Fully automated deep learning-based localization and segmentation of the locus coeruleus in aging and Parkinson's disease using neuromelanin-sensitive MRI

Purpose: Development and performance measurement of a fully automated pipeline that localizes and segments the locus coeruleus in so-called neuromelanin-sensitive magnetic resonance imaging data for the derivation of quantitative biomarkers of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease. Methods: We propose a pipeline composed of several 3D-Unet-based convolutional neural networks for iterative multi-scale localization and multi-rater segmentation and non-deep learning-based components for automated biomarker extraction. We trained on the healthy aging cohort and did not carry out any adaption or fine-tuning prior to the application to Parkinson’s disease subjects. Results: The localization and segmentation pipeline demonstrated sufficient performance as measured by Euclidean distance (on average around 1.3mm on healthy aging subjects and 2.2mm in Parkinson’s disease subjects) and Dice similarity coefficient (overall around 71% on healthy aging subjects and 60% for subjects with Parkinson’s disease) as well as promising agreement with respect to contrast ratios in terms of intraclass correlation coefficient of ≥0.80 for healthy aging subjects compared to a manual segmentation procedure. Lower values (≥0.48) for Parkinson’s disease subjects indicate the need for further investigation and tests before the application to clinical samples. Conclusion: These promising results suggest the usability of the proposed algorithm for data of healthy aging subjects and pave the way for further investigations using this approach on different clinical datasets to validate its practical usability more conclusively

Hippocampal vascularization patterns: A high-resolution 7 Tesla time-of-flight magnetic resonance angiography study

Considerable evidence suggests a close relationship between vascular and degenerative pathology in the human hippocampus. Due to the intrinsic fragility of its vascular network, the hippocampus appears less able to cope with hypoperfusion and anoxia than other cortical areas. Although hippocampal blood supply is generally provided by the collateral branches of the posterior cerebral artery (PCA) and the anterior choroidal artery (AChA), different vascularization patterns have been detected postmortem. To date, a methodology that enables the classification of individual hippocampal vascularization patterns in vivo has not been established. In this study, using high-resolution 7 Tesla time-of-flight angiography data (0.3 mm isotropic resolution) in young adults, we classified individual variability in hippocampal vascularization patterns involved in medial temporal lobe blood supply in vivo. A strong concordance between our classification and previous autopsy findings was found, along with interesting anatomical observations, such as the variable contribution of the AChA to hippocampal supply, the relationships between hippocampal and PCA patterns, and the different distribution patterns of the right and left hemispheres. The approach presented here for determining hippocampal vascularization patterns in vivo may provide new insights into not only the vulnerability of the hippocampus to vascular and neurodegenerative diseases but also hippocampal vascular plasticity after exercise training

Visualization of Anatomic Tree Structures with Convolution Surfaces

We present a method for visualizing anatomic tree structures, such as vasculature and bronchial trees based on clinical CT- or MR data. The vessel skeleton as well as the diameter information per voxel serve as input. Our method adheres to these data, while producing smooth transitions at branchings and closed, rounded ends by means of convolution surfaces. We discuss the filter design with respect to irritating bulges, unwanted blending and the correct visualization of the vessel diameter. Similar to related work our method is based on the assumption of a circular cross-section of vasculature. In contrast to other authors we employ implicit surfaces to achieve high quality visualization. The method has been applied to a large variety of anatomic trees and produces good results

Automated segmentation of the locus coeruleus from neuromelanin-sensitive 3t MRI using deep convolutional neural networks

The locus coeruleus (LC) is a small brain structure in the brainstem that may play an important role in the pathogenesis of Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). The majority of studies to date have relied on using manual segmentation methods to segment the LC, which is time consuming and leads to substantial interindividual variability across raters. Automated segmentation approaches might be less error-prone leading to a higher consistency in Magnetic Resonance Imaging (MRI) contrast assessments of the LC across scans and studies. The objective of this study was to investigate whether a convolutional neural network (CNN)-based automated segmentation method allows for reliably delineating the LC in in vivo MR images. The obtained results indicate performance superior to the inter-rater agreement, i.e. approximately 70% Dice similarity coefficient (DSC)

