New Advances in Susceptibility Weighted MRI to Determine Physiological Parameters

Die Magnetresonanztomographie bietet die Möglichkeit der Bestimmung des Blutoxygenierungsgrades kleiner venöser Gefäße und damit lokaler Hirnareale mit Hilfe einer Multiecho-Gradientenecho-Sequenz. Mit dieser Sequenz kann der Signalzerfall in einem Voxel, welches von einer einzelnen Vene bzw. von Blutkapillaren durchzogen ist, bestimmt werden. Der Signalzerfall ist charakteristisch für die von der Vene oder den Kapillaren erzeugten Feldinhomogenitäten, so dass sich Aussagen über den Blutoxygenierungsgrad und Blutvolumenanteil treffen lassen. Durch Fitten simulierter Signalverläufe an gemessene Phantom- und Probandendaten konnte gezeigt werden, dass es mit der hier vorgestellten Methode möglich ist, den venösen Blutoxygenierungsgrad zu quantifizieren. Weiterhin konnte eine durch gezielte Modulation des zerebralen Blutflusses hervorgerufene Änderung der Blutoxygenierung in vivo nachgewiesen werden. Die Erweiterung des Modells eines einzelnen Gefäßes auf ein Gefäßnetzwerk diente als Grundlage zur theoretischen Beschreibung der Blutkapillaren, die das Hirngewebe durchziehen und mit Sauerstoff versorgen. Dieses Netzwerkmodel konnte in Phantomexperimenten verifiziert werden. Dagegen zeigte sich bei einer Probandenmessung, dass es nicht möglich ist einzig anhand des gemessenen Signalverlaufs valide Werte für die Blutoxygenierung und den Blutvolumenanteil eindeutig zu bestimmen. Die hohe Korrelation zwischen beiden Parametern bewirkt, dass mehrere Paare von Oxygenierungs- und Volumenwerten passende Signalkurven liefern. Eine unabhängige Quantifizierung oder Abschätzung des venösen Blutvolumens kann hier helfen eindeutige Oxygenierungswerte zu erhalten. Im Rahmen der vorliegenden Dissertation konnte das Signalverhalten von suszeptibilitätssensitiven Messungen in der Magnetresonanztomographie genauer untersucht und eine Methode zur nicht-invasiven Bestimmung der venösen Blutoxygenierung an einzelnen Gefäßen entwickelt werden. Erste in vivo Ergebnisse des Gefäßnetzwerkes verdeutlichen, dass für eine genaue Quantifizierung der Blutoxygenierung weitere Parameter, wie das Blutvolumen, unabhängig bestimmt werden müssen. Dennoch ist es möglich, die Methode am einzelnen Blutgefäß zur besseren Charakterisierung von Pathologien sowie physiologischen Änderungen, z.B. bei der funktionellen Magnetresonanztomographie, einzusetzen.Magnetic resonance imaging allows to determine the blood oxygenation level of small venous vessels or the blood capillary network by evaluating the magnetic resonance signal acquired with multi-echo gradient-echo sequences. The signal formation of a voxel traversed by a vein or interspersed with capillaries shows a characteristic decay or modulation as a function of time from which the blood oxygenation and blood volume fraction can be derived. It could be demonstrated in phantom measurements that the signal of a single vessel traversed voxel correctly matched the calculations of numerical signal simulation. By fitting the signal simulation to in vivo measurements of cerebral venous vessels, vessel size and venous blood oxygenation was determined quantitatively. Furthermore, it was possible to detect and to quantify a physiologically induced change in cerebral venous blood oxygenation. To describe the signal of the blood capillary network in normal brain matter, an extension of the single vessel model to a vessel network was applied. This network model was also validated in phantom experiments. As a result of these investigations it was found that the two parameters describing the network, the blood volume fraction and blood oxygenation level, are correlated to each other and can not be separated without additional information by simply fitting the signal simulation to the measurement. This finding was of special importance in the initial in vivo measurements conducted in the present work. Where, independent blood volume determination may help to further validate the quantified blood oxygenation level. In the present work a non-invasive method was developed to quantify cerebral blood oxygenation levels in single veins. This was possible by investigating the signal evolution of susceptibility sensitive magnetic resonance imaging. The initial result of the vessel network signal reveals, that for obtaining a valid blood oxygenation level, the volume fraction has to be further determined by an independent measurement. Nevertheless, is has been demonstrated that the quantification of the blood oxygenation level in single venous vessels is possible and can be applied in clinical diagnosis for better characterization of cerebral pathologies or in physiological investigations, like in functional magnetic resonance imaging

From in situ to ex vivo: the effect of autolysis and fixation on quantitative MRI markers for myelin

