Diffuse Optical Imaging with Ultrasound Priors and Deep Learning

Diffuse Optical Imaging (DOI) techniques are an ever growing field of research as they are noninvasive, compact, cost-effective and can furnish functional information about human tissues. Among others, they include techniques such as Tomography, which solves an inverse reconstruction problem in a tissue volume, and Mapping which only seeks to find values on a tissue surface. Limitations in reliability and resolution, due to the ill-posedness of the underlying inverse problems, have hindered the clinical uptake of this medical imaging modality. Multimodal imaging and Deep Learning present themselves as two promising solutions to further research in DOI. In relation to the first idea, we implement and assess here a set of methods for SOLUS, a combined Ultrasound (US) and Diffuse Optical Tomography (DOT) probe for breast cancer diagnosis. An ad hoc morphological prior is extracted from US B-mode images and utilised for the regularisation of the inverse problem in DOT. Combination of the latter in reconstruction with a linearised forward model for DOT is assessed on specifically designed dual phantoms. The same reconstruction approach with the incorporation of a spectral model has been assessed on meat phantoms for reconstruction of functional properties. A simulation study with realistic digital phantoms is presented for an assessment of a non-linear model in reconstruction for the quantification of optical properties of breast lesions. A set of machine learning tools is presented for diagnosis breast lesions based on the reconstructed optical properties. A preliminary clinical study with the SOLUS probe is presented. Finally, a specifically designed deep learning architecture for diffusion is applied to mapping on the brain cortex or Diffuse Optical Cortical Mapping (DOCM). An assessment of its performances is presented on simulated and experimental data

Structural brain networks from diffusion MRI: methods and application

Structural brain networks can be constructed at a macroscopic scale using diffusion magnetic resonance imaging (dMRI) and whole-brain tractography. Under this approach, grey matter regions, such as Brodmann areas, form the nodes of a network and tractography is used to construct a set of white matter fibre tracts which form the connections. Graph-theoretic measures may then be used to characterise patterns of connectivity. In this study, we measured the test-retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions. High resolution T1-weighted brains were parcellated into regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, constraints on anatomical plausibility and three alternative network weightings. Test-retest performance was found to improve when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography, rather than deterministic. In terms of network weighting, a measure of streamline density produced better test-retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is most representative of the underlying axonal connections. These findings were then used to inform network construction for two further cohorts: a casecontrol analysis of 30 patients with amyotrophic lateral sclerosis (ALS) compared with 30 age-matched healthy controls; and a cross-sectional analysis of 80 healthy volunteers aged 25– 64 years. In both cases, networks were constructed using a weighting reflecting tract-averaged fractional anisotropy (FA). A mass-univariate statistical technique called network-based statistics, identified an impaired motor-frontal-subcortical subnetwork (10 nodes and 12 bidirectional connections), consistent with upper motor neuron pathology, in the ALS group compared with the controls. Reduced FA for three of the impaired network connections, which involved fibres of the cortico-spinal tract, were significantly correlated with the rate of disease progression. Cross-sectional analysis of the 80 healthy volunteers was intended to provide supporting evidence for the widely reported age-related decline in white matter integrity. However, no meaningful relationships were found between increasing age and impaired connectivity based on global, lobar and nodal network properties – findings which were confirmed with a conventional voxel-based analysis of the dMRI data. In conclusion, whilst current acquisition protocols and methods can produce networks capable of characterising the genuine between-subject differences in connectivity, it is challenging to measure subtle white matter changes, for example, due to normal ageing. We conclude that future work should be undertaken to address these concerns

Computational Characterization of Nonwoven Fibrous Materials: Transport and Wetting Properties

