Entwicklung der multifrequenten Magnetresonanz-Elastographie zur Quantifizierung der biophysikalischen Eigenschaften von menschlichem Hirngewebe

Magnetic resonance elastography (MRE) is an emerging technique for the quantitative imaging of the biophysical properties of soft tissues in humans. Following its successful clinical application in detecting and characterizing liver fibrosis, the scientific community is investigating the use of viscoelasticity as a biomarker for neurological diseases. Clinical implementation requires a thorough understanding of brain tissue mechanics in conjunction with innovative techniques in new research areas. Therefore, three in vivo studies were conducted to analyze the inherent stiffness dispersion of brain tissue over a wide frequency range, to investigate real-time MRE in monitoring the viscoelastic response of brain tissue during the Valsalva maneuver (VM), and to study mechanical alterations of small lesions in multiple sclerosis (MS). Ultra-low frequency MRE with profile-based wave analysis was developed in 14 healthy subjects to determine large-scale brain stiffness, from pulsation-induced shear waves (1 Hz) to ultra-low frequencies (5 – 10 Hz) to the conventional range (20 – 40 Hz). Furthermore, multifrequency real-time MRE with a frame rate of 5.4 Hz was introduced to analyze stiffness and fluidity changes in response to respiratory challenges and cerebral autoregulation in 17 healthy subjects. 2D and 3D wavenumber-based stiffness reconstruction of the brain was established for conventional MRE in 12 MS patients. MS lesions were analyzed in terms of mechanical contrast with surrounding tissue in relation to white matter (WM) heterogeneity. We found superviscous properties of brain tissue at large scales with a strong stiffness dispersion and a relatively high model-based viscosity of η = 6.6 ± 0.3 Pa∙s. The brain’s viscoelasticity was affected by perfusion changes during VM, which was associated with an increase in brain stiffness of 6.7% ± 4.1% (p<.001), whereas fluidity decreased by -2.1 ± 1.4% (p<.001). In the diseased brain, the analysis of 147 MS lesions revealed 46% of lesions to be softer and 54% of lesions to be stiffer than surrounding tissue. However, due to the heterogeneity of WM stiffness, the results provide no significant evidence for a systematic pattern of mechanical variations in MS. Nevertheless, the results may explain, for the first time, the gap between static ex vivo and dynamic in vivo methods. Fluidity-induced dispersion provides rich information on the structure of tissue compartments. Moreover, viscoelasticity is affected by perfusion during cerebral autoregulation and thus may be sensitive to intracranial pressure modulation. The overall heterogeneity of stiffness obscures changes in MS lesions, and MS may not exhibit sclerosis as a mechanical signature. In summary, this thesis contributes to the field of human brain MRE by presenting new methods developed in studies conducted in new research areas using state-of-the-art technology. The results advance clinical applications and open exciting possibilities for future in vivo studies of human brain tissue.Die Magnetresonanz-Elastographie (MRE) ist ein Verfahren zur quantitativen Darstellung der viskoelastischen Eigenschaften von Weichgewebe. Nach der erfolgreichen klinischen Anwendung in der Leberdiagnostik wird versucht, Viskoelastizität als Biomarker für neurologische Krankheiten zu nutzen. Hierzu bedarf es einer genauen Analyse der Gewebemechanik und innovativen Anwendungsgebieten. Daher, wurden drei Studien durchgeführt, um die Steifigkeitsdispersion von Hirngewebe zu analysieren, das viskoelastische Verhalten während des Valsalva Manövers (VM) abzubilden, und die mechanischen Veränderungen in Läsionen bei Multipler Sklerose (MS) zu untersuchen. Niedrigfrequenz-MRE mit profilbasierter Wellenanalyse wurde in 14 Probanden entwickelt, um die Steifigkeit des Gesamthirns von pulsationsinduzierten Scherwellen (1 Hz) über ultraniedrige Frequenzen (5 – 10 Hz) bis hin zum konventionellen Bereich (20 – 40 Hz) zu bestimmen. Außerdem wurde die multifrequente Echtzeit-MRE mit einer Bildfrequenz von 6.4 Hz eingeführt, um die viskoelastische Antwort des Gehirns auf respiratorische Herausforderungen bei 17 gesunden Probanden zu untersuchen. Neue 2D- und 3D-Wellenzahl-basierte Steifigkeitsrekonstruktionen für das Gehirn wurden in 12 MS Patienten und konventioneller MRE entwickelt. Die Steifigkeitsänderungen in MS-Läsionen wurden mit umliegender weißer Substanz und dessen Heterogenität verglichen. Wir fanden superviskose Eigenschaften des Hirngewebes mit einer starken Dispersion und relativ hohen, modellbasierten Viskosität von η = 6,6 ± 0,3 Pa∙s. Die mechanischen Gewebeeigenschaften wurden durch Perfusionsänderungen während VM beeinflusst und die Hirnsteifigkeit erhöhte sich um 6,7 ± 4,1% (p<.001) wobei sich die Fluidität um -2,1 ± 1,4% (p<.001) verringerte. Die Analyse von 147 MS-Läsionen ergab, dass 54% bzw. 46% der Läsionen steifer bzw. weicher sind als das umgebende Gewebe. Aufgrund der Heterogenität der WM-Steifigkeit konnte jedoch kein Hinweis auf ein systematisches Muster mechanischer Veränderungen in MS-Läsionen gefunden werden. Die Ergebnisse können zum ersten Mal die Lücke zwischen statischen ex vivo und dynamischen in vivo Methoden erklären. Die fluiditätsinduzierte Dispersion liefert interessante Informationen über die zugrundeliegende Gewebestruktur. Darüber hinaus wird die Viskoelastizität durch die Perfusion während der zerebralen Autoregulation beeinflusst und kann daher empfindlich auf intrakranielle Druckschwankungen reagieren. Die allgemeine Heterogenität der Steifigkeit überschattet die Veränderungen in MS-Läsionen, und somit ist Sklerose möglicherweise kein prominentes Merkmal von MS. Zusammenfassend lässt sich festhalten, dass diese Dissertation einen Beitrag zum Gebiet der MRE leistet, indem neue Methoden und Anwendungen in neuen Forschungsgebieten mit modernster Technologie dargestellt werden. Hierdurch wird die klinische Translation gefördert und spannende Möglichkeiten für zukünftige Studien eröffnet

