Image registration methods underpin many analysis techniques in neuroimaging. They are essential in group studies when images of different individuals or different modalities need to be brought into a common reference frame. This thesis explores the potential of brain connectivity- driven alignment and develops surface registration techniques for magnetic resonance imaging (MRI), which is a noninvasive neuroimaging tool for probing function and structure of the human brain. The first part of this work develops a novel surface registration framework, based on free mesh deformations, which aligns cortical and subcortical surfaces by matching structural connectivity patterns derived using probabilistic tractography (diffusion-weighted MRI). Structural, i.e. white matter, connectivity is a good predictor of functional specialisation and structural connectivity-driven registration can therefore be expected to enhance the alignment of functionally homologous areas across subjects. The second part validates developed methods for cortical surfaces. Resting State Networks are used in an innovative way to delineate several functionally distinct regions, which were then used to quantify connectivity-driven registration performance by measuring the inter- subject overlap before and after registration. Consequently, the proposed method is assessed using an independent imaging modality and the results are compared to results from state-of-the-art cortical geometry-driven surface registration methods. A connectivity-driven registration pipeline is also developed for, and applied to, the surfaces of subcortical structures such as the thalamus. It is carefully validated on a set of artificial test examples and compared to another novel surface registration paradigm based on spherical wavelets. The proposed registration pipeline is then used to explore the differences in the alignment of two groups of subjects, healthy controls and Alzheimer's disease patients, to a common template. Finally, we propose how functional connectivity can be used instead of structural connectivity for driving registrations, as well as how the surface-based framework can be extended to a volumetric one. Apart from providing the benefits such as the improved functional alignment, we hope that the research conducted in this thesis will also represent the basis for the development of templates of structural and functional brain connectivity.

Myocardial scar, a non-viable tissue which occurs on the myocardium due to the insufficient blood supply to the heart muscle, is one of the leading causes of life-threatening heart disorders, including arrhythmias. Analysis of myocardial scar is important for predicting the risk of arrhythmia and locations of re-entrant circuits in patients’ hearts. For applications, such as computational modeling of cardiac electrophysiology aimed at stratifying patient risk for post-infarction arrhythmias, reconstruction of the intact geometry of scar is required. Currently, 2D multi-slice late gadolinium-enhanced magnetic resonance imaging (LGEMRI) is widely used to detect and quantify myocardial scar regions of the heart. However, due to the anisotropic spatial dimensions in 2D LGE-MR images, creating scar geometry from these images results in substantial reconstruction errors. For applications requiring reconstructing the intact geometry of scar surfaces, 3D LGE-MR images are more suited as they are isotropic in voxel dimensions and have a higher resolution. While many techniques have been reported for segmentation of scar using 2D LGEMR images, the equivalent studies for 3D LGE-MRI are limited. Most of these 2D and 3D techniques are basic intensity threshold-based methods. However, due to the lack of optimum threshold (Th) value, these intensity threshold-based methods are not robust in dealing with complex scar segmentation problems. In this study, we propose an algorithm for segmentation of myocardial scar from 3D LGE-MR images based on Markov random field based continuous max-flow (CMF) method. We utilize the segmented myocardium as the region of interest for our algorithm. We evaluated our CMF method for accuracy by comparing its results to manual delineations using 3D LGE-MR images of 34 patients. We also compared the results of the CMF technique to ones by conventional full-width-at-half-maximum (FWHM) and signal-threshold-to-reference-mean (STRM) methods. The CMF method yields a Dice similarity coefficient (DSC) of 71 +- 8.7% and an absolute volume error (|VE|) of 7.56 +- 7 cm3. Overall, the CMF method outperformed the conventional methods for almost all reported metrics in scar segmentation. We present a comparison study for scar geometries obtained from 2D vs 3D LGE-MRI. As the myocardial scar geometry greatly influences the sensitivity of risk prediction in patients, we compare and understand the differences in reconstructed geometry of scar generated using 2D versus 3D LGE-MR images beside providing a scar segmentation study. We use a retrospectively acquired dataset of 24 patients with a myocardial scar who underwent both 2D and 3D LGE-MR imaging. We use manually segmented scar volumes from 2D and 3D LGE-MRI. We then reconstruct the 2D scar segmentation boundaries to 3D surfaces using a LogOdds-based interpolation method. We use numerous metrics to quantify and analyze the scar geometry including fractal dimensions, the number-of-connected-components, and mean volume difference. The higher 3D fractal dimension results indicate that the 3D LGE-MRI produces a more complex surface geometry by better capturing the sparse nature of the scar. Finally, 3D LGE-MRI produces a larger scar surface volume (27.49 +- 20.38 cm3) than 2D-reconstructed LGE-MRI (25.07 +- 16.54 cm3).