Nonwoven fibrous materials represent a platform of flexible material substrates. Nonwovens are widely used in the production of napkins, paper, filters, wound covers and face masks. In addition, for many applications, nonwoven materials interact with fluids. For example, in filtration applications, nonwoven materials are used to clean fluids containing solid particles or emulsified droplets. The filtration performance is affected not only by the geometrical arrangement of fibers in non-woven materials but also wettability of fibers. Understanding the transport properties of nonwoven materials and interactions between the dispersed droplets and solid substrate is crucial for the design and optimization of filter media. The present work is focused on: (1) obtaining pore space information from 3D structure in nonwoven media and 2) predicting the liquid transport properties in fibrous materials, including permeability and tortuosity (3) investigating droplet morphology on fibers. Chapters 1-3 provide the basis of fiber-liquid interactions and introduce the lattice Boltzmann method (LBM). Chapter 4 deals with characterization of microstructures generated from 3D reconstructed plywood and random oriented fibrous media. An algorithm based on watershed segmentation is utilized to extract pore network information including: pore diameter, throat diameter and connectivity. The effect of fiber overlapping arrangements, fiber radius and porosity on the pore space morphology was explored by statistical pore-network analysis. A thorough analysis of the correlation between effective geometrical properties and mean pore size, demonstrated that randomness on microscopic level can have a significant effect on the macroscopic properties of the fibrous media. In Chapter 5, simulations on pore-scale single phase fluid flow through fibrous media using the lattice Boltzmann method were performed. From the simulated flow field, permeability and tortuosity of nonwoven fibrous materials can be evaluated over a wide range of porosity 0.1 \u3c φ \u3c 0.9. The validity of Darcy’s law which describes the flow behavior through a porous medium was confirmed in the studied porosity regime. The simulation results were used to test the accuracy of semi-empirical scaling relations, that enabled predictions in trans-plane permeability and tortuosity based on porosity and specific surface area. Chapter 6 deals with the wetting and capillarity effects of droplets deposited on a single fiber. A multicomponent pseudopotential lattice Boltzmann model was applied to study the interface dynamics of droplets and wetting/dewetting behavior. By adopting different initial droplet configurations, we studied the stability of barrel-shaped and clam-shell droplets on a single fiber for contact angles ranging from 10° to 68°. The simulated barrel drop profile was validated with experimental results. The morphology diagram established from simulations showed that both barrel and clam-shell configurations are stable in coexistence. Dr. Ulf Schiller introduced me to the LBM, and guided my research described in Chapter 3-5. These chapters are based on publications [1, 2, 3], but significantly modified to include additional materials that has never been published. Chapter 6 has been developed to explain recent experimental results obtained in Dr. Kornev’s group. The developed simulation protocol revealed new physics related to the classical problem of fiber-drop interactions and a new diagram of morphological transitions of droplets on fibers was determined. The numerical simulations and data analysis were carried out on Palmetto high-performance computing (HPC) cluster

