Automatic signal and image-based assessments of spinal cord injury and treatments.

Spinal cord injury (SCI) is one of the most common sources of motor disabilities in humans that often deeply impact the quality of life in individuals with severe and chronic SCI. In this dissertation, we have developed advanced engineering tools to address three distinct problems that researchers, clinicians and patients are facing in SCI research. Particularly, we have proposed a fully automated stochastic framework to quantify the effects of SCI on muscle size and adipose tissue distribution in skeletal muscles by volumetric segmentation of 3-D MRI scans in individuals with chronic SCI as well as non-disabled individuals. We also developed a novel framework for robust and automatic activation detection, feature extraction and visualization of the spinal cord epidural stimulation (scES) effects across a high number of scES parameters to build individualized-maps of muscle recruitment patterns of scES. Finally, in the last part of this dissertation, we introduced an EMG time-frequency analysis framework that implements EMG spectral analysis and machine learning tools to characterize EMG patterns resulting in independent or assisted standing enabled by scES, and identify the stimulation parameters that promote muscle activation patterns more effective for standing. The neurotechnological advancements proposed in this dissertation have greatly benefited SCI research by accelerating the efforts to quantify the effects of SCI on muscle size and functionality, expanding the knowledge regarding the neurophysiological mechanisms involved in re-enabling motor function with epidural stimulation and the selection of stimulation parameters and helping the patients with complete paralysis to achieve faster motor recovery

Prior Knowledge, Random Walks and Human Skeletal Muscle Segmentation

International audienceIn this paper, we propose a novel approach for segmenting the skeletal muscles in MRI automatically. In order to deal with the absence of contrast between the different muscle classes, we proposed a principled mathematical formulation that integrates prior knowledge with a random walks graph-based formulation. Prior knowledge is repre- sented using a statistical shape atlas that once coupled with the random walks segmentation leads to an efficient iterative linear optimization sys- tem. We reveal the potential of our approach on a challenging set of real clinical data

Personalisation of musculoskeletal models using Magnetic Resonance Imaging

Musculoskeletal (MSK) disorders affecting locomotion represent one of the leading causes for disability in the developed countries, impacting on the patients’ lifestyle and social inclusion, as well as the national healthcare resources. Due to the different aetiologies and progression of such diseases, and to the individual needs of patients, personalised assessment is currently promoted as the gold standard for the diagnosis and treatment of MSK disorders. The introduction of MSK models has recently integrated more traditional measurements of gait-related parameters, enabling the simulation of clinical scenarios and rehabilitation plans within a computational environment, therefore limiting the invasiveness of the experiments. However, the lack of standardised and validated procedures currently limits the adoption of these techniques in the clinical practice and restricts their shareability across the research community. The aim of this PhD thesis was to develop an innovative, robust, and repeatable procedure for the definition of MRI-based subject-specific MSKMs of the lower limb. A fully documented procedure (and associated methodologies) for producing such models was proposed. The final scope of this project is to promote the adoption of personalised modelling in the clinical assessment of lower-limb MSK disorders. The versatility of the proposed modelling approach was successfully tested by applying it in cohorts featured by different age (juvenile and elderly), genders and health conditions (juvenile idiopathic arthritis and osteopenia). In particular the model was tested for its ability to: discriminate joint kinematics and joint loadings that are typical of different populations; identify informative biomechanical parameters to characterise disease and disease progression in juvenile idiopathic arthritis; quantify the effect of different physiological muscle features, such as volumes and geometry, on the estimate of joint loading. As a result of the work carried out as part of the above studies, a significant advance in the standardisation and automation of the procedures needed for building fully personalised MRI-based models of the MSK system has been achieved. The model outputs were proved to have good repeatability and reproducibility and to be informative in all above applications. The proposed approach also showed a clear potential toward complementing traditional clinical gait analysis approaches by providing information on the muscle and joint internal forces, otherwise not easily accessible in-vivo. Future work will aim at reducing the cost, operator time, and errors associated to MRI-based MSK modelling by further improving and automating the image processing techniques and even replacing the MRI with affordable and portable technologies, such as ultrasound-based systems

Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults

This article was supported by the German Research Foundation (DFG) and the Open Access Publication Fund of Humboldt-Universität zu Berlin.There is increasing evidence that including sex as a biological variable is of crucial importance to promote rigorous, repeatable and reproducible science. In spite of this, the body of literature that accounts for the sex of participants in human locomotion studies is small and often produces controversial results. Here, we investigated the modular organization of muscle activation patterns for human locomotion using the concept of muscle synergies with a double purpose: i) uncover possible sex-specific characteristics of motor control and ii) assess whether these are maintained in older age. We recorded electromyographic activities from 13 ipsilateral muscles of the lower limb in young and older adults of both sexes walking (young and old) and running (young) on a treadmill. The data set obtained from the 215 participants was elaborated through non-negative matrix factorization to extract the time-independent (i.e., motor modules) and time-dependent (i.e., motor primitives) coefficients of muscle synergies. We found sparse sex-specific modulations of motor control. Motor modules showed a different contribution of hip extensors, knee extensors and foot dorsiflexors in various synergies. Motor primitives were wider (i.e., lasted longer) in males in the propulsion synergy for walking (but only in young and not in older adults) and in the weight acceptance synergy for running. Moreover, the complexity of motor primitives was similar in younger adults of both sexes, but lower in older females as compared to older males. In essence, our results revealed the existence of small but defined sex-specific differences in the way humans control locomotion and that these are not entirely maintained in older age.Peer Reviewe

Human activity recognition for an intelligent knee orthosis

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaActivity recognition with body-worn sensors is a large and growing field of research. In this thesis we evaluate the possibility to recognize human activities based on data from biosignal sensors solely placed on or under an existing passive knee orthosis, which will produce the needed information to integrate sensors into the orthosis in the future. The development of active orthotic knee devices will allow population to ambulate in a more natural, efficient and less painful manner than they might with a traditional orthosis. Thus, the term ’active orthosis’ refers to a device intended to increase the ambulatory ability of a person suffering from a knee pathology by applying forces to correct the position only when necessary and thereby make usable over longer periods of time. The contribution of this work is the evaluation of the ability to recognize activities with these restrictions on sensor placement as well as providing a proof-of-concept for the development of an activity recognition system for an intelligent orthosis. We use accelerometers and a goniometer placed on the orthosis and Electromyography (EMG) sensors placed on the skin under the orthosis to measure motion and muscle activity respectively. We segment signals in motion primitives semi-automatically and apply Hidden-Markov-Models (HMM) to classify the isolated motion primitives. We discriminate between seven activities like for example walking stairs up and ascend a hill. In a user study with six participants, we evaluate the systems performance for each of the different biosignal modalities alone as well as the combinations of them. For the best performing combination, we reach an average person-dependent accuracy of 98% and a person-independent accuracy of 79%

