Image databases in medical applications

The number of medical images acquired yearly in hospitals increases all the time. These imaging data contain lots of information on the characteristics of anatomical structures and on their variations. This information can be utilized in numerous medical applications. In deformable model-based segmentation and registration methods, the information in the image databases can be used to give a priori information on the shape of the object studied and the gray-level values in the image, and on their variations. On the other hand, by studying the variations of the object of interest in different populations, the effects of, for example, aging, gender, and diseases on anatomical structures can be detected. In the work described in this Thesis, methods that utilize image databases in medical applications were studied. Methods were developed and compared for deformable model-based segmentation and registration. Model selection procedure, mean models, and combination of classifiers were studied for the construction of a good a priori model. Statistical and probabilistic shape models were generated to constrain the deformations in segmentation and registration so that only the shapes typical to the object studied were accepted. In the shape analysis of the striatum, both volume and local shape changes were studied. The effects of aging and gender, and also the asymmetries were examined. The results proved that the segmentation and registration accuracy of deformable model-based methods can be improved by utilizing the information in image databases. The databases used were relatively small. Therefore, the statistical and probabilistic methods were not able to model all the population-specific variation. On the other hand, the simpler methods, the model selection procedure, mean models, and combination of classifiers, gave good results also with the small image databases. Two main applications were the reconstruction of 3-D geometry from incomplete data and the segmentation of heart ventricles and atria from short- and long-axis magnetic resonance images. In both applications, the methods studied provided promising results. The shape analysis of the striatum showed that the volume of the striatum decreases in aging. Also, the shape of the striatum changes locally. Asymmetries in the shape were found, too, but any gender-related local shape differences were not found.reviewe

Central and peripheral autonomic influences : analysis of cardio-pulmonary dynamics using novel wavelet statistical methods

The development and implementation of novel signal processing techniques, particularly with regard to applications in the clinical environment, is critical to bringing computer-aided diagnoses of disease to reality. One of the most confounding factors in the field of cardiac autonomic response (CAR) research is the influence of the coupling of respiratory oscillations with cardiac oscillations. This research had three objectives. The first was the assessment of central autonomic influence over heart rate oscillations when the pulmonary system is damaged. The second was to assess the link between peripheral and central autonomic control schema by evaluating the heart rate variability (HRV) of people who were able or unable to adapt to the use of integrated lenses for vision, specifically acconrrmodation, correction (adaptive and non-adaptive presbyopes). The third objective was the development of a wavelet-based toolset by which the first two objectives could be achieved. The first tool is a wavelet based entropy measure that quantifies the level of information by assessing not only the entropy levels, but also the distribution of the entropy across frequency bands. The second tool is a wavelet source separation (WayS) method used to separate the respiratory component from the cardiac component, thereby allowing for analysis of the dynamics of the cardiac signal without the confounding influence of the respiratory signal that occurs when the body is perturbed. With regard to hypothesis one, the entropy method was used to separate the COPD study populations with 93% classification accuracy at rest, and with 100% accuracy during exercise. Changes in COPD and control autonomic markers were evident after respiration is removed. Specifically, the LF/HF ratio slightly decreased on average from pre to post reconstruction for controls, increased on average for COPD. In healthy controls, respiration frequency is distributed across multiple bandwidths, causing large decreases in both LF and HF when removed. With respiration effect removed from COPD population, LE dominates autonomic response, indicating that the frequency is concentrated in the HF autonomic region. Decrease in variance of data set increases probability tat smaller changes can be detected in values. The theory set forth in hypothesis two was validated by the quantification of a correlation between peripheral and central autonomic influences, as evidenced by differences in oculomotor adaptability correlating with differences in HRV. Standard Deviation varies with grouping, not with age. Increasing controlled respiration frequencies resulted in adaptive presbyopes and controls displaying similar sympathetic responses, diverging from non-adaptive group. WayS reduced frequency content in ranges concurrent with breathing rate, indicating a robust analysis. The outcome of hypothesis three was the confirmation that wavelet statistical methods possess significant potential for applications in HRV. Entropy can be used in conjunction with cluster analysis to classify patient populations with high accuracy. Using the WayS analysis, the respiration effect can be removed from HRV data sets, providing new insights into autonomic alterations, both central and peripheral, in disease

