The aim of this exploratory study was to evaluate the influence of hepatic steatosis on the detection rate of metastases in gadoxetic acid-enhanced liver magnetic resonance imaging (MRI). A total of 50 patients who underwent gadoxetic acid-enhanced MRI (unenhanced T1w in- and opposed-phase, T2w fat sat, unenhanced 3D-T1w fat sat and 3-phase dynamic contrast-enhanced (uDP), 3D-T1w fat sat hepatobiliary phase (HP)) were retrospectively included. Two blinded observers (O1/O2) independently assessed the images to determine the detection rate in uDP and HP. The hepatic signal fat fraction (HSFF) was determined as the relative signal intensity reduction in liver parenchyma from in- to opposed-phase images. A total of 451 liver metastases were detected (O1/O2, n = 447/411). O1/O2 detected 10.9%/9.3% of lesions exclusively in uDP and 20.2%/15.5% exclusively in HP. Lesions detected exclusively in uDP were significantly associated with a larger HSFF (area under curve (AUC) of receiver operating characteristic (ROC) analysis, 0.93; p < 0.001; cutoff, 41.5%). The exclusively HP-positive lesions were significantly associated with a smaller diameter (ROC-AUC, 0.82; p < 0.001; cutoff, 5 mm) and a smaller HSFF (ROC-AUC, 0.61; p < 0.001; cutoff, 13.3%). Gadoxetic acid imaging has the advantage of detecting small occult metastatic liver lesions in the HP. However, using non-optimized standard fat-saturated 3D-T1w protocols, severe steatosis (HSFF > 30%) is a potential pitfall for the detection of metastases in HP.

Localization of features and structures in images is an important task in medical image-processing. Characteristic structures and features are used in diagnostics and surgery planning for spatial adjustments of the volumetric data, including image registration or localization of bone-anchors and fiducials. Since this task is highly recurrent, a fast, reliable and automated approach without human interaction and parameter adjustment is of high interest. In this paper we propose and compare four image processing pipelines, including algorithms for automatic detection and localization of spherical features within 3D MRI data. We developed a convolution based method as well as algorithms based on connected-components labeling and analysis and the circular Hough-transform. A blob detection related approach, analyzing the Hessian determinant, was examined. Furthermore, we introduce a novel spherical MRI-marker design. In combination with the proposed algorithms and pipelines, this allows the detection and spatial localization, including the direction, of fiducials and bone-anchors.

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<p>Diffusion tensor (DT) and blood oxygen-level dependent (BOLD) imaging are two noninvasive magnetic resonance (MR) techniques that have been used to probe skeletal muscle microstructure and microvasculature, respectively. Over a series of four studies, the work in this thesis aimed at furthering our understanding of baseline DT metrics and BOLD signals in lower limb muscles (calf and foot) of healthy young subjects. Since postural changes have been shown to alter numerous quantities, including fluid volumes and muscle cross sectional area, DT indices and BOLD signal characteristics were examined in response to movement from upright to supine position.</p> <p>Reductions of 3.2-6.7% and 3.4-7.5% were measured in calf DT eigenvalues and apparent diffusion coefficient (ADC) in the various muscles, following 34 and 64 minutes of supine rest, respectively (P</p> <p>Establishment of baseline diffusion metrics in the foot region was feasible (chapter 6). Examination of foot DT indices in response to positional change showed that the metrics decreased from 2.7-4.6% following 34 minutes of supine rest (P</p> / Doctor of Philosophy (PhD)

