290 research outputs found

    Adaptive kernel estimation for enhanced filtering and pattern classification of magnetic resonance imaging: novel techniques for evaluating the biomechanics and pathologic conditions of the lumbar spine

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    This dissertation investigates the contribution the lumbar spine musculature has on etiological and pathogenic characteristics of low back pain and lumbar spondylosis. This endeavor necessarily required a two-step process: 1) design of an accurate post-processing method for extracting relevant information via magnetic resonance images and 2) determine pathological trends by elucidating high-dimensional datasets through multivariate pattern classification. The lumbar musculature was initially evaluated by post-processing and segmentation of magnetic resonance (MR) images of the lumbar spine, which characteristically suffer from nonlinear corruption of the signal intensity. This so called intensity inhomogeneity degrades the efficacy of traditional intensity-based segmentation algorithms. Proposed in this dissertation is a solution for filtering individual MR images by extracting a map of the underlying intensity inhomogeneity to adaptively generate local estimates of the kernel’s optimal bandwidth. The adaptive kernel is implemented and tested within the structure of the non-local means filter, but also generalized and extended to the Gaussian and anisotropic diffusion filters. Testing of the proposed filters showed that the adaptive kernel significantly outperformed their non-adaptive counterparts. A variety of performance metrics were utilized to measure either fine feature preservation or accuracy of post-processed segmentation. Based on these metrics the adaptive filters proposed in this dissertation significantly outperformed the non-adaptive versions. Using the proposed filter, the MR data was semi-automatically segmented to delineate between adipose and lean muscle tissues. Two important findings were reached utilizing this data. First, a clear distinction between the musculature of males and females was established that provided 100% accuracy in being able to predict gender. Second, degenerative lumbar spines were accurately predicted at a rate of up to 92% accuracy. These results solidify prior assumptions made regarding sexual dimorphic anatomy and the pathogenic nature of degenerative spine disease

    Analyse der Körperzusammensetzung: Messung der Skelettmuskulatur mit Computertomographie und Implikationen für die Patientenversorgung