Ex vivo histology remains the gold standard against which MRI biophysical models, e.g. the MR g-ratio which characterises the fraction of a fibre’s diameter that is myelinated, are evaluated. The MR g-ratio model requires a measure of myelin density, for which magnetization transfer saturation (MT) has been used as a biomarker. However, changes occurring post mortem, e.g. autolysis, temperature changes and fixation, significantly alter the MRI signal. Here we investigate how these changes impact MT. We found that MT decreased post mortem but greatlyincreased upon fixation. These effects are similar to reported changes of other established MRI myelin-markers

IL-23 (Interleukin-23)-producing conventional dendritic cells control the detrimental IL-17 (Interleukin-17) response in stroke

Background and Purpose—Inflammatory mechanisms can exacerbate ischemic tissue damage and worsen clinical outcome in patients with stroke. Both αβ and γδ T cells are established mediators of tissue damage in stroke, and the role of dendritic cells (DCs) in inducing the early events of T cell activation and differentiation in stroke is not well understood. Methods—In a murine model of experimental stroke, we defined the immune phenotype of infiltrating DC subsets based on flow cytometry of surface markers, the expression of ontogenetic markers, and cytokine levels. We used conditional DC depletion, bone marrow chimeric mice, and IL-23 (interleukin-23) receptor-deficient mice to further explore the functional role of DCs. Results—We show that the ischemic brain was rapidly infiltrated by IRF4+/CD172a+ conventional type 2 DCs and that conventional type 2 DCs were the most abundant subset in comparison with all other DC subsets. Twenty-four hours after ischemia onset, conventional type 2 DCs became the major source of IL-23, promoting neutrophil infiltration by induction of IL-17 (interleukin-17) in γδ T cells. Functionally, the depletion of CD11c+ cells or the genetic disruption of the IL-23 signaling abrogated both IL-17 production in γδ T cells and neutrophil infiltration. Interruption of the IL-23/ IL-17 cascade decreased infarct size and improved neurological outcome after stroke. Conclusions—Our results suggest a central role for interferon regulatory factor 4-positive IL-23–producing conventional DCs in the IL-17–dependent secondary tissue damage in stroke

Preclinical Models for Neuroblastoma: Establishing a Baseline for Treatment

Preclinical models of pediatric cancers are essential for testing new chemotherapeutic combinations for clinical trials. The most widely used genetic model for preclinical testing of neuroblastoma is the TH-MYCN mouse. This neuroblastoma-prone mouse recapitulates many of the features of human neuroblastoma. Limitations of this model include the low frequency of bone marrow metastasis, the lack of information on whether the gene expression patterns in this system parallels human neuroblastomas, the relatively slow rate of tumor formation and variability in tumor penetrance on different genetic backgrounds. As an alternative, preclinical studies are frequently performed using human cell lines xenografted into immunocompromised mice, either as flank implant or orthtotopically. Drawbacks of this system include the use of cell lines that have been in culture for years, the inappropriate microenvironment of the flank or difficult, time consuming surgery for orthotopic transplants and the absence of an intact immune system.Here we characterize and optimize both systems to increase their utility for preclinical studies. We show that TH-MYCN mice develop tumors in the paraspinal ganglia, but not in the adrenal, with cellular and gene expression patterns similar to human NB. In addition, we present a new ultrasound guided, minimally invasive orthotopic xenograft method. This injection technique is rapid, provides accurate targeting of the injected cells and leads to efficient engraftment. We also demonstrate that tumors can be detected, monitored and quantified prior to visualization using ultrasound, MRI and bioluminescence. Finally we develop and test a "standard of care" chemotherapy regimen. This protocol, which is based on current treatments for neuroblastoma, provides a baseline for comparison of new therapeutic agents.The studies suggest that use of both the TH-NMYC model of neuroblastoma and the orthotopic xenograft model provide the optimal combination for testing new chemotherapies for this devastating childhood cancer

Widespread diffusion changes differentiate Parkinson's disease and progressive supranuclear palsy

Background: Parkinson's disease (PD) and progressive supranuclear palsy – Richardson's syndrome (PSP-RS) are often represented by similar clinical symptoms, which may challenge diagnostic accuracy. The objective of this study was to investigate and compare regional cerebral diffusion properties in PD and PSP-RS subjects and evaluate the use of these metrics for an automatic classification framework. Material and methods: Diffusion-tensor MRI datasets from 52 PD and 21 PSP-RS subjects were employed for this study. Using an atlas-based approach, regional median values of mean diffusivity (MD), fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD) were measured and employed for feature selection using RELIEFF and subsequent classification using a support vector machine. Results: According to RELIEFF, the top 17 diffusion values consisting of deep gray matter structures, the brainstem, and frontal cortex were found to be especially informative for an automatic classification. A MANCOVA analysis performed on these diffusion values as dependent variables revealed that PSP-RS and PD subjects differ significantly (p < .001). Generally, PSP-RS subjects exhibit reduced FA, and increased MD, RD, and AD values in nearly all brain structures analyzed compared to PD subjects. The leave-one-out cross-validation of the support vector machine classifier revealed that the classifier can differentiate PD and PSP-RS subjects with an accuracy of 87.7%. More precisely, six PD subjects were wrongly classified as PSP-RS and three PSP-RS subjects were wrongly classified as PD. Conclusion: The results of this study demonstrate that PSP-RS subjects exhibit widespread and more severe diffusion alterations compared to PD patients, which appears valuable for an automatic computer-aided diagnosis approach. Keywords: Support vector machines, Diffusion-tensor magnetic resonance imaging, Computer-Assisted Image Analysis, Parkinson's disease, Progressive supranuclear pals