In spinal cord injury the amount of total surviving white matter is known to be strongly related to post injury neurological functions (1). Accurate segmentation of these regions is shown to be critical in terms of developing effective treatment (1). Diffusion Tensor Imaging (DTI) has been shown to be effective in obtaining spinal cord images (2). However challenges still exist in clear separation of gray/white/cerebrospinal fluid (CSF) structures within the cord using DTI. The purpose of this study is to (1) test a semi-automatic tissue segmentation algorithm based on grow cut algorithm (GCA), to classify CSF, gray and white matter in conventional T2 weighted MRI and Diffusion Tensor Imaging (DTI) images in pediatric spinal cord injury (SCI) subjects, and (2) to compare the results of semi-automatic GCA segmentation with manually segmented spinal cord data performed on various DTI images by a board certified pediatric neuroradiologist. Results show that semi-automatic segmentation of the spinal cord using GCA was successfully implemented. Qualitatively, good separation of cord/CSF was seen in B0, CFA and FA maps (of a representative patient with SCI and a control using this GCA method. They demonstrate more homogeneous signal within the cervical spinal cord as well as greater conspicuity of the cord and surrounding CSF interface. Quantitative analysis of images segmented using GCA and manual segmentation between and within the groups showed no significant differences in CFA (p=0.1347) and FA (p=0.1442) images but B0 (p=0.0001) images showed statistically significant differences. Overall, in both the controls and subjects with SCI, quantitative and qualitative analysis showed a superior semi-automated segmentation on CFA and FA images over a B0 image the using modified GCA. Key words: Grow Cut Algorithm (GCA), Magnetic Resonance Imaging (MRI), segmentation, Diffusion Tensor Imaging (DTI), cervical spinal cord, cerebral-spinal fluid (CSF). / Bioengineering

Parkinson's disease (PD) represents the second most prevalent neurodegenerative disorder. Despite notable advancements in the comprehension of its pathophysiology in recent years and the introduction of updated diagnostic criteria that reflect these developments, the comprehensive understanding of its intricate clinical presentation remains to be further investigated and more precisely delineated. A substantial body of evidence from existing literature has consistently demonstrated the presence of non-motor symptoms in patients with PD, including deficits in social cognition. Nevertheless, socio-cognitive dysfunctions in PD remain a relatively underexplored area, and the available tools for assessing these deficits are limited, particularly in the clinical setting. To address this gap in the existing literature, the present doctoral thesis is structured around three key objectives. The first aim is to improve the characterisation of socio-cognitive dysfunctions in PD in the clinical context. Secondly, the objective is to introduce and evaluate the clinical validity of a novel test designed to assess the ability to recognise complex mental states. Finally, the thesis aims to contribute to the elucidation of the neural correlates underlying deficits in mental state recognition in individuals with PD via magnetic resonance imaging. In order to achieve these aims, three behavioural studies (Study 1-3) and one neuroimaging study (Study 4) have been conducted. The results of this doctoral thesis posit that deficits in socio-cognitive processes can be observed in patients with PD from the early stages of the disease and are associated with structural and functional changes in brain areas involved in emotion understanding. Furthermore, this thesis suggests that a detailed characterisation of early socio-cognitive dysfunctions in PD may facilitate the definition of new cognitive phenotypes and potentially the development of new non-pharmacological interventions (Study 5) tailored to the individual's specific needs.

No / Aim To review the recent evidence that has resulted from experiences in and out of conflict in relation to improving imaging in cases of major trauma. Method A search of electronic databases, the internet and Cochrane library was undertaken to identify relevant publications which were analysed in terms of quality. Evidence that has emerged from civilian and military practice that could influence the practice of major trauma imaging in future was discussed. Results The importance of speed in assessing patients suffering major trauma is becoming more recognised. There is growing evidence that the use of portable ultrasound at the site of major trauma as first line investigation has potential. In more stable patients, the evidence for whole body CT at the expense of radiography is also growing. The concern regarding availability and radiation dose related to CT scanning remain significant but with the outcome of the recent Major Trauma Review and improvements in CT scanning techniques, such concerns are being addressed. There is limited research in the use of MRI in relation to major trauma. Conclusion Ultrasound at the sight of major trauma has potential but further research will be needed. Factors such as operator training in particular need to be considered. CT scanning remains an important diagnostic tool for patients suffering major trauma and this is borne out by the Major Trauma Review and NICE guidelines. The availability of CT scanning in relation to accident and emergency scanning is a factor the Major Trauma Review has highlighted and the close proximity of new CT scanners to accident and emergency is a factor that will need to be taken into account in strategic planning. Given the growing evidence of CT involvement, the continued practice of cervical spine and pelvic radiography in cases of major trauma should be questioned.

