillaren Gefäßbettes mit abnormem Kurzschluss zwischen dem arteriellen und dem folgendem venösen System auszeichnet, dem sog. Nidus. Die veränderten hämodynamischen Bedingungen resultieren in neurologischen Ausfällen sowie in dysplastischen Veränderungen der zu- und abführenden Gefäße und daraus folgenden erhöhten Blutungsrisiko. Zielsetzung: Für die diagnostische Beurteilung der AVM sind Informationen über die individuelle Gefäßstruktur und die Hämodynamik von besonderem Interesse. In diesem Beitrag wird ein Verfahren zur Extraktion von Parametern zur Beschreibung der Hämodynamik präsentiert. Aufbauend hierauf werden Verfahren zur automatischen Detektion des Nidus der arteriovenösen Malformation sowie der zuleitenden (Feeder), ableitenden (Drainagevenen) und "en passage"-Gefäße vorgestellt. Als Eingabe hierfür dienen hochaufgelöste 3D- sowie zeitlich-räumliche 4D-MRT-Bildsequenzen. Methoden: Bei der vorgestellten Methode wird zunächst in den 3D-MRT-Bilddaten das Gefäßsystem semi-automatisch segmentiert. Auf Basis eines neuen Verfahrens zur Charakterisierung der Hämodynamik durch Bestimmung des Einflusszeitpunktes des Kontrastmittels mittels referenzbasierter Kurvenanpassung wird in einem weiteren Schritt in den zeitlich-räumlichen MR-Bildfolgen für jedes Voxel der zeitliche Signalverlauf analysiert. Zusätzlich wird die Flussgeschwindigkeit des Kontrastmittels diskret approximiert. Anschließend werden die extrahierten Parameterbilder mittels eines nicht-linearen Registrierungsverfahrens automatisch auf das segmentierte Gefäßsystem übertragen. Durch eine kombinierte Analyse der Intensität, der Geschwindigkeit und des relativen Einflusszeitpunktes des Blutes werden Gefäßstrukturen automatisch charakterisiert. Ergebnisse: Zur Evaluation der vorgestellte Methode standen 19 Datensätze von Patienten mit diagnostizierter AVM zur Verfügung. Durch Anwendung der neuen Methode zur Beschreibung der Einströmzeitpunkte konnten Artefakte in Form von starken zeitlichen Sprüngen zwischen den Einflusszeitpunkten benachbarter Voxel deutlich verringert werden. Die Detektion des Nidus wurde anhand von manuellen Segmentierungen validiert und ergab eine mittlere Volumenübereinstimmung von ca. 88%. Drainagevenen und Feeder konnten mit einer Genauigkeit von 95% detektiert werden. Schlussfolgerung: Die vorgestellte Methode ermöglicht eine robuste automatische Detektion des AVM-Nidus sowie eine Klassifikation der Gefäße. Eine visuelle Begutachtung durch erfahrene Neuroradiologen ergab, dass bei Verwendung der vorgestellten Methode zur Charakterisierung des Blutflusses mittels referenzbasierter Kurvenanpassung dieser besser dargestellt werden kann, als bei der Verwendung konventioneller Parameter. Die Detektion von zuleitenden und ableitenden Gefäßen unterstützt den Mediziner bei der räumlichen Beurteilung der arteriovenösen Malformation. Die Detektion der "en passage"-Gefäße ist besonders hinsichtlich der Planung von neurochirurgischen Eingriffen von hoher Bedeutung

A methodology for generating four-dimensional arterial spin labeling MR angiography virtual phantoms

Four-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is a non-invasive medical imaging modality that can be used for anatomical and hemodynamic analysis of the cerebrovascular system. However, it generates a considerable amount of data, which is tedious to analyze visually. As an alternative, medical image processing methods can be used to process the data and present measurements of the geometry and blood flow in the cerebrovascular system to the user, such as vessel radius, tortuosity, blood flow volume, and transit time. Nevertheless, evaluating medical image processing methods developed for this modality requires annotated data, which can be time-consuming and expensive to obtain. Alternatively, virtual simulations are a faster and less expensive option that can be used for initial evaluation of image processing methods. The present work proposes a methodology for generating annotated 4D ASL MRA virtual phantoms, in different scenarios with different acquisition parameter settings. In each scenario, the phantoms are generated using real cerebrovascular geometries of healthy volunteers, where blood flow is simulated according to a mathematical model specifically designed to describe the signal observed in 4D ASL MRA images. Realistic noise is added using an homomorphic approach, designed to replicate noise characteristic of multi-coil acquisitions. In order to exemplify the utility of the phantoms, they are used to evaluate the accuracy of a method to estimate blood flow parameter values, such as relative blood volume and transit time, in different scenarios. The estimated values are then compared to its corresponding virtual ground-truth values. The accuracy of the results is ranked according to the average absolute error. The results of the experiments show that blood flow parameters can be more accurately estimated when blood is magnetically labeled for longer periods of time and when the datasets are acquired with higher temporal resolution. In summary, the present work describes a methodology to create annotated virtual phantoms, which represent a useful alternative for initial evaluation of medical image processing methods for 4D ASL MRA images