Improved reliability of perfusion estimation in dynamic susceptibility contrast MRI by using the arterial input function from dynamic contrast enhanced MRI

The arterial input function (AIF) plays a crucial role in estimating quantitative perfusion properties from dynamic susceptibility contrast (DSC) MRI. An important issue, however, is that measuring the AIF in absolute contrast-agent concentrations is challenging, due to uncertainty in relation to the measured (Formula presented.) -weighted signal, signal depletion at high concentration, and partial-volume effects. A potential solution could be to derive the AIF from separately acquired dynamic contrast enhanced (DCE) MRI data. We aim to compare the AIF determined from DCE MRI with the AIF from DSC MRI, and estimated perfusion coefficients derived from DSC data using a DCE-driven AIF with perfusion coefficients determined using a DSC-based AIF. AIFs were manually selected in branches of the middle cerebral artery (MCA) in both DCE and DSC data in each patient. In addition, a semi-automatic AIF-selection algorithm was applied to the DSC data. The amplitude and full width at half-maximum of the AIFs were compared statistically using the Wilcoxon rank-sum test, applying a 0.05 significance level. Cerebral blood flow (CBF) was derived with different AIF approaches and compared further. The results showed that the AIFs extracted from DSC scans yielded highly variable peaks across arteries within the same patient. The semi-automatic DSC–AIF had significantly narrower width compared with the manual AIFs, and a significantly larger peak than the manual DSC–AIF. Additionally, the DCE-based AIF provided a more stable measurement of relative CBF and absolute CBF values estimated with DCE–AIFs that were compatible with previously reported values. In conclusion, DCE-based AIFs were reproduced significantly better across vessels, showed more realistic profiles, and delivered more stable and reasonable CBF measurements. The DCE–AIF can, therefore, be considered as an alternative AIF source for quantitative perfusion estimations in DSC MRI.</p

Infrared hyperspectral imaging for point-of-care wound assessment

Wound healing assessment and management are both important in ensuring a correct healing sequence. Most of these assessment techniques involve simple observation with the naked eye, which causes two main issues: the parameters assessed are highly subjective, and they rely upon the knowledge and experience of a trained medical professional. Any failure or incorrect management can result in further complications and even fatality, therefore quantitative wound assessment techniques are the next step towards a more accessible and reliable wound management strategy. Current research in this field is focused on utilising non-invasive imaging techniques, mainly within the visible and infrared (IR) range, to identify the biological and chemical changes during the wound healing process. Any abnormalities can then be identified earlier to aid in the correct diagnosis and treatment of the wound. Technologies that utilise concepts of non-contact imaging, such as optical imaging and spectroscopy can be used to obtain spatial and spectral maps of biomarkers, which provide valuable information on the wound (e.g., precursors to improper healing or delineate viable and necrotic tissue). This work extends this research further by investigating two different imaging modalities, Negative Contrast Imaging (NCI), along with Spatial Frequency Domain Imaging (SFDI) for the applications of point of care wound assessment. Intelligent data analysis algorithms, in the form of k-means clustering and principal component analysis were applied to spectral data, collected from wound biopsies as part of a previous study, highlighting the ability to diagnose wound healing status from the contrast of spectral information, which is not reliant upon a subjective clinical diagnosis. These methods provided the motivation for a larger cell culture trauma study, in which the NCI was utilised to obtain spectral reflectance maps across a 2.5- 3.5 μm wavelength region of both healthy and traumatised human epidermal fibroblasts, induced via chemical assays. Using the same intelligent analysis tools, along with pre-processing methods including spectral derivatives, the resulting clusters can be utilised as a diagnostic tool for the assessment of cellular health and were quantifiable metrics were defined to compare the different analysis methods Near infrared (NIR) methodologies were also investigated, with two areas of SFDI identified for further advancements. Current SFDI acquisition and optical property parameter recovery is performed via a pixel-wise process, generating large amounts of data and a high computational burden for parameter recovery. Data reduction, through the application of Compressive Sensing (CS) at both the image acquisition and data analysis stages provided up to a 90% reduction in data, whilst maintaining <10% error in recovered absorption and reduced scattering optical maps. This pixel-wise methodology also affects the forward modelling and inverse problem (imaging), based upon the diffusion approximation or Monte-Carlo methods due to their pixel-independent nature. NIRFAST, an existing FEM based NIR modelling tool, was adapted to produce pixel-dependent forward modelling for heterogenic samples, providing a mechanism towards a pixel dependent SFDI image modelling and parameter recovery system