Physics of Brain Folding

The human brain is characterized by its folded structure, being the most folded brain among all primates. The process by which these folds emerge, called gyrogenesis, is still not fully understood. The brain is divided into an outer region, called gray matter, which grows at a faster rate than the inner region, called white matter. It is hypothesized that this imbalance in growth -- and the mechanical stress thereby generated -- drives gyrogenesis, which is the focus of this thesis. Finite element simulations are performed where the brain is modeled as a non-linear elastic and growth is introduced via a multiplicative decomposition. A small section of the brain, represented by a rectangular slab, is analyzed. This slab is divided into a thin hard upper layer mimicking the gray matter, and a soft substrate, mimicking the white matter. The top layer is then grown tangentially, while the underlying substrate does not grow. JuFold, the software developed to perform these simulations, is introduced, and its design is explained. An overview of its capabilities, and examples of simulation possibilities are shown. Additionally, one patent-leading application of JuFold in the realm of material science showcases its flexibility. Simulations are first performed by minimizing the elastic energy, corresponding to the slow growth regime. Systems with homogeneous cortices are studied, where growth initially compresses, and then buckles the cortical region, which generates wavy patterns with wavelength proportional to cortical thickness. After buckling, the sulcal regions (i.e. the valleys of the system) are thinner than the gyral regions (i.e. the hills). Introducing thickness inhomogeneities along the cortex lead to new and localized configurations, which are strongly dependent not only on the thickness of the region, but also on its gradient. Furthermore, cortical landmarks appear sequentially, consistent with the hierarchical folding observed during gestation. A linear stability theory is developed based on thin plate theory and is compared with homogeneous and inhomogeneous systems. Next, we turn to more physically stringent dynamic simulations. For slow growth rate and time-constant thickness, the results obtained through energy minimization are recovered, justifying previous literature. For faster growth, an overshoot of the wavenumber and a broad wavenumber spectrum are observed immediately after buckling. After a relaxation period, where the average wavenumber decreases and the wavenumber spectrum narrows, it is observed that the system stabilizes into a finite spectrum, whose average wavelength is smaller than that expected from energy minimization arguments. Cortical inhomogeneities are further explored in this new regime. Systems with inhomogeneous cortical thickness are revisited, with effects similar to the homogeneous cortex (i.e., results are consistent between the slow growth and the quasistatic regimes, and overshoot is observed in the fast growth regimes). Systems with inhomogeneous cortical growth are simulated, with this new type of inhomogeneity inducing fissuration and localized folding. The interplay between these two inhomogeneities is studied, and their interaction is shown to be nonlinear, with each inhomogeneity type inhibiting the folding effects of the other. That is, the folding profile of each individual region emerges as a result of the local inhomogeneity, and the system does not display an intermediate behavior. Finally, these results are compared with an extended linear stability theory. Taken together, our simulations and analytical theory expose new phenomena predicted by an incremented buckling hypothesis for folding and show a series of new avenues which could give rise to the important cortical features in the mammalian brain, especially those related to higher-order folding

Structured deep learning with applications in astrophysics

This work explores structured deep learning algorithms through geometric, temporal, and other inductive biases, as well as their applications in astrophysics. The first part of the thesis first contributes a self-supervised algorithm for time-series particle filter models, effective when data is noisy or underlying dynamics are unknown. This method performs exact inference while allowing integration of expert scientific knowledge. Next, geometric algebra-based neural networks are introduced for modeling dynamical systems, successfully learning transformation laws in areas like rigid body dynamics and fluid mechanics simulations. Third, Clifford Group-Equivariant Neural Networks are proposed for group-equivariant representation learning. Their general framework is applicable to problems in various dimensions and symmetry groups, including the Lorentz group used in relativistic astrophysics. Finally, Rolling Diffusion Models are presented for learning generative models of time-series data, such as video or scientific simulations, showing improved performance with complex temporal dynamics.In the second part, we introduce a deep learning-backed algorithm for hunting transient radio sources autonomously. It processes imaged radio interferometric data from LOFAR's AARTFAAC system to produce catalogs of candidate transients. This addresses a methodological gap for low-frequency, high-noise, and high-dimensionality data. Finally, normalizing flows are used to infer gravitational wave parameters from LIGO and Virgo detector data

Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems

Cosmic Microwave Background and Large Scale Structure: Cross-Correlation as seen from Herschel and Planck satellites

As well as providing us with a snapshot of the Universe at the time of recombination, the cosmic microwave backround (CMB) radiation carries a wealth of information about the later evolution of the Universe through the so-called CMB secondary anisotropies that originates from the interaction between CMB photons and the Large Scale Structure (LSS). This thesis deals with two of these effects: the CMB lensing and the kinematic Sunyaev-Zel'dovich (kSZ). In particular, we present the first cross-correlation analysis between the CMB lensing maps reconstructed by Planck team and the angular position of galaxies from the Herschel H-ATLAS survey, the highest redshift sample exploited for cross-correlation analysis to date. By splitting the galaxy catalog in two redshift bins, we also attempt a tomographic analysis of the signal and reconstruct the galaxy bias evolution over cosmic time. On the other hand, the kSZ effect measures the integrated free electron momentum up to high redshift, thus being sensitive to the cosmic flows and the reionization history. Here we study its capabilities in constraining theories of modified gravity

Phenotyping of multiple sclerosis patients through clinical and paraclinical resources