Analysis of Venous Blood Flow and Deformation in the Calf under External Compression

Deep vein thrombosis (DVT) is a common post-operative complication, and a serious threat to the patient’s general recovery. In recent years, there has been increasing awareness of the risk of DVT in healthy individuals after prolonged immobility, such as people taking long-period flights or sitting at a computer. Mechanical methods of DVT prophylaxis, such as compression stockings, have gained widespread acceptance, but the haemodynamic mechanism of their action is still not well understood. In this study, computational modelling approaches based on magnetic resonance (MR) images are used to (i) predict the deformation of calf and deep veins under external compression, (ii) determine blood flow and wall shear stress in the deep veins of the calf, and (iii) quantify the effect of external compression on flow and wall shear stress in the deep veins. As a first step, MR images of the calf obtained with and without external compression were analysed, which indicated different levels of compressibility for different calf muscle compartments. A 2D finite element model (FEM) with specifically tailored boundary conditions for different muscle components was developed to simulate the deformation of the calf under compression. The calf tissues were described by a linear elastic model. The simulation results showed a good qualitative agreement with the measurements in terms of deep vein deformation, but the area reduction predicted by the FEM was much larger than that obtained from the MR images. In an attempt to improve the 2D FEM, a hyperelastic material model was employed and a finite element based non-rigid registration algorithm was developed to calculate the bulk modulus of the calf tissues. Using subject-specific bulk modulus derived with this method together with a hyperelastic material model, the numerical results showed better quantitative agreement with MR measured deformations of deep veins and calf tissues. In order to understand the effect of external compression on flow in the deep veins, MR imaging and real-time flow mapping were performed on 10 healthy volunteers before and after compression. Computational fluid dynamics was then employed to calculate the haemodynamic wall shear stress (WSS), based on the measured changes in vessel geometry and flow waveforms. The overall results indicated that application of the compression stocking led to a reduction in both blood flow rate and cross sectional area of the peroneal veins in the calf, which resulted in an increase in WSS, but the individual effects were highly variable. Finally, a 3D fluid-structure interactions (FSI) model was developed for a segment of the calf with realistic geometry for the calf muscle and bones but idealised geometry for the deep vein. The hyperelastic material properties evaluated previously were employed to describe the solid behaviours. Some predictive ability of the FSI model was demonstrated, but further improvement and validation are still needed