Enhanced Cardio Vascular Image Analysis by Combined Representation of Results from Dynamic MRI and Anatomic CTA

The diagnosis support in the field of coronary artery disease (CAD) is very complex due to the numerous symptoms and performed studies leading to the final diagnosis. CTA and MRI are on their way to replace invasive catheter angiography. Thus, there is a need for sophisticated software tools that present the different analysis results, and correlate the anatomical and dynamic image information. We introduce a new software assistant for the combined result visualization of CTA and MR images, in which a dedicated concept for the structured presentation of original data, segmentation results, and individual findings is realized. Therefore, we define a comprehensive class hierarchy and assign suitable interaction functions. User guidance is coupled as closely as possible with available data, supporting a straightforward workflow design. The analysis results are extracted from two previously developed software assistants, providing coronary artery analysis and measurements, function analysis as well as late enhancement data investigation. As an extension we introduce a finding concept directly relating suspicious positions to the underlying data. An affine registration of CT and MR data in combination with the AHA 17-segment model enables the coupling of local findings to positions in all data sets. Furthermore, sophisticated visualization in 2D and 3D and interactive bull’s eye plots facilitate a correlation of coronary stenoses and physiology. The software has been evaluated on 20 patient data sets

Novel Methods for Parameter Based Analysis of Myocardial Tissue in MR-Images

The analysis of myocardial tissue with contrast-enhanced MR yields multiple parameters, which can be used to classify the examined tissue. Perfusion images are often distorted by motion, while late enhancement images are acquired with a different size and resolution. Therefore, it is common to reduce the analysis to a visual inspection, or to the examination of parameters related to the 17-segment-model proposed by the American Heart Association (AHA). As this simplification comes along with a considerable loss of information, our purpose is to provide methods for a more accurate analysis regarding topological and functional tissue features. In order to achieve this, we implemented registration methods for the motion correction of the perfusion sequence and the matching of the late enhancement information onto the perfusion image and vice versa. For the motion corrected perfusion sequence, vector images containing the voxel enhancement curves ’ semi-quantitative parameters are derived. The resulting vector images are combined with the late enhancement information and form the basis for the tissue examination. For the exploration of data we propose different modes: the inspection of the enhancement curves and parameter distribution in areas automatically segmented using the late enhancement information, the inspection of regions segmented in parameter space by user defined threshold intervals and the topological comparison of regions segmented with different settings. Results showed a more accurate detection of distorted regions in comparison to the AHA-model-based evaluation

Automatic transfer function specification for visual emphasis of coronary artery plaque

Cardiovascular imaging with current multislice spiral computed tomography (MSCT) technology enables a non-invasive evaluation of the coronary arteries. Contrast-enhanced MSCT angiography with high spatial resolution allows for a segmentation of the coronary artery tree. We present an automatically adapted transfer function (TF) specification to highlight pathologic changes of the vessel wall based on the segmentation result of the coronary artery tree. The TFs are combined with common visualization techniques, such as multiplanar reformation and direct volume rendering for the evaluation of coronary arteries in MSCT image data. The presented TF-based mapping of CT values in Hounsfield Units (HU) to color and opacity leads to a different color coding for different plaque types. To account for varying HU values of the vessel lumen caused by the contrast medium, the TFs are adapted to each dataset by local histogram analysis. We describe an informal evaluation with three board-certified radiologists which indicates that the represented visualizations guide the user's attention to pathologic changes of the vessel wall as well as provide an overview about spatial variations

Validität eines portablen 3-minütigen Psychomotorischen Vigilanztests (PVT) zur Erfassung von Müdigkeit bei Operatoren