COMBINING MUSCULOSKELETAL MODELING AND FEM IN DIABETIC FOOT PREVENTION

Recently the development of Patient-specific models (PSMs) tailored to patient-specific data, has gained more and more attention in clinical applications. PSMs could represent a solution to the growing awareness of personalized medicine which allow the realization of more effective rehabilitation treatments designed on the subject capabilities. PSMs have the potential of improving diagnosis and optimizing clinical treatments by predicting and comparing the outcomes of different approaches of intervention. Furthermore they can provide information that cannot be directly measured, such as muscle forces or internal stresses and strains of the bones. Given the considerable amount of diseases affecting motor ability, PSMs of the lower limbs have been broadly addressed in literature. Two techniques are mostly used in this area: musculoskeletal (MS) modeling and finite element (FE) analysis. (MS) models represent a valuable tool, as they can provide important information about the unique anatomical and functional characteristics of different subjects, through the computation of human internal variables, such as muscle activations and forces and joint contact forces. The flexibility and adaptability of FE analysis makes it a perfect solution to model biological geometries and materials and to simulate complicated boundary and loading conditions. Accurate and descriptive FE models would serve as an excellent tool for scientific and medical research. Furthermore they could be used in clinical settings if combined with medical imaging, in order to improve patient care. Several 3-dimensional (3D) foot FE models were recently developed to analyze the biomechanical behavior of the human foot and ankle complex that is commonly studied with experimental techniques like stereophotogrammetry, force and plantar pressure plates. In this context, many gait analysis protocols have been proposed to assess the 3D kinetics, kinematics and plantar pressure distribution. This evaluation has shown to be useful in characterizing the foot biomechanics in different pathologies like the diabetic foot. Diabetic foot is an invalidating complication of diabetes mellitus, a chronic disease frequently encountered in the aging population. It is characterize by the development of ulcers which can lead to amputation. Models for simulations of deformations and stresses in the diabetic plantar pad are required to predict high risk areas on the plantar surface and can be used to investigate the performance of different insoles design for optimal pressure relief. This work represents a first effort towards the definition of a more complete PSM which combining both a MS model and a FE model, can increase the understanding of the diabetic foot pathology. To achieve this objective, several limitations and issues have been addressed. As first, MS models of diabetic and control subjects were developed using OpenSim, to estimate muscle forces. The objective was to evaluate whether the diabetic population exhibit lower limb muscle strength deficits compared to the healthy one. Subjects routine gait analysis was performed and lower limb joints kinematics, kinetics, time and space parameters estimated by means of a modified version of the IORgait protocol. 3D lower limb joints kinematics and kinetics was also calculated with OpenSim. Both methodologies were able to highlight differences in joint kinematics and kinetics between the two populations. Furthermore MS models showed significant differences in healthy muscle forces with respect to the diabetic ones, in some of the muscles. This knowledge can help the planning of specific training in order to improve gait speed, balance, muscle strength and joint mobility. After the use of MS models proved to be applicable in the diabetic population, the next step was to combine them with foot FE models. This was done in two phases. At first the impact of applying the foot joints contact forces (JCFs) obtained from MS models as boundary condition on the foot FE models was verified. Subject specific geometries from MRI were used for the development of the foot FE models while the experimental plantar pressures acquired during gait were used in the validation process. A better agreement was found between experimentally measured and simulated plantar pressure obtained with JCFs than with the experimentally measured ground reaction forces as boundary conditions. Afterwards the use of muscles forces as boundary condition in the FE simulations was evaluated. Subject-specific integrated and synchronized kinematic-kinetic data acquired during gait analysis were used for the development of the MS models and for the computation of the muscle forces. Muscle insertions were then located in the MRI and correspondent connectors were created in the FE model. FE subject-specific simulations were subsequently run with Abaqus by conducting a quasi-static analysis on 4 gait cycle phases and adopting 2 conditions: one including the muscle forces and one without. Once again the validation of the FE simulations was done by means of a comparison between simulated and experimentally measured plantar pressures. Results showed a marked improvement in the estimation of the peak pressure for the model that included the muscles. Finally, an attempt towards the definition of a parametric foot finite element model was done. In fact, despite the recent developments, patient-specific models are not yet successfully applied in a clinical setting. One of the challenges is the time required for mesh creation, which is difficult to automate. The development of parametric models by means of the Principle Component Analysis (PCA) can represent an appealing solution. In this study PCA was applied to the feet of a small cohort of diabetic and healthy subjects in order to evaluate the possibility of developing parametric foot models and to use them to identify variations and similarities between the two populations. The limitations of the use of models have also been analyzed. Their adoption is indeed limited by the lack of verification and validation standards. Even using subjects’ MRI or CT data for the development of FEM together with experimentally acquired motion analysis data for the boundary and loading conditions, the subject specifity is still not reached for what regards all the material properties. Furthermore it should be considered that everything relies on algorithm and models that would never be perfectly representing the reality. Overall, the work presented in this thesis represents an extended evaluation of the possible uses of modeling techniques in the diabetic foot prevention, by considering all the limitations introduced as well as the potential benefits of their use in a clinical context. The research is organized in six chapters: Chapter 1 - provides a background on the modeling techniques, both FE modeling and MS modeling. Furthermore it also describes the gait analysis, its instrumentation and some of the protocols used in the evaluation of the biomechanics of the lower limbs; Chapter 2 - gives a detailed overview of the biomechanics of the foot. It particularly focuses on the diabetes and the diabetic foot; Chapter 3 - introduces the application of MSs for the diabetic foot prevention after a brief background on the techniques usually chosen for the evaluation of the motor impairments caused by the disease. Aim, material and methods, results and discussion are presented. The complete work flow is described, and the chapter ends with a discussion on new key findings and limitations. Chapter 4 – reports the work done to combine the use of musculoskeletal models with foot FEMs. At first the impact of applying the foot joints contact forces obtained from MS models as boundary condition on the foot FEMs is verified. Then the use of muscles forces (again obtained from MS models) as boundary condition in the FE simulations is evaluated. For both studies a brief background is presented together with the methods applied, the results obtained and a discussion of novelties and drawbacks. Chapter 5 – explores the possibility of defining a parametric foot FEM applying the Principle Component Analysis (PCA) on the feet of a small cohort of diabetic and healthy subjects. A background on the importance of patient specific models is presented followed by material and methods, results and discussion of what obtained with this study. Chapter 6 - summarizes the results and the novelty of the thesis, delineating the conclusions and the future research paths