Left Ventricle Myocardium Segmentation from 3D Cardiac MR Images using Combined Probabilistic Atlas and Graph Cut-based Approaches

Medical imaging modalities, including Computed Tomography (CT) Magnetic Resonance Imaging (MRI) and Ultrasound (US) are critical for the diagnosis and progress monitoring of many cardiac conditions, planning, visualization and delivery of therapy via minimally invasive intervention procedures, as well as for teaching, training and simulation applications. Image segmentation is a processing technique that allows the user to extract the necessary information from an image dataset, in the form of a surface model of the region of interest from the anatomy. A wide variety of segmentation techniques have been developed and implemented for cardiac MR images. Despite their complexity and performance, many of them are intended for specific image datasets or are too specific to be employed for segmenting classical clinical quality Magnetic Resonance (MR) images. Graph Cut based segmentation algorithms have been shown to work well in regards to medical image segmentation. In addition, they are computationally efficient, which scales well to real time applications. While the basic graph cuts algorithms use lower-order statistics, combining this segmentation approach with atlas-based methods may help improve segmentation accuracy at a lower computational cost. The proposed technique will be tested at each step during the development by assessing the segmentation results against the available ground truth segmentation. Several metrics will be used to quantify the performance of the proposed technique, including computational performance, segmentation accuracy and fidelity assessed via the Sørensen-Dice Coefficient (DSC), Mean Absolute Distance (MAD) and Hausdorff Distance (HD) metrics

A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth.

Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth

Dynamic Deformation and Mechanical Properties of Brain Tissue

Traumatic brain injury is an important medical problem affecting millions of people. Mathematical models of brain biomechanics are being developed to simulate the mechanics of brain injury and to design protective devices. However, because of a lack of quantitative data on brain-skull boundary conditions and deformations, the predictions of mathematical models remain uncertain. The objectives of this dissertation are to develop methods and obtain experimental data that will be used to parameterize and validate models of traumatic brain injury. To that end, this dissertation first addresses the brain-skull boundary conditions by measuring human brain motion using tagged magnetic resonance imaging. Magnetic resonance elastography was performed in the ferret brain to measure its mechanical properties in vivo. Brain tissue is not only heterogeneous, but may also be anisotropic. To characterize tissue anisotropy, an experimental procedure combining both shear testing and indentation was developed and applied to white matter and gray matter. These measurements of brain-skull interactions and mechanical properties of the brain will be valuable in the development and validation of finite element simulations of brain biomechanics

Investigation of blood flow in the superior mesenteric artery and its potential influence on atheroma and gut ischaemia.

Atherosclerosis is the underlying process in coronary heart disease leading to myocardial infarction, and in arterial damage leading to cerebrovascular accidents. It accounts for almost 50% of deaths in the western world. Atherosclerosis is characterised by the presence of fibro-lipid plaques (atheroma) within the vessel wall. Whilst the initiation and progression of atheroma are not fully understood, it is generally accepted that the time-varying haemodynamic wall shear stress (WSS) that the vessel wall is exposed to is important in determining the likelihood of development of an atherosclerotic plaque The superior mesenteric artery (SMA) is the major blood vessel feeding the small intestine; compared to other vessels of similar size, it is largely spared the effects of atherosclerosis

An Image Based Computational Fluid Dynamics Study of Mitral Valve. A novel Approach to Assess the Mitral Valve, from Physiology to Surgical Practice