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    Objective: This thesis aims to evaluate the relationship between the skeletal muscle index derived from computed tomography (CT) images and patient outcomes, as well as its implications for patient care. This goal was pursued in five individual studies: Studies A and B evaluated the relationship between the lumbar skeletal muscle index (L3SMI) and patient outcomes in the intensive care unit (ICU) and oncology setting, respectively. Studies C and D evaluated the effect of CT acquisition parameters on body composition measures. Study E proposed a novel technique to automate the segmentation of skeletal muscle using a fully automated deep learning system. Material and methods: In total, 1328 axial CT images were included in the five studies. Patients in studies A and B were part of the clinical trials NCT01967056 and NCT01401907 at Massachusetts General Hospital (MGH), respectively. Body composition indices were computed using semi-automated segmentation. Multivariable regression models with a priori defined covariates were used to analyze clinical outcomes. To evaluate whether CT acquisition parameters influence segmentation, the Bland-Altman approach was used. In study E, a fully convolutional neural network was implemented for deep learning-based automatic segmentation. Results: Study A found lower L3SMI to be a predictor of increased mortality within 30 days of extubation (p = 0.033), increased rate of pneumonia within 30 days of extubation (p = 0.002), increased adverse discharge disposition (p = 0.044), longer hospital stays post-extubation (p = 0.048), and higher total hospital costs (p = 0.043). In study B, low L3SMI was associated with worse quality of life (p = 0.048) and increased depression symptoms (p = 0.005). Threshold-based segmentation of skeletal muscle in study C and adipose tissue compartments in study D were significantly affected by CT acquisition parameters. The proposed deep learning system in study E provided automatic segmentation of skeletal muscle cross-sectional area and achieved a high congruence to segmentations performed by domain experts (average Dice coefficient of 0.93). Conclusion: L3SMI is a useful tool for the assessment of muscle mass in clinical practice. In critically ill patients, L3SMI can provide clinically useful information about patient outcomes at the time of extubation. Patients with advanced cancer who suffered from low muscle mass reported worse quality of life and increased depression symptoms. This highlights the clinical relevance of addressing muscle loss early on as part of a multimodal treatment plan. Importantly, indices utilized in body composition analysis are significantly affected by CT acquisition parameters. These effects should be considered when body composition analysis is used in clinical practice or research studies. Finally, our fully automated deep learning system enabled instantaneous segmentation of skeletal muscle.Zielsetzung: Das Ziel der vorliegenden Dissertation war es, den Einfluss des auf CT-Bildern berechneten Skelettmuskelindexes auf klinische Ergebnisse von Patienten und die daraus resultierenden Implikationen für die Patientenversorgung zu evaluieren. Dieses Ziel wurde in fünf Einzelstudien verfolgt: In den Studien A und B wurde der Einfluss des lumbalen Skelettmuskelindex (L3SMI) auf klinische Endpunkte von Patienten auf der Intensivstation sowie in der Onkologie untersucht. Die Studien C und D evaluierten die Auswirkungen von CT-Akquisitionsparametern auf Indizes der Körperzusammensetzung. Studie E stellte eine neuartige Technik der automatisierten Segmentierung von Skelettmuskulatur vor, die durch maschinelles Lernen ermöglicht wurde. Material und Methoden: Insgesamt wurden 1328 axiale CT-Bilder in die fünf Studien eingeschlossen. Die Patienten der Studien A und B waren Teilnehmer der klinischen Studien NCT01967056 und NCT01401907 am Massachusetts General Hospital. Die Indizes der Körperzusammensetzung wurden mithilfe halbautomatischer Segmentierung berechnet. Die klinischen Endpunkte wurden in multivariablen Regressionsmodellen mit a priori definierten Kovariaten analysiert. Um zu evaluieren, ob CT-Akquisitionsparameter die Segmentierung beeinflussen, wurde der Bland-Altman-Ansatz verwendet. In Studie E wurden ein künstliches neuronales Netzwerk sowie maschinelles Lernen für die automatische Segmentierung eingesetzt. Ergebnisse: In Studie A war ein niedriger L3SMI ein Prädiktor für eine höhere Mortalität (p = 0.033) und Pneumonierate (p = 0.002) innerhalb von 30 Tagen nach der Extubation sowie für mehr ungünstige Entlassungen (p = 0.044) und höhere Behandlungskosten für den gesamten Krankenhausaufenthalt (p = 0.043). Ein niedriger L3SMI war in Studie B mit einer schlechteren Lebensqualität (p = 0.048) und stärkeren depressiven Symptomen (p = 0.005) assoziiert. Die schwellenwertbasierte Segmentierung der Skelettmuskulatur in Studie C und der Fettgewebekompartimente in Studie D wurde durch CT-Akquisitionsparameter signifikant beeinflusst. Das in Studie E vorgestellte vollautomatische Segmentierungssystem erreichte eine hohe Übereinstimmung mit den durch Experten erstellten Segmentationen (durchschnittlicher Dice-Koeffizient von 0.93). Fazit: Der L3SMI ist ein Werkzeug zur Beurteilung von Muskelmasse. Bei Intensivpatienten kann L3SMI zum Zeitpunkt der Extubation nützliche klinische Informationen liefern. Patienten mit fortgeschrittener Krebserkrankung, die zudem eine geringere Muskelmasse hatten, berichteten über eine schlechtere Lebensqualität und stärkere depressive Symptome. Dies unterstreicht die Notwendigkeit, die Muskulatur frühzeitig als Teil eines multimodalen Behandlungskonzeptes zu adressieren. Die Indizes der Körperzusammensetzung werden signifikant von CT-Akquisitionsparametern beeinflusst. Darüber hinaus ermöglichte unser vollautomatisiertes System dank maschinellen Lernens die verzögerungsfreie Segmentierung von Skelettmuskulatur

    Use of Serial Block Face-Scanning Electron Microscopy to Study the Ultrastructure of Vertebrate and Invertebrate Biology