Liver Fibrosis Surface Assessment Based on Non-Linear Optical Microscopy

Ph.DDOCTOR OF PHILOSOPH

Biomedical Signal Analysis of the Brain and Systemic Physiology

Near-infrared spectroscopy (NIRS) is a non-invasive and easy-to-use diagnostic technique that enables real-time tissue oxygenation measurements applied in various contexts and for different purposes. Continuous monitoring with NIRS of brain oxygenation, for example, in neonatal intensive care units (NICUs), is essential to prevent lifelong disabilities in newborns. Moreover, NIRS can be applied to observe brain activity associated with hemodynamic changes in blood flow due to neurovascular coupling. In the latter case, NIRS contributes to studying cognitive processes allowing to conduct experiments in natural and socially interactive contexts of everyday life. However, it is essential to measure systemic physiology and NIRS signals concurrently. The combination of brain and body signals enables to build sophisticated systems that, for example, reduce the false alarms that occur in NICUs. Furthermore, since fNIRS signals are influenced by systemic physiology, it is essential to understand how the latter impacts brain signals in functional studies. There is an interesting brain body coupling that has rarely been investigated yet. To take full advantage of these brain and body data, the aim of this thesis was to develop novel approaches to analyze these biosignals to extract the information and identify new patterns, to solve different research or clinical questions. For this the development of new methodological approaches and sophisticated data analysis is necessary, because often the identification of these patterns is challenging or not possible with traditional methods. In such cases, automatic machine learning (ML) techniques are beneficial. The first contribution of this work was to assess the known systemic physiology augmented (f)NIRS approach for clinical use and in everyday life. Based on physiological and NIRS signals of preterm infants, an ML-based classification system has been realized, able to reduce the false alarms in NICUs by providing a high sensitivity rate. In addition, the SPA-fNIRS approach was further applied in adults during a breathing task. The second contribution of this work was the advancement of the classical fNIRS hyperscanning method by adding systemic physiology measures. For this, new biosignal analyses in the time-frequency domain have been developed and tested in a simple nonverbal synchrony task between pairs of subjects. Furthermore, based on SPA-fNIRS hyperscanning data, another ML-based system was created, which is able distinguish familiar and unfamiliar pairs with high accuracy. This approach enables to determine the strength of social bonds in a wide range of social interaction contexts. In conclusion, we were the first group to perform a SPA-fNIRS hyperscanning study capturing changes in cerebral oxygenation and hemodynamics as well as systemic physiology in two subjects simultaneously. We applied new biosignals analysis methods enabling new insights into the study of social interactions. This work opens the door to many future inter-subjects fNIRS studies with the benefit of assessing the brain-to-brain, the brain-to-body, and body-to-body coupling between pairs of subjects

Nonlinear and factorization methods for the non-invasive investigation of the central nervous system