Personalized Electromechanical Modeling of the Human Heart : Challenges and Opportunities for the Simulation of Pathophysiological Scenarios

Mathematische Modelle des menschlichen Herzens entwickeln sich zu einem Eckpfeiler der personalisierten Medizin. Sie sind ein nützliches Instrument und helfen klinischen Entscheidungsträgern die zugrundeliegenden Mechanismen von Herzkrankheiten zu erforschen und zu verstehen. Aufgrund der Komplexität des Herzens benötigen derartige Modelle allerdings eine detaillierte Beschreibung der physikalischen Prozesse, welche auf verschiedenen räumlichen und zeitlichen Skalen miteinander interagieren. Aus mathematischer Perspektive stellen vor allem die Entwicklung robuster numerischer Methoden für die Lösung des Modells in Raum und Zeit sowie die Identifizierung von Parametern aus patientenspezifischen Messungen eine Herausforderung dar. In dieser Arbeit wird ein detailliertes mathematisches Modell vorgestellt, welches ein vollgekoppeltes Multiskalenmodell des menschlichen Herzens beschreibt. Das Modell beinhaltet unter anderem die Ausbreitung des elektrischen Signals und die mechanische Verformung des Herzmuskels sowie eine Beschreibung des Herz-Kreislauf-Systems. Basierend auf dem neusten Stand der Technik wurden Modelle der Membrankinetik sowie der Entwicklung der aktiven Kraft zu einem einheitlichen Modell einer Herzmuskelzelle zusammengeführt. Dieses beschreibt die elektromechanische Kopplung in Herzmuskelzellen der Vorhöfe und der Herzkammern basierend auf der Physiologie im Menschen und wurde mit Hilfe von experimentellen Daten aus einzelnen Zellen neu parametrisiert. Um das elektromechanisch gekoppelte Modell des menschlichen Herzens lösen zu können, wurde ein gestaffeltes Lösungsverfahren entwickelt, welches auf bereits existierenden Softwarelösungen der Elektrophysiologie und Mechanik aufbaut. Das neue Modell wurde verwendet, um den Einfluss elektromechanischer Rückkopplungseffekte auf das Herz im Sinusrhythmus zu untersuchen. Die Simulationsergebnisse zeigten, dass elektromechanische Rückkopplungseffekte auf zellulärer Ebene einen wesentlichen Einfluss auf das mechanische Verhalten des Herzens haben. Dahingegen hatte die Verformung des Herzens nur einen geringen Einfluss auf den Diffusionskoeffizienten des elektrischen Signals. Um die verschiedenen Komponenten der Simulationssoftware zu verifizieren, wurden spezielle Probleme definiert, welche die wichtigsten Aspekte der Elektrophysiologie und der Mechanik abdecken. Zusätzlich wurden diese Probleme dazu verwendet, den Einfluss von räumlicher und zeitlicher Diskretisierung auf die numerische Lösung zu bewerten. Die Ergebnisse zeigten, dass Raum- und Zeitdiskretisierung vor allem für das elektrophysiologische Problem die limitierenden Faktoren sind, während die Mechanik hauptsächlich anfällig für volumenversteifende Effekte ist. Weiterhin wurde das Modell verwendet, um zu untersuchen, wie sich eine Verteilung der Faserspannung auf den gesamten Herzmuskel auf die Funktion der linken Herzkammer auswirkt. Hierzu wurde zusätzlich eine Spannung in die Normalenrichtungen der Fasern einer idealisierten linken Herzkammer angewandt. Es zeigte sich, dass insbesondere eine Spannung senkrecht zu den Faserschichten zu einer physiologischeren Kontraktion der Kammer führte. Allerdings konnten diese Ergebnisse auf einem ganzen Herzen nicht vollständig bestätigt werden. In einem zweiten Projekt wurde mit Hilfe eines Modells der linken Herzkammer untersucht, wie sich das Rotationsmuster der Kammer unter Modifikation der lokalen elektromechanischen Eigenschaften verändert. Hierzu wurden in vivo Daten elektromechanischer Parameter von 30 Patienten mit Herzversagen und Linksschenkelblock in das Modell integriert, simuliert und ausgewertet. Die Ergebnisse konnten die klinisch aufgestellte Hypothese nicht bestätigen und es zeigte sich keine Korrelation zwischen den elektromechanischen Parametern und dem Rotationsverhalten. Die Auswirkungen von standardisierten Ablationsstrategien zur Behandlung von Vorhofflimmern in Bezug auf die kardiovaskuläre Leistung wurde in einem Modell des ganzen Herzens untersucht. Aufgrund der Narben im linken Vorhof wurde die elektrische Aktivierung und die Steifigkeit des Herzmuskels verändert. Dies führte zu einem reduzierten Auswurfvolumen, welches in direktem Zusammenhang mit dem inaktiven Gewebe steht. Abhängig von der Steifigkeit der Narben hat sich zusätzlich der Druck im linken Vorhof erhöht. Die linke Herzkammer war nur wenig beeinflusst. Zu guter Letzt wurden schrittweise pathologische Mechanismen in das Herzmodell integriert, welche in Zusammenhang mit Herzversagen stehen und in Patienten mit dilatativer Kardiomyopathie zu beobachten sind. Die Simulationen zeigten, dass vor allem zelluläre Veränderungen bezüglich der elektrophysiologischen Eigenschaften für die schlechte mechanische Aktivtät des Herzens verantwortlich sind. Weiterhin zeigte sich, dass strukturelle Veränderungen der Anatomie und die erhöhte Steifigkeit des Herzmuskels und die damit einhergehenden Anpassungen des Herz-Kreislauf-Systems nötig sind, um in vivo Messungen zu reproduzieren. In dieser Arbeit wurde eine Simulationsumgebung vorgestellt, welche die Berechnung der elektromechanischen Aktivität des Herzens und des Herz-Kreislauf-Systems ermöglicht. Die Simulationsumgebung wurde mit Hilfe von einfachen Beispielen verifiziert und unter Einbeziehung von Daten aus der Magnetresonanztomographie validiert. Zu guter Letzt wurde die Simulationsumgebung genutzt, um klinische Fragen zu beantworten, welche andernfalls im Dunkeln blieben

