An interactive segmentation tool for quantifying fat in lumbar muscles using axial lumbar-spine MRI

In this paper we present an interactive tool that can be used to quantify fat infiltration in lumbar muscles, which is useful in studying fat infiltration and lower back pain (LBP) in adults. Currently, a qualitative assessment by visual grading via a 5-point scale is used to study fat infiltration in lumbar muscles from an axial view of lumbar-spine MR Images. However, a quantitative approach (on a continuous scale of 0–100%) may provide a greater insight. In this paper, we propose a method to precisely quantify the fat deposition/infiltration in a user-defined region of the lumbar muscles, which may aid better diagnosis and analysis. The key steps are interactively segmenting the region of interest (ROI) from the lumbar muscles using the well known livewire technique, identifying fatty regions in the segmented region based on variable-selection of threshold and softness levels, automatically detecting the center of the spinal column and fragmenting the lumbar muscles into smaller regions with reference to the center of the spinal column, computing key parameters [such as total and region-wise fat content percentage, total-cross sectional area (TCSA) and functional cross-sectional area (FCSA)] and exporting the computations and associated patient information from the MRI, into a database. A standalone application using MATLAB R2014a was developed to perform the required computations along with an intuitive graphical user interface (GUI)

Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images

Abstract Background The imaging assessment of paraspinal muscle morphology and fatty infiltration has gained considerable attention in the past decades, with reports suggesting an association between muscle degenerative changes and low back pain (LBP). To date, qualitative and quantitative approaches have been used to assess paraspinal muscle composition. Though highly reliable, manual thresholding techniques are time consuming and not always feasible in a clinical setting. The tedious and rater-dependent nature of such manual thresholding techniques provides the impetus for the development of automated or semi-automated segmentation methods. The purpose of the present study was to develop and evaluate an automated thresholding algorithm for the assessment of paraspinal muscle composition. The reliability and validity of the muscle measurements using the new automated thresholding algorithm were investigated through repeated measurements and comparison with measurements from an established, highly reliable manual thresholding technique. Methods Magnetic resonance images of 30 patients with LBP were randomly selected cohort of patients participating in a project on commonly diagnosed lumbar pathologies in patients attending spine surgeon clinics. A series of T2-weighted MR images were used to train the algorithm; preprocessing techniques including adaptive histogram equalization method image adjustment scheme were used to enhance the quality and contrast of the images. All muscle measurements were repeated twice using a manual thresholding technique and the novel automated thresholding algorithm, from axial T2-weigthed images, at least 5 days apart. The rater was blinded to all earlier measurements. Inter-method agreement and intra-rater reliability for each measurement method were assessed. The study did not received external funding and the authors have no disclosures. Results There was excellent agreement between the two methods with inter-method reliability coefficients (intraclass correlation coefficients) varying from 0.79 to 0.99. Bland and Altman plots further confirmed the agreement between the two methods. Intra-rater reliability and standard error of measurements were comparable between methods, with reliability coefficient varying between 0.95 and 0.99 for the manual thresholding and 0.97–0.99 for the automated algorithm. Conclusion The proposed automated thresholding algorithm to assess paraspinal muscle size and composition measurements was highly reliable, with excellent agreement with the reference manual thresholding method

Manually defining regions of interest when quantifying paravertebral muscles fatty infiltration from axial magnetic resonance imaging : a proposed method for the lumbar spine with anatomical cross-reference

Background: There is increasing interest in paravertebral muscle composition as a potential prognostic and diagnostic element in lumbar spine health. As a consequence, it is becoming popular to use magnetic resonance imaging (MRI) to examine muscle volume and fatty infiltration in lumbar paravertebral muscles to assess both age-related change and their clinical relevance in low back pain (LBP). A variety of imaging methods exist for both measuring key variables (fat, muscle) and for defining regions of interest, making pooled comparisons between studies difficult and rendering post-production analysis of MRIs confusing. We therefore propose and define a method as an option for use as a standardized MRI procedure for measuring lumbar paravertebral muscle composition, and to stimulate discussion towards establishing consensus for the analysis of skeletal muscle composition amongst clinician researchers. Method: In this descriptive methodological study we explain our method by providing an examination of regional lumbar morphology, followed by a detailed description of the proposed technique. Identification of paravertebral muscles and vertebral anatomy includes axial E12 sheet-plastinates from cadaveric material, combined with a series of axial MRIs that encompass sequencing commonly used for investigations of muscle quality (fat-water DIXON, T1-, and T2-weighted) to illustrate regional morphology; these images are shown for L1 and L4 levels to highlight differences in regional morphology. The method for defining regions of interest (ROI) for multifidus (MF), and erector spinae (ES) is then described. Results: Our method for defining ROIs for lumbar paravertebral muscles on axial MRIs is outlined and discussed in relation to existing literature. The method provides a foundation for standardising the quantification of muscle quality that particularly centres on examining fatty infiltration and composition. We provide recommendations relating to imaging parameters that should additionally inform a priori decisions when planning studies examining lumbar muscle tissues with MRI. Conclusions: We intend this method to provide a platform towards developing and delivering meaningful comparisons between MRI data on lumbar paravertebral muscle quality