This thesis focuses on the functional study of the Central Nervous System (CNS) with non-invasive techniques. Two different aspects are investigated: nonlinear aspects of the cerebrovascular system, and the muscle synergies model for motor control strategies. The main objective is to propose novel protocols, post-processing procedures or indices to enhance the analysis of cerebrovascular system and human motion analysis with noninvasive devices or wearable sensors in clinics and rehabilitation. We investigated cerebrovascular system with Near-infrared Spectroscopy (NIRS), a technique measuring blood oxygenation at the level of microcirculation, whose modification reflects cerebrovascular response to neuronal activation. NIRS signal was analyzed with nonlinear methods, because some physiological systems, such as neurovascular coupling, are characterized by nonlinearity. We adopted Empirical Mode Decomposition (EMD) to decompose signal into a finite number of simple functions, called Intrinsic Mode Functions (IMF). For each IMF, we computed entropy-based features to characterize signal complexity and variability. Nonlinear features of the cerebrovascular response were employed to characterize two treatments. Firstly, we administered a psychotherapy called eye movement desensitization and reprocessing (EMDR) to two groups of patients. The first group performed therapy with eye movements, the second without. NIRS analysis with EMD and entropy-based features revealed a different cerebrovascular pattern between the two groups, that may indicate the efficacy of the psychotherapy when administered with eye movements. Secondly, we administered ozone autohemotherapy to two groups of subjects: a control group of healthy subjects and a group of patients suffering by multiple sclerosis (MS). We monitored the microcirculation with NIRS from oxygen-ozone injection up 1.5 hours after therapy, and 24 hours after therapy. We observed that, after 1.5 hours after the ozonetherapy, oxygenation levels improved in both groups, that may indicate that ozonetherapy reduced oxidative stress level in MS patients. Furthermore, we observed that, after ozonetherapy, autoregulation improved in both groups, and that the beneficial effects of ozonetherapy persisted up to 24 hours after the treatment in MS patients. Due to the complexity of musculoskeletal system, CNS adopts strategies to efficiently control the execution of motor tasks. A model of motor control are muscle synergies, defined as functional groups of muscles recruited by a unique central command. Human locomotion was the object of investigation, due to its importance for daily life and the cyclicity of the movement. Firstly, by exploiting features provided from statistical gait analysis, we investigated consistency of muscle synergies. We demonstrated that synergies are highly repeatable within-subjects, reinforcing the hypothesis of modular control in motor performance. Secondly, in locomotion, we distinguish principal from secondary activations of electromyography. Principal activations are necessary for the generation of the movement. Secondary activations generate supplement movements, for instance slight balance correction. We investigated the difference in the motor control strategies underlying muscle synergies of principal (PS) and secondary (SS) activations. We found that PS are constituted by a few modules with many muscles each, whereas SS are described by more modules than PS with one or two muscles each. Furthermore, amplitude of activation signals of PS is higher than SS. Finally, muscle synergies were adopted to investigate the efficacy of rehabilitation of stiffed-leg walking in lower back pain (LBP). We recruited a group of patients suffering from non-specific LBP stiffening the leg at initial contact. Muscle synergies during gait were extracted before and after rehabilitation. Our results showed that muscles recruitment and consistency of synergies improved after the treatment, showing that the rehabilitation may affect motor control strategies

Multimodal FTIR Microscopy-guided Acquisition and Interpretation of MALDI Mass Spectrometry Imaging Data