Muscle growth in the lower legs of typically developing children

Little is known about human muscle growth in children with and without cerebral palsy (CP). The MUGgLE study aims to investigate growth-related changes in the three-dimensional (3D) architecture of lower leg muscles (muscle volume, physiological cross-sectional area (PCSA), fascicle length, and pennation angle) in 320 infants and children with and without CP aged < 3 months and 5 to 15 years. Infants have one leg scan (anatomical magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) images), while children have three scans over three years. The MUGgLE study is ongoing. This thesis presents data derived from the first scan conducted on each of 208 typically developing (TD) infants and children. Chapter 2 provides muscle volumes of ten muscle groups in infants, and the architecture and moment arms of the medial (MG) and lateral gastrocnemius (LG) muscles. By comparing these data to data obtained from adults, it was shown that MG muscle fascicles grow primarily in cross-section rather than in length from birth to adulthood. Chapter 3 determines if lower leg muscles grow synchronously from birth to 15 years. The data show that muscle volumes, normalised to total lower leg volume, vary with age, indicating asynchronous growth. The soleus and MG muscles grow disproportionately faster. Chapter 4 determines muscle-, age-, and sex-conditional distributions of MG and tibialis anterior (TA) muscle architecture from birth to 15 years. Up to age 15 years, both muscles grow nonlinearly in volume, PCSA, and fascicle length, while the pennation angles remain nearly constant. The MG and TA muscle fascicles grow primarily transversely rather than longitudinally over this period. Chapter 5 explores the development and evaluation of a portable dynamometer used to estimate the passive length-tension curves of the gastrocnemius muscles in children. The evaluation shows that the dynamometer requires further methodological refinements to be reliable enough for clinical and research use. This thesis contributes to the fields of biomechanics, muscle physiology, and human anatomy, providing the largest high-resolution 3D dataset of muscle architecture in children to date. Biomechanists can use the data to build more effective structure-function models of children’s muscles, clinicians can use the data to investigate disordered muscle growth in children and inform early interventions and treatments, and academics can use the data to teach muscle and bone anatomy