Fragestellung: (712 von 900) Defizite in der psychomotorischen Vigilanz – insbesondere unvorhersehbare Einbrüche in der Aufmerksamkeit - sind typische Folgen verlängerter Wachphasen. Insbesondere bei Operatoren mit monotoner Überwachungstätigkeit im Schichtdienst ist das Risiko für Unfälle erhöht. Objektive Methoden zur Messung der kognitiven Leistungsfähigkeit sind jedoch in vielen Arbeitsumgebungen nur eingeschränkt einsetzbar, da diese meist zu zeitaufwendig sind (≥10 Minuten) und nur stationär anwendbar. Das DLR-Institut für Luft- und Raumfahrtmedizin hat deshalb einen Psychomotorischen Vigilanztest (PVT, 10 min) in seiner Dauer auf 3 Minuten verkürzt und auf einem portablen Handheld-Computer implementiert, um den Einsatz im Arbeitsalltag zu ermöglichen. Das Ziel der vorgestellten Studie war es, die Validität des 3-min PVT zu bestätigen. Methoden: (900 von 900) Im Schlaflabor wurden 47 Probanden (mittleres Alter 27 ± 5 Jahre, 21 Frauen, 26 Männer) an 12 aufeinander folgenden Tagen und Nächten untersucht. Müdigkeit wurde durch Schlafentzug erzeugt in 3 Bedingungen, die im Cross-over Design und durch je 2 Erholungsnächte getrennt dargeboten wurden: 1) 38 h wach, 2) 4 h Schlaf und 3) 4 h Schlaf nach moderatem Alkoholgenuss. Alkohol wurde an einem der Nachmittage konsumiert, so dass die Probanden im Mittel um 18 Uhr eine maximale Blutalkoholkonzentration von 0,7‰ ± 0,1‰ aufwiesen. Während der wachen Zeit wurden die Versuchspersonen in 3-stündigen Intervallen in ihrer kognitiven Leistungsfähigkeit getestet (insg. 63 Testeinheiten). Die Paralleltest-Reliabilität wurde im Vergleich zu einem 10-min PVT an einem Desktopcomputer bestimmt. Die Validität wurde durch Korrelation mit anwendungsorientierten Aufgaben aus dem Bereich Luftfahrt und Verkehr berechnet, welche Tests zur Hand-Auge-Koordination, zur räumlichen Orientierung und zum Spurwechsel- und halteverhalten sowie dem Reaktionsvermögen bei Autofahrten beinhalteten. Ergebnisse: (718 von 900) Die Leistung im 3-min PVT zeigte einen typischen zirkadianen Verlauf, wie er von der 10-min Version bekannt ist. Zusätzlich zeigte die Korrelation des 3-min PVT mit dem 10-min PVT eine gute Reliabilität unter Schlafentzugsbedingungen und bei verlängerter Wachdauer (r: 0,63 bis 0,87). Auch der prozentuale Anteil an Lapses (Reaktionszeit >500ms) pro Testsitzung war im 3-min PVT nicht erniedrigt. Die durch Alkohol verursachte, akute Leistungseinbuße betrug im 10-min PVT 30,1 ± 6,6 ms und 24,4 ± 5,1 ms im 3-min PVT (r = 0,89) und war somit geringer als das durch 26 Stunden Wachzeit hervorgerufene Leistungsdefizit (10-min PVT: 39,3 ± 3,6 ms; 3-min PVT: 47,3 ± 4,9 ms; r = 0,63). Die Korrelation mit den anwendungsbezogenen Tests unter Schlafentzugsbedingungen (r: 0,45 bis 0,7) und unter Alkoholeinfluss (r: 0,50 bis 0,75) erwies sich ebenfalls als gut. Schlussfolgerungen: (479 von 900) Der 3-min PVT detektierte die Leistungseinbußen, die durch die unterschiedlichen Schlafentzugsbedingungen verursacht wurden, zuverlässig und mit guter Validität. Obwohl die anwendungsbezogenen Testverfahren andersartige kognitive Domänen abbilden, konnte der 3-min PVT deren Leistungsdefizite durchaus nachvollziehen. Der 3-min PVT ist somit zur Überprüfung der kognitiven Leistung im Arbeitsalltag einsetzbar und kann dem Operator eine Hilfestellung geben, einen verbesserten Umgang mit Schlaf- und Wachzeiten zu erlernen