Characterising Shape Variation in the Human Right Ventricle Using Statistical Shape Analysis: Preliminary Outcomes and Potential for Predicting Hypertension in a Clinical Setting

Variations in the shape of the human right ventricle (RV) have previously been shown to be predictive of heart function and long term prognosis in Pulmonary Hypertension (PH), a deadly disease characterised by high blood pressure in the pulmonary arteries. The extent to which ventricular shape is also affected by non-pathological features such as sex, body mass index (BMI) and age is explored in this thesis. If fundamental differences in the shape of a structurally normal RV exist, these might also impact the success of a predictive model. This thesis evaluates the extent to which non-pathological features affect the shape of the RV and determines the best ways, in terms of procedure and analysis, to adapt the model to consistently predict PH. It also identifies areas where the statistical shape analysis procedure is robust, and considers the extent to which specific, non-pathological, characteristics impact the diagnostic potential of the statistical shape model. Finally, recommendations are made on next steps in the development of a classification procedure for PH. The dataset was composed of clinically-obtained, cardiovascular magnetic resonance images (CMR) from two independent sources; The University of Pittsburgh Medical Center and Newcastle University. Shape change is assessed using a 3D statistical shape analysis technique, which topologically maps heart meshes through an harmonic mapping approach to create a unique shape function for each shape. Proper Orthogonal Decomposition (POD) was applied to the complete set of shape functions in order to determine and rank a set of shape features (i.e. modes and corresponding coefficients from the decomposition). MRI scanning protocol produced the most significant difference in shape; a shape mode associated with detail at the RV apex and ventricular length from apex to base strongly correlated with the MRI sequence used to record each subject. Qualitatively, a protocol which skipped slices produced a shorter RV with less detail at the apex. Decomposition of sex, age and BMI also derives unique RV shape descriptors which correspond to anatomically meaningful features. The shape features are shown to be able to predict presence of PH. The predictive model can be improved by including BMI as a factor, but these improvements are mainly concentrated in identification of healthy subjects