Mitral valve disease is the second most frequent valve disease requiring surgery. The aim of our study was to develop through a computational fluid dynamics a method to study the mitral valve from Pathophysiology to mitral valve regurgitation undergone surgical repair. As a first stage, we performed computational fluid dynamic (CFD) simulations in the left ventricle, left atrium and aortic root, with a resistive immersed method, a turbulence model, and with imposed systolic wall motion reconstructed from Cine-Magnetic Resonance Imaging (MRI) images, which allowed us to segment also the mitral valve. For the regurgitant scenarios we considered an increase of the heart rate and a dilation of the left ventricle. Our results highlighted that mitral varve regurgitation (MVR) gave rise to regurgitant jets through the mitral orifice impinging against the atrial walls and scratching against the mitral valve leading to high values of wall shear stresses (WSSs) with respect to the healthy case. CFD with prescribed wall motion and immersed mitral valve revealed to be an effective tool to quantitatively describe hemodynamics in case of MVR and to compare different regurgitant scenarios. Our findings highlighted in particular the presence of transition to turbulence in the atrium and allowed us to quantify some important cardiac indices such as cardiac output and WSS. After validation of the model, we performed a computational image-based study of blood dynamics in the whole left heart, both in a healthy subject and in a patient with MVR. We elaborated dynamic cine-MRI images with the aim of reconstructing the geometry and the corresponding motion of left ventricle, left atrium, mitral and aortic valves, and aortic root of the subjects. This allowed us to prescribe such motion to computational blood dynamics simulations where, for the first time, the whole left heart motion of the subject is considered, allowing us to obtain reliable subject-specific information. The final aim was to investigate and compare between the subjects the occurrence of turbulence and the risk of hemolysis and of thrombi formation. In particular, we modeled blood with the Navier-Stokes equations in the Arbitrary Lagrangian-Eulerian framework, with a Large Eddy Simulation model to describe the transition to turbulence and a resistive method to manage the valve dynamics, and we used a Finite Elements discretization implemented in an in-house code for the numerical solution. Our results highlighted that the regurgitant jet in the MVR case gave rise to a large amount of transition to turbulence in the left atrium resulting in a higher risk of formation of hemolysis. Moreover, MVR promoted a more complete washout of stagnant flows in the left atrium during the systolic phase and in the left ventricle apex during diastole. This work put the base for a new clinical approach to the mitral valve such as the analysis and the comparison of different surgical techniques of the diseased mitral valve undergone a surgical repair

Magnetic resonance imaging and navigation of ferromagnetic thermoseeds to deliver thermal ablation therapy

Minimally invasive therapies aim to deliver effective treatment whilst reducing off-target burden, limiting side effects, and shortening patient recovery times. Remote navigation of untethered devices is one method that can be used to deliver targeted treatment to deep and otherwise inaccessible locations within the body. Minimally invasive image-guided ablation (MINIMA) is a novel thermal ablation therapy for the treatment of solid tumours, whereby an untethered ferromagnetic thermoseed is navigated through tissue to a target site within the body, using the magnetic field gradients generated by a magnetic resonance imaging (MRI) system. Once at the tumour, the thermoseed is heated remotely using an alternating magnetic field, to induce cell death in the surrounding cancer tissue. The thermoseed is then navigated through the tumour, heating at pre-defined locations until the entire volume has been ablated. The aim of this PhD project is to develop MINIMA through a series of proof-of-concept studies and to assess the efficacy of the three key project components: imaging, navigation, and heating. First, an MR imaging sequence was implemented to track the thermoseeds during navigation and subsequently assessed for precision and accuracy. Secondly, movement of the thermoseeds through a viscous fluid was characterised, by measuring the effect of different navigation parameters. This was followed by navigation experiments performed in ex vivo tissue. To assess thermoseed heating, a series of in vitro experiments were conducted in air, water, and ex vivo liver tissue, before moving onto in vivo experiments in the rat brain and a murine subcutaneous tumour model. These final experiments allowed the extent of cell death induced by thermoseed heating to be determined, in both healthy and diseased tissue respectively