Background. Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease of the central nervous system. It is the most common neurological disease in young adults, affecting mostly women at the peak of their productive age. The most frequent disease phenotype is the relapsing remitting MS, where patients present relapses with a total or partial recovery of their symptoms. A group of these patients develops a secondary progressive course (SPMS), with an accumulation of MS-related disability independent of the relapse activity. As the identification of SPMS represents a significant diagnostic challenge, the identification of disease disability plays an important role in the monitoring of these patients. Gait and balance problems stand out among the most important symptoms of disability in MS patients. Due to the heterogeneity of the demyelinating lesions and their burden, several anatomic structures with a consequence in postural control may be affected by the MS. Recent advances in technology make possible a reliable evaluation of balance through objective measures. The implementation of static posturography could complement the classical neurological evaluation through the Expanded Disability Status Scale (EDSS) in the detection and characterization of balance impairment. Additionally, in the recent years, several new disease modifying treatment (DMTs) options have been developed for the treatment of MS. Fingolimod is a modulator of the S1P receptor that alters the trafficking of lymphocytes, decreasing the absolute count in blood. Several adverse effects have been described for this drug through its mechanism of action, such as cardiac or vascular alterations. Animal models have demonstrated a reduction of the fecal secretory immunoglobulin A (sIgA) levels through an impairment of the local immune response in the colon and a reduction of plasma cell precursors controlled by the S1P receptor. Human studies for this effect have not been so far. A similar effect in MS patients could lead to an impairment in the gut function and alter the commensal bacteria in the gut microbiome. Considering the limitations of the classic neurological evaluation and the still incomplete but growing understanding of MS, the investigation of clinical and paraclinical characteristics of MS patients and the effects of DMTs gains a remarkable importance. The main objective of this dissertation was to characterize people with MS through clinical outcome measures, as well as through the evaluation of fecal immunity after long-term treatment with fingolimod. Additionally, an assessment of balance parameters generated through static posturography was performed as well as physiological characteristics and alterations of postural control in this group. Methods. A cross-sectional evaluation of MS patients and healthy controls was performed. Patients with confirmed diagnosis of MS according to the 2010’s McDonald’s criteria and healthy controls were evaluated. An EDSS score was calculated for each patient according to the neurostatus scoring guidelines. Static posturography measures were performed with patients standing on a force platform in a standardized position equivalent to the Romberg test. The following balance outcomes were generated through an automatized software fur further evaluation: delineated area, average sway and average speed of sway. Both were obtained in the open and closed eyes conditions. The difference between the conditions was calculated. Fecal and salivary samples of a group of 25 patients with MS diagnosis and treatment with fingolimod or glatirameracetat for over twelve months were analyzed after a proper standardization. Through ELISA immunoasays free sIgA levels were calculated in the supernant. Required statistical analysis were performed. Results. 