Doctor of Philosophy

dissertationAtrial fibrillation (AF) is the leading cause of ischemic stroke and is the most commonly observed arrhythmia in clinical cardiology. Catheter ablation of AF, in which specific regions of cardiac anatomy associated with AF are intenionally injured to create scar tissue, has been honed over the last 15 years to become a relatively common and safe treatment option. However, the success of these anatomically driven ablation strategies, particularly in hearts that have been exposed to AF for extended periods, remains poor. AF induces changes in the electrical and structural properties of the cardiac tissue that further promotes the permanence of AF. In a process known as electroanatomical (EAM) mapping, clinicians record time signals known as electrograms (EGMs) from the heart and the locations of the recording sites to create geometric representations, or maps, of the electrophysiological properties of the heart. Analysis of the maps and the individual EGM morphologies can indicate regions of abnormal tissue, or substrates that facilitate arrhythmogenesis and AF perpetuation. Despite this progress, limitations in the control of devices currently used for EAM acquisition and reliance on suboptimal metrics of tissue viability appear to be hindering the potential of treatment guided by substrate mapping. In this research, we used computational models of cardiac excitation to evaluate param- eters of EAM that affect the performance of substrate mapping. These models, which have been validated with experimental and clinical studies, have yielded new insights into the limitations of current mapping systems, but more importantly, they guided us to develop new systems and metrics for robust substrate mapping. We report here on the progress in these simulation studies and on novel measurement approaches that have the potential to improve the robustness and precision of EAM in patients with arrhythmias. Appropriate detection of proarrhythmic substrates promises to improve ablation of AF beyond rudimentary destruction of anatomical targets to directed targeting of complicit tissues. Targeted treatment of AF sustaining tissues, based on the substrate mapping approaches described in this dissertation, has the potential to improve upon the efficacy of current AF treatment options

Development of instrumentation for autofluorescence spectroscopy and its application to tissue autofluorescence studies and biomedical research

Autofluorescence spectroscopy is a promising non-invasive label-free approach to characterise biological samples and has shown potential to report structural and biochemical changes occurring in tissue owing to pathological transformations. This thesis discusses the development of compact and portable single point fibre-optic probe-based instrumentation for time-resolved spectrofluorometry, utilising spectrally resolved time-correlated single photon counting (TCSPC) detection and white light reflectometry. Following characterisation and validation, two of these instruments were deployed in clinical settings and their potential to report structural and metabolic alterations in tissue associated with osteoarthritis and heart disease was investigated. Osteoarthritis is a chronic and progressive disease of the joint characterised by irreversible destruction of articular cartilage for which there is no effective treatment. Working with the Kennedy Institute of Rheumatology, we investigated the potential of time-resolved autofluorescence spectroscopy as a diagnostic tool for early detection and monitoring of the progression of osteoarthritis. Our studies in enzymatically degenerated porcine and murine cartilage, which serve as models for osteoarthritis, suggest that autofluorescence lifetime is sensitive to disruption of the two major extracellular matrix components, aggrecan and collagen. Preliminary autofluorescence lifetime data were also obtained from ex vivo human tissue presenting naturally occurring osteoarthritis. Overall, our studies indicate that autofluorescence lifetime may offer a non-invasive readout to monitor cartilage matrix integrity that could contribute to future diagnosis of early cartilage defects as well as monitoring the efficacy of therapeutic agents. This thesis also explored the potential of time-resolved autofluorescence spectroscopy and steady-state white-light reflectometry of tissue to report structural and metabolic changes associated with cardiac disease, both ex vivo and in vivo, in collaboration with clinical colleagues from the National Heart and Lung Institute. Using a Langendorff rat model, the autofluorescence signature of cardiac tissue was investigated following different insults to the heart. We were able to correlate and translate results obtained from ex vivo Langendorff data to an in vivo myocardial infarction model in rats, where we report structural and functional alterations in the infarcted and remote myocardium at different stages following infarction. This investigation stimulated the development of a clinically viable instrument to be used in open-chest surgical procedures in humans, of which progress to date is described. 4 The impact of time-resolved autofluorescence spectroscopy for label-free diagnosis of diseased would be significantly enhanced if the cost of the instrumentation could be reduced below what is achievable with commercial TCSPC-based technology. The last part of this thesis concerns the development of compact and portable instrumentation utilising low-cost FPGA-based circuitry that can be used with laser diodes and photon-counting photomultipliers. A comprehensive description of this instrument is presented together with data from its application to both fluorescence lifetime standards and biological tissue. The lower potential cost of this instrument could enhance the potential of autofluorescence lifetime metrology for commercial development and clinical deployment.Open Acces