Radiomics integrates machine learning (ML) and radiology to extract and analyze quantitative features from medical imaging modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), ultrasound (US) and digital radiographs (DX). By extracting pixel/voxel-level data, followed by standardization and feature selection, radiomics enables ML algorithms to assist in diagnosis and prognosis. While extensively researched in human medicine its application in veterinary medicine remains limited. Radiomics offers objective, data-driven insights, surpassing qualitative evaluations by revealing micromolecular disease features invisible to the human eye. Radiomics holds significant promise for diagnosing gliomas (GM), a challenging brain tumor where histopathology, the diagnostic gold standard, is seldom performed in veterinary medicine due to logistical and financial barriers, and it is also limited by inherent pathologist subjectivity and disagreement. Additionally, qualitative MRI demonstrates limited accuracy in identifying GM type and grade. By offering non-invasive and reproducible diagnostic and prognostic solutions, radiomics has the potential to overcome these challenges, enhancing brain tumor evaluation in both veterinary and human medicine. The primary goal of this study is to enhance the diagnosis and prognosis of GM by exploring both conventional and innovative non-invasive imaging techniques, with a focus on qualitative and quantitative MRI approaches. We hypothesize that quantitative and novel qualitative methods will surpass conventional expert qualitative assessments in accurately diagnosing GM type, grade, and progression. By doing so, we aim to improve the precision of GM imaging diagnoses, offering clinicians a more accessible and reliable tool to support their diagnostic and treatment decisions. Chapter 1 of this dissertation presents a comprehensive review of the challenges associated with diagnosing GM using MRI. It also introduces principles of radiomics, a novel and relatively underexplored field in veterinary medicine centered on quantitative imaging analysis for diagnostic and prognostic purposes. This includes an in-depth discussion of the radiomics workflow and associated ML methods. Chapter 2 demonstrates the use and efficacy of quantitative MRI for determination of GM size and therapeutic response assessments using both linear and volumetric techniques. Chapter 3 investigates the T2-weighted–FLAIR mismatch sign (T2FMM) in dogs, a well-established imaging biomarker of human low-grade astrocytomas, and demonstrates that the T2FMM is a highly specific biomarker for oligodendrogliomas —the first such imaging biomarker for GM to be discovered in veterinary research. Finally, Chapter 4 illustrates a structured radiomics pipeline for the standardized quantitative analysis of brain tumors on MRI and demonstrates that the use of radiomics ML models results in superior ability to diagnose canine GM subtypes and grades and discriminate GM from non-neoplastic intra-axial lesions when compared to expert rater opinions derived from qualitative MRI evaluations. / Doctor of Philosophy / Radiomics is a cutting-edge approach that combines advanced computer algorithms with medical imaging techniques like Magnetic resonance imaging (MRI), Computed tomography (CT), Positron emission tomography (PET), ultrasound, and X-rays to uncover patterns invisible to the human eye. By analyzing detailed image data and using artificial intelligence (AI), radiomics provides new ways to diagnose and predict diseases. While this field has been widely studied in human medicine, its use in veterinary medicine is just beginning to be explored. Radiomics could transform how we diagnose gliomas (GM), a type of brain tumor that is particularly hard to identify in medical imaging studies in animals due to cost, logistical issues, and shared features with other diseases. Additionally, conventional MRI techniques often fail to accurately determine GM type and aggressiveness. This research aims to enhance GM diagnosis by using advanced imaging methods, combining both traditional visual and innovative quantitative MRI techniques. We believe that objective, measurable approaches and novel qualitative imaging features will be more effective than relying solely on radiologist' conventional visual assessments. The goal is to develop a more accurate, accessible, and objective tool to assist veterinary clinicians in diagnosing and treating their patients. Chapter 1 reviews the challenges in diagnosing GM with conventional MRI and introduces radiomics as a promising solution, discussing how it integrates AI with quantitative imaging analysis. Chapter 2 demonstrates how tumor size can be effectively assessed to predict response to treatments using simple quantitative measurement methods. Chapter 3 explores the T2-weighted–FLAIR mismatch sign (T2FMM), a key imaging biologic marker in human brain tumors, and evaluates its application in dogs—a pioneering effort in veterinary science. Finally, Chapter 4 outlines a radiomics-based pipeline for analyzing brain tumors, focusing on identifying GM type and aggressiveness, distinguishing tumors from non-tumor conditions, and comparing the performance of AI against expert diagnoses. This work has the potential to revolutionize veterinary brain tumor diagnostics and advance care for both animals and humans.