Effect of geometric distortion correction on thickness and volume measurements of cortical parcellations in 3D T1w gradient echo sequences

International audienceObjective Automated brain volumetric analysis based on high-resolution T1-weighted MRI datasets is a frequently used tool in neuroimaging for early detection, diagnosis, and monitoring of various neurological diseases. However, image distortions can corrupt and bias the analysis. The aim of this study was to explore the variability of brain volumetric analysis due to gradient distortions and to investigate the effect of distortion correction methods implemented on commercial scanners. Material and methods 36 healthy volunteers underwent brain imaging using a 3T magnetic resonance imaging (MRI) scanner, including a high-resolution 3D T1-weighted sequence. For all participants, each T1-weighted image was reconstructed directly on the vendor workstation with (DC) and without (nDC) distortion correction. For each participant's set of DC and nDC images, FreeSurfer was used for the determination of regional cortical thickness and volume. Results Overall, significant differences were found in 12 cortical ROIs comparing the volumes of the DC and nDC data and in 19 cortical ROIs comparing the thickness of the DC and nDC data. The most pronounced differences for cortical thickness were found in the precentral gyrus, the lateral occipital and postcentral ROI (2.69,-2.91% and-2.79%, respectively) while cortical volumes differed most prominently in the paracentral, the pericalcarine and lateral occipital ROI (5.52%,-5.40% and-5.11%, respectively)

Hypoxia-ischemia disrupts directed interactions within neonatal prefrontal-hippocampal networks.

Due to improved survival rates and outcome of human infants experiencing a hypoxic-ischemic episode, cognitive dysfunctions have become prominent. They might result from abnormal communication within prefrontal-hippocampal networks, as synchrony and directed interactions between the prefrontal cortex and hippocampus account for mnemonic and executive performance. Here, we elucidate the structural and functional impact of hypoxic-ischemic events on developing prefrontal-hippocampal networks in an immature rat model of injury. The magnitude of infarction, cell loss and astrogliosis revealed that an early hypoxic-ischemic episode had either a severe or a mild/moderate outcome. Without affecting the gross morphology, hypoxia-ischemia with mild/moderate outcome diminished prefrontal neuronal firing and gamma network entrainment. This dysfunction resulted from decreased coupling synchrony within prefrontal-hippocampal networks and disruption of hippocampal theta drive. Thus, early hypoxia-ischemia may alter the functional maturation of neuronal networks involved in cognitive processing by disturbing the communication between the neonatal prefrontal cortex and hippocampus

Localized prediction of tissue outcome in acute ischemic stroke patients using diffusion- and perfusion-weighted MRI datasets.

BackgroundAn accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences.Material and methodsMulti-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics.ResultsStatistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p ConclusionThe results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain

Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features.

INTRODUCTION:In recent years, numerous methods have been proposed to predict tissue outcome in acute stroke patients using machine learning methods incorporating multiparametric imaging data. Most methods include diffusion and perfusion parameters as image-based parameters but do not include any spatial information although these parameters are spatially dependent, e.g. different perfusion properties in white and gray brain matter. This study aims to investigate if including spatial features improves the accuracy of multi-parametric tissue outcome prediction. MATERIALS AND METHODS:Acute and follow-up multi-center MRI datasets of 99 patients were available for this study. Logistic regression, random forest, and XGBoost machine learning models were trained and tested using acute MR diffusion and perfusion features and known follow-up lesions. Different combinations of atlas coordinates and lesion probability maps were included as spatial information. The stroke lesion predictions were compared to the true tissue outcomes using the area under the receiver operating characteristic curve (ROC AUC) and the Dice metric. RESULTS:The statistical analysis revealed that including spatial features significantly improves the tissue outcome prediction. Overall, the XGBoost and random forest models performed best in every setting and achieved state-of-the-art results regarding both metrics with similar improvements achieved including Montreal Neurological Institute (MNI) reference space coordinates or voxel-wise lesion probabilities. CONCLUSION:Spatial features should be integrated to improve lesion outcome prediction using machine learning models