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    PhD ThesisThe development of Serial Block Face Scanning Electron Microscopy (SBF-SEM) allows for acquisition of serially sectioned, imaged data of ultrastructure at high resolution. In this project, optimisation of both SBF-SEM methodology and 3-D image segmentation analysis was applied to the ultrastructural examination of two types of biological tissues, each requiring a different experimental approach. The first project was a connectomic based study, to determine the relationship between the neurons that synapse upon the Lobula Giant Movement Detector 2 (LGMD 2) neuron, within the optic lobe of the locust. A substantial portion of the LGMD 2 neuron was reconstructed along with the afferent neurons, enabling the discovery of retinotopic mapping from the photoreceptors of the eye onto the LGMD 2 neuron. A sub-class of afferent neurons was also found, most likely vital in the process of signal integration across the large LGMD 2 neuron. For the second project, two types of skeletal muscle (psoas and soleus) obtained from fetal and adult guinea pigs were analysed to assess tissue-specific changes in mitochondrial morphology with muscle maturation. Distinct mitochondrial shapes were found across both muscles and age groups and a classification system was developed. It was found that, in both muscles, by late fetal gestation the mitochondrial network is well developed and akin to that found in the adult. Quantitative and qualitative differences in mitochondria morphology and complexity were found between the two muscles in the adult group. These differences are likely to be related to functional specialisation. All data collected during the experiments have also been made available online on Zenodo, roughly 240GB, which can be used for further studies. Overall SBF-SEM was proven to be a robust method of gaining new insights into the ultrastructure in both models and has wide ranging capabilities for a variety of experimental objectives

    Cultivate Quantitative Magnetic Resonance Imaging Methods to Measure Markers of Health and Translate to Large Scale Cohort Studies

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    Magnetic Resonance Imaging (MRI) is an indispensable tool in healthcare and research, with a growing demand for its services. The appeal of MRI stems from its non-ionizing radiation nature, ability to generate high-resolution images of internal organs and structures without invasive procedures, and capacity to provide quantitative assessments of tissue properties such as ectopic fat, body composition, and organ volume. All without long term side effects. Nine published papers are submitted which show the cultivation of quantitative measures of ectopic fat within the liver and pancreas using MRI, and the process of validating whole-body composition and organ volume measurements. All these techniques have been translated into large-scale studies to improve health measurements in large population cohorts. Translating this work into large-scale studies, including the use of artificial intelligence, is included. Additionally, an evaluation accompanies these published studies, appraising the evolution of these quantitative MRI techniques from the conception to their application in large cohort studies. Finally, this appraisal provides a summary of future work on crowdsourcing of ground truth training data to facilitate its use in wider applications of artificial intelligence.In conclusion, this body of work presents a portfolio of evidence to fulfil the requirements of a PhD by published works at the University of Salford

    Cloud-Based Benchmarking of Medical Image Analysis

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    Medical imagin

    Doctor of Philosophy

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    dissertationGeometric abnormalities of the human hip joint, as found in femoroacetabular impingement (FAI) and acetabular dysplasia, alter hip biomechanics and may be the primary causes of osteoarthritis in young adults. However, empirical evidence of direct correlations between abnormal geometry, altered biomechanics, and osteoarthritis is scarce. Also, clinical measures used to diagnose FAI and dysplasia still have substantial limitations, including questions about their reliability, assumptions about hip joint geometry and their ability to definitively distinguish pathologic from normal hips. The goals of this dissertation are twofold. First, a set of tools are presented and applied to quantify three-dimensional (3D) anatomical differences between hips with FAI and control subjects. The 3D tools were developed, validated and applied to patients with a subtype of FAI, called cam FAI, to improve basic understanding of the spectrum of FAI deformities, and to provide meaningful new metrics of morphology that are relatable to current diagnostic methods and translate easily for clinical use. The second goal of this dissertation is to improve our understanding of intra-articular hip contact mechanics as well as hip joint kinematics and muscle forces. To do so, a finite element study of intraarticular cartilage contact mechanics was completed with a cohort of live human subjects, using a validated modeling protocol. Finally, musculoskeletal modeling was used with gait data from healthy subjects and acetabular dysplasia patients to provide preliminary estimates of hip joint kinematics, kinetics, and muscle forces and compare differences between the groups. The translational methods of this dissertation utilized techniques from orthopaedics, computer science, physical therapy, mechanics, and medical imaging. Results from this dissertation offer new insight into the complex pathomechanics and pathomorphology of FAI and acetabular dysplasia. Application and extension of the work of this dissertation has the potential to help establish links between FAI and dysplasia with osteoarthritis and to improve patient care

    Musculoskeletal modeling and finite element analysis of the proximal juvenile femur