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Cancer is one of the most common causes of death worldwide. When given optimal treatment, however, the risk of severe illness may greatly be reduced. Determining optimal treatment in turn requires evaluation of disease progression and response to potential, previous treatment. Analysis of perfusion, a physiological property that describes how well different tissues are supplied with blood, has been shown useful for revealing important tumor characteristics. By performing a contrast agent-enhanced, non-invasive medical imaging procedure, quantitative parameters of perfusion can be obtained by fitting the image data to mathematical models. These parameters may then provide valuable insights into tumor properties, useful for purposes such as diagnostics and treatment response evaluation. Varieties of parameter calculation frameworks and perfusion models may however lead to a wide range of possible parameter values, which negatively impacts reproducibility and confidence in results. The aim of this thesis project was to explore how different implementation choices in a perfusion parameter calculations framework, as well as image data noise and filtering, affected the parameter estimations. Image data of nine brain-tumor patients and a physical phantom was used for calculating perfusion parameters after systematically applying changes to the default calculations framework. The results showed that the choice of optimization method for parameter estimations could provide a significant difference in parameter estimations. A semi-automated method for obtaining a venous input function was evaluated and shown to be robust with respect to simulated user inputs. Generation of a T1 map, used when performing the parameter calculations, was explored for the variable flip-angle method and from this investigation it was concluded that a few combinations of flip-angles generated unrealistic T1 maps. Finally, a Gaussian image filter applied in the x- and ydimensions of the image data was found to provide a noticeable reduction of applied noise. The outcome of the experiments exemplified how calculation framework setup affected parameter estimations, which was discussed to be of importance for other areas of research as well. Future work could encompass exploration of other, more complex perfusion models, and performing similar analysis for tumors in other body-parts. / Cancer är en av de vanligaste dödsorsakerna i världen. Risken för svår sjukdom kan dock minimeras om optimal behandling ges, vilket kräver utvärdering av sjukdomstillstånd och svar på eventuell tidigare behandling för att åstadkommas. Mätningar av perfusion, en fysiologisk egenskap som direkt relaterar till vävnadernas blodtillförsel, har visat sig vara användbar för att avslöja viktiga tumöregenskaper. Genom att utföra en icke-invasiv medicinsk bildtagningsprocedur med kontrastvätska kan kvantitativa perfusionsparametrar erhållas genom att anpassa bilddatat till matematiska modeller. Dessa parametrar kan sedan ge värdefulla insikter om tumörers egenskaper, användbara för ändamål som diagnostik och utvärdering av behandling. Variationer av ramverk för parameterberäkningar och perfusionsmodeller kan dock leda till många olika, möjliga parametervärden, vilket negativt påverkar reproducerbarhet och förtroende för korrekthet hos de beräknade parametrarna. Syftet med detta examensarbete var att utforska hur implementeringen av ett ramverk för perfusionsparameterberäkningar, samt bilddatabrus och filtrering, påverkade parameterberäkningarna. Bilddata från nio hjärntumörpatienter samt en fysisk fantom användes för att beräkna perfusionsparametrar efter att systematiskt ändrat delar av ett ursprungligt beräkningsramverk. Resultaten visade att valet av optimeringsmetod för modelanpassning kunde ge en signifikant skillnad i parameteruppskattningar. En semi-automatiserad metod designad för att erhålla en venös inflödesfunktion utvärderades och påvisades vara robust med avseende på simulerad användarinteraktion. Generering av en T1-karta, som kan användas för parameterberäkningarna, undersöktes för variable flip-angle metoden, och från denna undersökning drogs slutsatsen att ett antal kombinationer av vinklar genererade orealistiska T1-kartor. Slutligen visade sig ett Gaussiskt bildfilter applicerat i x- och y-dimensionerna av bilddata ge en märkbar reducering av applicerat brus. Arbetet gav exempel på hur val av beräkningsramverk kan påverka parameteruppskattningar, vilket vidare diskuterades kan ha betydelse inom andra forskningsområden. Framtida undersökningar kan innefatta att utforska andra, mer komplexa perfusionsmodeller, samt att utföra liknande analyser för tumörer i andra kroppsdelar.

Burn injuries are a significant global health concern, causing high mortality and morbidity rates. Clinical assessment is the current standard for diagnosing burn injuries, but it suffers from interobserver variability and is not suitable for intermediate burn depths. To address these challenges, machine learning-based techniques were proposed to evaluate burn wounds in a thesis. The study utilized image-based networks to analyze two medical image databases of burn injuries from Caucasian and Black-African cohorts. The deep learning-based model, called BurnsNet, was developed and used for real-time processing, achieving high accuracy rates in discriminating between different burn depths and pressure ulcer wounds. The multiracial data representation approach was also used to address data representation bias in burn analysis, resulting in promising performance. The ML approach proved its objectivity and cost-effectiveness in assessing burn depths, providing an effective adjunct for clinical assessment. The study's findings suggest that the use of machine learning-based techniques can reduce the workflow burden for burn surgeons and significantly reduce errors in burn diagnosis. It also highlights the potential of automation to improve burn care and enhance patients' quality of life. / Petroleum Technology Development Fund (PTDF); Gombe State University study fellowship

Chronic obstructive pulmonary disease (COPD) describes a range of lung conditions including emphysema, chronic bronchitis and small airways disease. While COPD is a major cause of death and debilitating illness, current clinical assessment methods are inadequate: they are a poor predictor of patient outcome and insensitive to mild disease. A new imaging technology, hyperpolarised xenon MRI, offers the hope of improved diagnostic techniques, based on regional measurements using functional imaging. There is a need for quantitative analysis techniques to assist in the interpretation of these images. The aim of this work is to develop these techniques as part of a clinical trial into hyperpolarised xenon MRI. In this thesis we develop a fully automated pipeline for deriving regional measurements of lung function, making use of the multiple imaging modalities available from the trial. The core of our pipeline is a novel method for automatically segmenting the pulmonary lobes from CT data. This method combines a Hessian-based filter for detecting pulmonary fissures with anatomical cues from segmented lungs, airways and pulmonary vessels. The pipeline also includes methods for segmenting the lungs from CT and MRI data, and the airways from CT data. We apply this lobar map to the xenon MRI data using a multi-modal image registration technique based on automatically segmented lung boundaries, using proton MRI as an intermediate stage. We demonstrate our pipeline by deriving lobar measurements of ventilated volumes and diffusion from hyperpolarised xenon MRI data. In future work, we will use the trial data to further validate the pipeline and investigate the potential of xenon MRI in the clinical assessment of COPD. We also demonstrate how our work can be extended to build personalised computational models of the lung, which can be used to gain insights into the mechanisms of lung disease.