99 people with MS and 30 healthy controls participated in the static posturography evaluation. The MS had a worse performance than healthy controls in the three static posturography outcomes. Both groups had a worse performance in the closed eyes condition. However, the magnitude of the effect of vision was more significant in the MS patients as a significant interaction between vision and MS diagnosis was seen in the delineated area (p < 0.001) and average speed of sway (p = 0.001). There were moderate and significant correlations between most of the evaluated parameters and the EDSS and MSFC (r’s ranging from 0.207 to 0.537, p < 0.05). The highest correlations were seen for the delineated area and average speed in the closed eyes condition and their difference between the open and closed eyes evaluations. Especially these two parameters could differentiate between disability groups and healthy controls. Additionally, patients without postural instability documented through the Romberg test score of the EDSS assessment showed significantly worse outcomes in the delineated area [+1.97 cm2, 95%-CI (0.61–3.34); p = 0.002] than healthy controls. A similar trend was observed for the comparison between MS patients with normal cerebellar function EDSS-systems and healthy subjects. On the other hand, 15 patients with fingolimod and 10 with glatirameracetat participated in the measure of fecal sIgA. There was no significant difference between both groups at the evaluated time point. A similar pattern was seen in the salivary sIgA and serum immunoglobulins. In our studie, we evaluated the static posturography as a complement of the neurological assessment through the EDSS, which could characterize disease disability already at early stages of the diseases. The MS patients were more dependent of the visual feedback than HC to maintain postural control. In contrast, we could not confirm a decrease of fecal sIgA after a long-term treatment with fingolimod, although further longitudinal studies are needed for further analysis.Hintergrund. Die Multiple Sklerose (MS) ist eine chronisch entzündliche Autoimmunerkrankung des Zentralnervensystems. Sie ist die häufigste neurologische Erkrankung bei jungen Erwachsenen und betrifft vor allem Frauen auf dem Höhepunkt ihres produktiven Alters. Der häufigste Krankheitsverlauf ist die schubförmig remittierende MS (RRMS), bei der die Patienten Schübe mit einer vollständigen oder teilweisen Erholung ihrer Symptome aufweisen. Eine Gruppe dieser Patienten entwickelt einen sekundär progredienten Verlauf (SPMS), wobei eine schleichende MS-bedingte Behinderungsprogression unabhängig von der Schubsaktivität beschrieben wird. Da die Diagnose von SPMS eine bedeutende diagnostische Herausforderung darstellt, spielt die frühzeitige Erkennung der krankheitsbedingten Einschränkungen eine wichtige Rolle beim Monitoring von MS-Patienten. Gang- und Gleichgewichtsstörungen gehören zu den wichtigsten Beschwerden bei MS-Patienten. Aufgrund einer breiten Heterogenität der demyelinisierenden Läsionen können mehrere anatomische Strukturen mit Auswirkungen in der Haltungskontrolle betroffen sein. Jüngste technologische Fortschritte ermöglichen eine zuverlässige Bewertung des Gleichgewichts durch objektive Messungen. Die Anwendung der statischen Posturographie könnte die klassische neurologische Untersuchung durch die Expanded Disability Status Scale (EDSS) hinsichtlich einer Erkennung und Charakterisierung von Gleichgewichtsstörungen ergänzen. Darüber hinaus wurden in den letzten Jahren mehrere neue krankheitsmodifizierende therapeutische Optionen (DMTs) für die Behandlung von MS-Patienten entwickelt. Fingolimod ist ein Modulator des S1P-Rezeptors, der die Lymphozytendistribution modifiziert mit einer absoluten Verminderung der Anzahl im Blut. Für dieses Medikament wurden mehrere unerwünschte Wirkungen durch seinen Wirkmechanismus beschrieben, wie z.B. kardiale oder vaskuläre Veränderungen. In Tiermodellen wurde eine Verringerung der fäkalen sekretorischen Immunglobulin-A-Spiegel (sIgA) durch eine Beeinträchtigung der lokalen Immunantwort im Dickdarm und eine Verringerung der durch den S1P-Rezeptor kontrollierten Plasmazellvorläufer nachgewiesen. Humanuntersuchungen bezüglich dieses Effekts sind zu diesem Zeitpunkt noch nicht durchgeführt worden. Ein ähnlicher Effekt bei MS-Patienten könnte zu einer Beeinträchtigung der Darmfunktion führen und die kommensalen Bakterien der Darmflora verändern. In Anbetracht der Einschränkungen der klassischen neurologischen Unteruschung und des noch unvollständigen, jedoch wachsenden Verständnisses