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    The influence of mechanical loading on bone modelling and remodelling has been, and still is the subject of many studies. It is widely accepted that the internal structure of long bones is orientated to the strains experienced throughout activities, and the morphometry of the bones are as a result of the loading. Although other influences play a role in bone development including, hormonal, nutritional and genetic. The internal structure is orientated in such a way that it transfers the loads experienced without being excessive in weight, providing an efficient weight bearing structure. Many researchers have analysed the adult femur but little work has been undertaken to understand femoral development in juveniles. Therefore the aim this work was to develop an understanding of the mechanical stresses and strains that the femur experiences during growth.The juvenile femur changes dramatically throughout growth. These changes occur from prenatal through to full maturity. The most notable include the ossification from a highly cartilaginous structure in the early years of development, to bone at ~18 years old, an increase in the length and angle of the neck, a change in the shaft torsion and a change in the bicondylar angle. Similarly, the development of movement patterns and locomotion in humans changes significantly throughout growth. Movement is restricted in utero, in neonates the movement begins to engage muscular activity, at 6 months a baby is usually able to sit upright; 9 months crawling begins; by 1 year old there is the ability to walk without support and at 4 years old an adult like gait pattern has developed. Full adult gait pattern has been documented to be achieved between 8-11 years old.In this work through gait analysis and musculoskeletal modelling the loads which the femur experiences at specific stages/ages of bipedal locomotion are analysed. Finite element analyses were then performed to develop an understanding of the stresses and strains of the proximal juvenile femur in relation to the attainment and development of bipedal gait. This was achieved by evaluating changes in these mechanical stresses and strains throughout different ages, relating them to the variations discovered in the gait patterns.Digitisation of the femora was performed on four specimens; prenatal, 3 years old, 7 years old and an adult. Following the scanning of the specimens in a micro CT scanner, some restoration to the damaged samples was required. Furthermore the dry samples were incomplete, and the models were needed to be modelled to accurately resemble fully intact femurs. The CT scans contained the full shaft however were missing the fully articulated proximal femur, due to the dry nature of the specimens the cartilages were absent. MRI scans which contained the femoral head data but were missing the full shaft were merged with the CT data to create a fully articulated femur for use in subsequent modelling.Gait analysis was performed on five children aged from 3-7 years old, with an average of five adults gait data used for comparison. The analysis showed that kinematic data was similar between all ages, however kinetic results revealed some differences. Ground reaction force in the 3 year old showed a higher heel strike compared to a higher toe off observed in adult during the gait cycle, indicating a lack of control in the 3 year old. Furthermore the 3 year old, compared to the other ages, had different values in joint moments. These joint moment results in particular played a role in the muscle forces produced from the musculoskeletal modelling.To obtain the muscle force data required for the FEA, musculoskeletal models were built. Testing the reliability of the musculoskeletal model was performed comparing the kinematic and kinetic data from the musculoskeletal modelling against the data obtained from the motion capture system. A good agreement was found between these data sets with the kinematics having the largest difference in the ankle plantar flexion of 8.6°. The kinetic results revealed almost exact matches. Further testing was attempted between the muscle force data and collected EMG. The collected EMG matched reported EMG in the literature and the onset and offset times of muscle activity corresponded well to muscle force peaks produced in the musculoskeletal model. Comparisons between the EMG and force through calculating the EMG as a force were inconclusive, although a degree of accuracy was shown but a more comprehensive method is required. It was concluded that with the accuracy of the kinematic and kinetic results the musculoskeletal modelling was accurate enough to give a true representation of physiological muscle forces to be modelled during FEA.Analysis of the musculoskeletal modelling results in the children revealed that the 3 year old had the highest significance between all the age groups. With the greatest significance in the hip flexors and abductors throughout the gait cycle. Joint reaction forces as a percentage of bodyweight were found to be much higher in the juvenile models. The adult model had a value of 265% bodyweight whereas the 3 year old showed a reaction force of 537% bodyweight. These differences observed in the musculoskeletal modelling had a direct effect on the FEA because the loads calculated here were applied to the finite element models to evaluate the effects that these would have on the stresses and strains during growth and development of the femur.FE models were built to represent a 3 year old, 7 year old and adult femur. Age specific loads calculated over 100% of a gait cycle, were applied to the models. The stress/strain analysis revealed some differences between the models but in general the areas exposed to high and low strain levels were similar. The similarities could suggest that each model was structurally adapted to the loads the femur regularly experiences. The thesis was successful in evaluating the stress and strain distribution apparent in the developing femur. However the work would be advanced by evaluating models from age ranges with a much more varied movement pattern i.e. crawling. This would increase an understanding of the structural optimisation of the femur
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