Multimodale klinische Bildgebung stellt eine der bedeutendsten Entwicklung der letzten Jahrzehnte dar. Neben der Kombination komplementärer in vivo Sensoren in beispielsweise PET-MRI oder SPECT-CT sind auch ex vivo Analyseverfahren, welche eine genauere Beschreibung der Probe ermöglichen, in den Bereich der (prä)klinischen Diagnostik vorgedrungen. Eine der vielversprechendsten Techniken in diesem Zusammenhang stellt die bildgebende Massenspektrometrie dar, welche die Verteilungsmuster hunderter Biomoleküle oder Pharmazeutika semi-quantitativ erfasst. Dabei kommt das Verfahren ohne die Verwendung von markierten Substanzen aus und erlaubt eine höhere räumliche und spektrale Auflösung im Vergleich zu in vivo Sensoren. Allerdings unterliegt die Technik auch einigen wesentlichen Einschränkungen, da die Datenakquisition besonders bei der Verwendung von ultrahochauflösenden FTICR-Detektoren sehr langsam erfolgt. Die niedrige Durchsatzleistung und damit verbundene unhandliche Datenmenge erschwert somit die Analyse größerer Patientenkohorten, wodurch ein Bedarf an multimodalen Lösungsansätzen besteht. Ein geeignetes Verfahren in dieser Hinsicht stellt die Schwingungsspektroskopie (bsp. Infrarotspektroskopie) dar, welche räumliche Details vergleichsweise schnell erfasst; dabei allerdings keine Rückschlüsse auf die Verteilung bestimmter chemischer Substanzen ermöglicht. Im Rahmen der vorliegenden Arbeit wurde ein MATLAB-gestütztes Verfahren zur multimodalen Akquirierung von Infrarotspektroskopie- und Massenspektrometrie-Daten entwickelt und bewertet. Dabei werden räumliche Strukturen und Zellpopulationen innerhalb von Geweben mittels FTIR-basierter Clusteranalyse segmentiert. Anschließend kann die chemische Zusammensetzung einzelner Segmente zielgerichtet akquiriert und verglichen werden. Das entwickelte Verfahren funktioniert dabei unabhängig von konventioneller histopathologischer Gewebeannotation. Ein wichtiger Faktor bei Mittelinfrarot- und Massenspektrometrie-Messungen auf Gewebe stellt die Zusammensetzung der verwendeten Objektträger-Beschichtung dar. Für die Bewertung der erhaltenen Spektren und der damit verbundenen Bildsegmentierung wurden deshalb Experimente auf Indiumzinnoxid, Silberzinnoxid und Gold durchgeführt und verglichen. Dabei konnte gezeigt werden, dass Infrarot- und Massenspektrometrie-Bilder von der gleichen Probe auf Gold mit hoher Qualität aufgenommen werden können. Weiterhin konnte gezeigt werden, dass durch einfache Infrarotsegmentierung eine Identifizierung relevanter morphologischer Gehirnstrukturen möglich ist. Die erzielte räumliche Präzision und Auflösung der Infrarot-Segmente stellt dabei einen deutlichen Mehrwert gegenüber der direkten Segmentierung von Massenspektrometriebildern dar. Darüber hinaus können Infrarotsegmente bereits vor der eigentlichen MS-Messung generiert werden. Nach erfolgter Methodenentwicklung und Validierung konnte diese auf verschiedene diagnostische Studien angewendet werden. In einem ersten Anwendungsbeispiel konnten in Mäuse xenotransplantierte humane Glioblastomzellen mit erhöhter Präzision visualisiert werden. Darüber hinaus wurde eine im korrespondierenden H&E-Bild unauffällige, den Tumor-umschließende Struktur identifiziert. Durch den erfolgreichen Transfer der Infrarotsegmente in das Koordinatensystem von nachfolgend gemessenen MS-Bildern, konnten spezifische Markersignaturen automatisch extrahiert werden. Im Zuge dessen konnte die Authentizität Tumorstruktur sowie der zweiten Tumor-assoziierten Struktur durch spezifische Massen bekräftigt werden. In einer weiteren Studie, wurde die entwickelte Methode für das automatische Screening von Markersignaturen in Niemann-Pick Typ C1 ähnlichen murinen Kleinhirnschnitten getestet. Dabei konnten regionsspezifische, im Gesamtdatensatz insignifikante Änderungen in der Lipidzusammensetzung automatisiert uns Annotations-unabhängig erfasst werden. In einer weiteren Infrarotspektroskopie-Studie an 89 kryokonservierten GIST Schnitten von 27 Patienten konnte eine schnelle und simultane Segmentierung aller Gewebeproben exemplarisch gezeigt werden. Dabei wurden farbkodierte Bilder aller Proben generiert, in denen gleiche Farben für eine spektrale Ähnlichkeit stehen. Durch den Abgleich der erhaltenen Farbcodes mit histopathologisch annotierten Folgeschnitten konnten zwei der fünf dargestellten Farbgruppen mit dem Auftreten von Tumorzellen assoziiert werden. Die anderen Gruppen repräsentierten Fibrosen, Nekrosen und weitere nicht-tumoröse Gewebeanteile. Abschließend wurde die Struktur-gerichtete Akquisition von ultrahochauflösenden FTICR-MS Bildern gezeigt, welche auf Basis von Mittelinfrarotbildern der identischen Gewebeprobe abgeleitet wurden. Indem die zeitaufwändige MS-Messung ausschließlich auf kleinere Strukturen von Interesse (wie beispielsweise die Körnerzell-Schicht der Cornu Ammonis) gerichtet wurde, konnte eine Zeit- und Datenersparnis von bis zu 97.8% gegenüber der vollständigen Messung erreicht werden. Damit ist ein großer Schritt hin zur Implementierung von ultrahochauflösender Massenspektrometrie im klinischen Umfeld erfolgt

Towards an Effective Imaging-Based Decision Support System for Skin Cancer

The usage of expert systems to aid in medical decisions has been employed since 1980s in distinct ap plications. With the high demands of medical care and limited human resources, these technologies are required more than ever. Skin cancer has been one of the pathologies with higher growth, which suf fers from lack of dermatology experts in most of the affected geographical areas. A permanent record of examination that can be further analyzed are medical imaging modalities. Most of these modalities were also assessed along with machine learning classification methods. It is the aim of this research to provide background information about skin cancer types, medical imaging modalities, data mining and machine learning methods, and their application on skin cancer imaging, as well as the disclosure of a proposal of a multi-imaging modality decision support system for skin cancer diagnosis and treatment assessment based in the most recent available technology. This is expected to be a reference for further implementation of imaging-based clinical support systems.info:eu-repo/semantics/publishedVersio