der MS gewinnt die klinische und paraklinische Phenotypisierung von MS-Patienten und der Wirkungen von DMTs eine bemerkenswerte Bedeutung. Das Hauptziel dieser Dissertation war die Phenotypisierung von Menschen mit MS durch klinische Ergebnismessungen sowie durch die Bewertung der fäkalen Immunität nach Langzeitbehandlung mit Fingolimod. Zusätzlich wurde eine Bemessung von Gleichgewichtsparametern, die durch statische Posturographie erzeugt wurden, sowie physiologische Merkmale und Veränderungen der posturalen Kontrolle in dieser Gruppe durchgeführt. Methoden. Es wurde eine Querschnittanalyse von MS-Patienten und gesunden Kontrollen durchgeführt. Patienten mit einer bestätigten MS-Diagnose nach den Revisionen der McDonald's-Kriterien von 2010 und gesunde Kontrollpersonen wurden untersucht. Für jeden Patienten wurde ein EDSS-Score nach den Neurostatus-Scoring-Leitlinien erhoben. Statische posturographische Messungen wurden durchgeführt, wobei die Patienten auf einer Kraftplattform in einer standardisierten Position standen, die dem Romberg-Test entsprach. Die folgenden Gleichgewichtsparameter wurden durch eine automatisierte Software zur weiteren Auswertung generiert: „delineated area“ (abgegrenzter Bereich), „average sway“ (durchschnittliches Schwanken) und „average speed of sway“ (durchschnittliche Schwenkgeschwindigkeit). Diese wurden mit dem Patienten mit offenen und geschlossenen Augen erhoben. Die Differenz zwischen beiden Bedingungen wurde dazu berechnet. Stuhl- und Speichelproben einer Gruppe von 25 Patienten mit MS-Diagnose nach Behandlung mit Fingolimod oder Glatirameracetat für über zwölf Monate wurden nach einer angemessenen Standardisierung analysiert. Mittels ELISA-Immunassays wurden die freien sIgA-Konzentrationen im Supernant berechnet. Die erforderliche statistische Analyse wurde durchgeführt. Ergebnisse. 99 Personen mit MS und 30 gesunde Kontrollen nahmen an der statischen posturographischen Auswertung teil. Die MS-Gruppe hatte in den drei statischen Posturographie-Ergebnissen eine schlechtere Leistung als gesunde Kontrollen. Beide Gruppen hatten eine schlechtere Leistung im Zustand mit geschlossenen Augen. Das Ausmaß der Auswirkung des Sehens war jedoch bei den MS-Patienten bedeutsamer, da eine signifikante Interaktion zwischen Sehen und MS-Diagnose im abgegrenzten Bereich (p < 0,001) und der durchschnittlichen Schwankungsgeschwindigkeit (p = 0,001) in einer ANCOVA-Analyse zu beobachten war. Es gab mäßige und signifikante Korrelationen zwischen den meisten der evaluierten Parameter und der EDSS und dem MSFC (die r-Werte lagen zwischen 0,207 und 0,537, p < 0,05). Die höchsten Korrelationen wurden für die „delineated area“ und die „average speed of sway“ im Zustand mit geschlossenen Augen und deren Differenz zwischen dem Zustand mit offenen und geschlossenen Augen festgestellt. Insbesondere diese beiden Parameter konnten zwischen (leicht) Behindertengruppen und gesunden Kontrollen unterscheiden. Zusätzlich zeigten Patienten ohne klinisch sichtbare Gleichgewichtsstörungen, die durch den Romberg-Testscore der EDSS-Bewertung dokumentiert wurden, signifikant schlechtere Ergebnisse der „delineated area“ [+1,97 cm2, 95%-CI (0,61-3,34); p = 0,002] als gesunde Kontrollen. Ein ähnliches Muster wurde für den Vergleich zwischen MS Patienten mit normaler Kleinhirnfunktion und gesunden Probanden beobachtet. Auf der anderen Seite nahmen 15 Patienten mit Fingolimod und 10 mit Glatirameracetat an der Messung der fäkalen sIgA teil. Zum ausgewerteten Zeitpunkt gab es keinen signifikanten Unterschied zwischen beiden Gruppen. Ein ähnliches Muster zeigte sich bei der Speichel-sIgA und den Serum-Immunglobulinen. In unseren Studien evaluierten wir die statische Posturographie als Ergänzung der neurologischen Beurteilung durch die EDSS, die die Krankheitsinvalidität bereits in frühen Krankheitsstadien charakterisieren könnte. Die MS-Patienten waren zur Aufrechterhaltung der posturalen Kontrolle stärker vom visuellen Feedback abhängig als von der HC. Im Gegensatz dazu konnten wir eine Abnahme der fäkalen sIgA Konzentration nach einer Langzeitbehandlung mit Fingolimod nicht bestätigen, wobei zur weiteren Analyse Längsschnittstudien erforderlich sind