Stroke is one of the leading causes of death and adult disability worldwide. The major therapeutic intervention for acute ischemic stroke is the administration of recombinant tissue plasminogen activator (rtPA) to help to restore blood flow to the brain. This has been shown to increase the survival rate and to reduce the disability of ischemic stroke patients. However, rtPA is associated with intracranial haemorrhage and thus its administration is currently limited to only about 5% of ischemic stroke patients. More advanced imaging techniques can be used to better stratify patients for rtPA treatment. One new imaging technique, chemical exchange saturation transfer (CEST) magnetic resonance imaging, can potentially image intracellular pH and since tissue acidification happens prior to cerebral infarction, CEST has the potential to predict ischemic injury and hence to improve patient selection. Despite this potential, most studies have generated pH-weighted rather than quantitative pH maps; the most widely used metric to quantify the CEST effect is only able to generate qualitative contrast measurements and suffers from many confounds. The greatest clinical benefit of CEST imaging lies in its ability to non-invasively measure quantitative pH values which may be useful to identify salvageable tissue. The quantitative techniques and work presented in this thesis thus provide the necessary analysis to determine whether a threshold for the quantified CEST effect or for pH exists to help to define tissue outcome following stroke; to investigate the potential of CEST for clinical stroke imaging; and subsequently to facilitate clinical translation of CEST for acute stroke management.

This dissertation is comprised of four studies investigating the hypothesis that adults and children who stutter differ from their same-age fluent peers in the neuroanatomy and neurophysiology underlying auditory speech processing. It has been consistently reported that adults who stutter demonstrate unique functional neural activation patterns during speech production, including reduced auditory activation, relative to nonstutterers. The extent to which these functional differences are accompanied by abnormal morphology of the brain in stutterers is unclear. The first study in this dissertation examined the neuroanatomical differences in speech-related cortex between adults who do and do not stutter using magnetic resonance imaging and voxel-based morphometry analyses. Adults who stutter were found to have localized grey matter volume increases in auditory and motor speech related cortex. The second study extended this line of research to children who stutter, who were found to have localized grey matter volume decreases in motor speech related cortex. Together, these studies suggest an abnormal trajectory of regional grey matter development in motor speech cortex of people who stutter. The last two studies investigated the mechanism underlying the repeated findings of reduced auditory activation during speech in people who stutter in more detail. Magnetoencephalography was used to investigate the hypothesis that people who stutter have increased speech induced suppression of early evoked auditory responses. Adults and children who stutter demonstrated typical levels of speech induced suppression relative to fluent peers. However, adults and children who stutter showed differences from peers in the timing of cortical auditory responses. Taken together, the studies demonstrate structural and functional abnormalities in brain regions related to auditory processing and point to the possibility that people who stutter have difficulty forming the neural representations of speech sounds necessary for fluent speech production.