Magnetic resonance imaging of the right ventricle in human pulmonary hypertension

Pulmonary Hypertension (PH) is a rare but devastating illness which results in progressive right ventricular (RV) failure and early death. RV function determines survival in all patients with PH but it is difficult to measure accurately using existing clinical techniques. The choice and design of the experiments in this thesis was driven by a desire to improve our understanding of the reasons for right, and left,ventricular dysfunction in this context. Cardiovascular magnetic resonance (CMR)imaging was utilized throughout as it allows the non-invasive, direct and accurate study of both ventricles; at rest and during stress. In Chapter 3, CMR imaging was used to identify an NT-proBNP threshold (1685 ng/l, sensitivity 100%, specificity 94%) for the non-invasive detection of RV systolic dysfunction in patients with PH. In Chapter 4, contrast-enhanced-CMR was utilized for the first time in PH patients and revealed previously unidentified areas of myocardial fibrosis within the RV insertion points and interventricular septum. The extent of these areas correlated inversely with RV ejection fraction (r = -0.762, p < 0.001). Septal contrast enhancement was particularly associated with bowing of the interventricular septum. Finally, in Chapter 5, dobutamine stress-CMR was used to determine the individual reasons for right and left ventricular stroke volume impairment during exercise in PH patients. ∆ RV stroke volume appeared limited by diminished contractile reserve as ∆ RVEF was lower in PH patients (27%) compared to controls (38%) and ∆ RVEF correlated with ∆ RV stroke volume (r = 0.94, p < 0.001). ∆ LV stroke volume appeared limited by impaired filling, probably due to reduced LV preload as RV stroke volume and LV end-diastolic volume remained closely related at rest (r = 0.821, p < 0.001) and stress (r = 0.693, p = 0.003)

Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms

Patienten mit Vorhofflimmern sind einem fünffach erhöhten Risiko für einen ischämischen Schlaganfall ausgesetzt. Eine frühzeitige Erkennung und Diagnose der Arrhythmie würde ein rechtzeitiges Eingreifen ermöglichen, um möglicherweise auftretende Begleiterkrankungen zu verhindern. Eine Vergrößerung des linken Vorhofs sowie fibrotisches Vorhofgewebe sind Risikomarker für Vorhofflimmern, da sie die notwendigen Voraussetzungen für die Aufrechterhaltung der chaotischen elektrischen Depolarisation im Vorhof erfüllen. Mithilfe von Techniken des maschinellen Lernens könnten Fibrose und eine Vergrößerung des linken Vorhofs basierend auf P Wellen des 12-Kanal Elektrokardiogramms im Sinusrhythmus automatisiert identifiziert werden. Dies könnte die Basis für eine nicht-invasive Risikostrat- ifizierung neu auftretender Vorhofflimmerepisoden bilden, um anfällige Patienten für ein präventives Screening auszuwählen. Zu diesem Zweck wurde untersucht, ob simulierte Vorhof-Elektrokardiogrammdaten, die dem klinischen Trainingssatz eines maschinellen Lernmodells hinzugefügt wurden, zu einer verbesserten Klassifizierung der oben genannten Krankheiten bei klinischen Daten beitra- gen könnten. Zwei virtuelle Kohorten, die durch anatomische und funktionelle Variabilität gekennzeichnet sind, wurden generiert und dienten als Grundlage für die Simulation großer P Wellen-Datensätze mit genau bestimmbaren Annotationen der zugrunde liegenden Patholo- gie. Auf diese Weise erfüllen die simulierten Daten die notwendigen Voraussetzungen für die Entwicklung eines Algorithmus für maschinelles Lernen, was sie von klinischen Daten unterscheidet, die normalerweise nicht in großer Zahl und in gleichmäßig verteilten Klassen vorliegen und deren Annotationen möglicherweise durch unzureichende Expertenannotierung beeinträchtigt sind. Für die Schätzung des Volumenanteils von linksatrialem fibrotischen Gewebe wurde ein merkmalsbasiertes neuronales Netz entwickelt. Im Vergleich zum Training des Modells mit nur klinischen Daten, führte das Training mit einem hybriden Datensatz zu einer Reduzierung des Fehlers von durchschnittlich 17,5 % fibrotischem Volumen auf 16,5 %, ausgewertet auf einem rein klinischen Testsatz. Ein Long Short-Term Memory Netzwerk, das für die Unterscheidung zwischen gesunden und P Wellen von vergrößerten linken Vorhöfen entwickelt wurde, lieferte eine Genauigkeit von 0,95 wenn es auf einem hybriden Datensatz trainiert wurde, von 0,91 wenn es nur auf klinischen Daten trainiert wurde, die alle mit 100 % Sicherheit annotiert wurden, und von 0,83 wenn es auf einem klinischen Datensatz trainiert wurde, der alle Signale unabhängig von der Sicherheit der Expertenannotation enthielt. In Anbetracht der Ergebnisse dieser Arbeit können Elektrokardiogrammdaten, die aus elektrophysiologischer Modellierung und Simulationen an virtuellen Patientenkohorten resul- tieren und relevante Variabilitätsaspekte abdecken, die mit realen Beobachtungen übereinstim- men, eine wertvolle Datenquelle zur Verbesserung der automatisierten Risikostratifizierung von Vorhofflimmern sein. Auf diese Weise kann den Nachteilen klinischer Datensätze für die Entwicklung von Modellen des maschinellen Lernens entgegengewirkt werden. Dies trägt letztendlich zu einer frühzeitigen Erkennung der Arrhythmie bei, was eine rechtzeitige Auswahl geeigneter Behandlungsstrategien ermöglicht und somit das Schlaganfallrisiko der betroffenen Patienten verringert

Quantitation in MRI : application to ageing and epilepsy

Multi-atlas propagation and label fusion techniques have recently been developed for segmenting the human brain into multiple anatomical regions. In this thesis, I investigate possible adaptations of these current state-of-the-art methods. The aim is to study ageing on the one hand, and on the other hand temporal lobe epilepsy as an example for a neurological disease. Overall effects are a confounding factor in such anatomical analyses. Intracranial volume (ICV) is often preferred to normalize for global effects as it allows to normalize for estimated maximum brain size and is hence independent of global brain volume loss, as seen in ageing and disease. I describe systematic differences in ICV measures obtained at 1.5T versus 3T, and present an automated method of measuring intracranial volume, Reverse MNI Brain Masking (RBM), based on tissue probability maps in MNI standard space. I show that this is comparable to manual measurements and robust against field strength differences. Correct and robust segmentation of target brains which show gross abnormalities, such as ventriculomegaly, is important for the study of ageing and disease. We achieved this with incorporating tissue classification information into the image registration process. The best results in elderly subjects, patients with TLE and healthy controls were achieved using a new approach using multi-atlas propagation with enhanced registration (MAPER). I then applied MAPER to the problem of automatically distinguishing patients with TLE with (TLE-HA) and without (TLE-N) hippocampal atrophy on MRI from controls, and determine the side of seizure onset. MAPER-derived structural volumes were used for a classification step consisting of selecting a set of discriminatory structures and applying support vector machine on the structural volumes as well as morphological similarity information such as volume difference obtained with spectral analysis. Acccuracies were 91-100 %, indicating that the method might be clinically useful. Finally, I used the methods developed in the previous chapters to investigate brain regional volume changes across the human lifespan in over 500 healthy subjects between 20 to 90 years of age, using data from three different scanners (2x 1.5T, 1x 3T), using the IXI database. We were able to confirm several known changes, indicating the veracity of the method. In addition, we describe the first multi-region, whole-brain database of normal ageing