Bone marrow Fat - A Novel Quantification Method and Potential Clinical Applications

Ageing bone is characterised by increased marrow fat infiltration altering its composition and microstructure, thus predisposing the person to osteoporosis. Yet to date, non-invasive quantifications of marrow fat are limited to special MRI techniques, and clinical studies examining marrow fat in the ageing skeleton are scarce. Thus, the key aims of this thesis are to: · Validate a new non-invasive technique of marrow fat quantification using CT technology · Determine the effects of dietary fatty acids on marrow fat · Measure marrow fat content in different skeletal regions in healthy older men · Determine the effect of exercise and calcium on marrow fat. The imaging techniques employed in our animal and human studies were micro CT (µCT) and quantitative CT (QCT) respectively. All images were analysed with the imaging software Slice O Matic version 4.1 (Tomovision). Regions of interest [ROIs] were Volumes of interests (VOIs) of bone, fat and blood measured in µm3 or mm3. Individual tissue volumes, expressed as percentages of the total marrow volume, and ratios of tissue volumes were also used in the analysis. Global and local thresholds for individual tissue volumes were determined separately for µCT and QCT. Thresholds for µCT were those derived from the initial validation study, whereas those for QCT were based on previous published data. To account for partial volume averaging effects, further manual refinement of threshold ranges were undertaken by inspection of individual pixels and their neighbours. This manual process was carried out for both µCT and QCT to derive local thresholds for use in manual segmentation and computation of volumes. Our validation study showed that quantification of marrow fat using µCT was reliable and accurate compared to the gold standard technique- histology- when reliably defined thresholds were used. Good agreement between tissue volumes measured by histology and those computed by the imaging software was demonstrated. We applied this technique to quantify marrow fat in an animal model of senile osteoporosis, and showed that fatty acids (ω- 3 and ω-6) had dual effects on bone. With QCT studies, we confirmed the age related increase in marrow adiposity, and more significantly, different ratios between fat and bone in common fracture regions. Similarly, exercise affects marrow fat differently in different regions, and there was a trend to statistically significant changes to marrow fat with exercise. In conclusion, this body of work showed that quantification of marrow fat using CT is promising, and has future clinical implications. However, significantly more clinical studies are needed to confirm these findings and refine shortfalls in quantification capabilities

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

Computer-aided techniques for assessment of MRI-detected inflammation for early identification of inflammatory arthritis

Inflammatory arthritis comprises a group of diseases in which the immune system attacks the body’s own tissues. Two prevalent types of inflammatory arthritis are rheumatoid arthritis (RA) and spondyloarthritis (SpA). Clinical research points to the importance of early diagnosis, as treatment in early disease stages increases chances of better outcome and improved quality of life for patients. To this end, the diagnostic potential of imaging modalities sensitive to local inflammation, such as magnetic resonance imaging (MRI), is of great interest. The goal of this thesis was to develop computer-aided methods for assessment of MRI-detected inflammation with the aim of aiding early diagnosis of inflammatory arthritis. In particular, we focused on the tasks of comparative visualization, automatic quantification, and feature selection. The presented studies showcase the potential of comparative visualization and automatic quantification to overcome the limitations of visual scoring and lay out a fertile ground for future improvements. Additionally, the understanding of the diagnostic role of individual inflammatory features in prediction of RA development is further advanced. Collectively, these findings can help facilitate the use of MRI for early diagnosis of inflammatory arthritis and potentially increase chances of better outcome and quality of life for patients.This research was supported by the Dutch Technology Foundation STW, under grant number 13329. STW (currently TTW) is part of the Netherlands Organization for Scientific Research (NWO), which is partly funded by the Dutch Ministry of Economic Affairs.LUMC / Geneeskunde Repositoriu

Studies on Spinal Fusion from Computational Modelling to ‘Smart’ Implants

Low back pain, the worldwide leading cause of disability, is commonly treated with lumbar interbody fusion surgery to address degeneration, instability, deformity, and trauma of the spine. Following fusion surgery, nearly 20% experience complications requiring reoperation while 1 in 3 do not experience a meaningful improvement in pain. Implant subsidence and pseudarthrosis in particular present a multifaceted challenge in the management of a patient’s painful symptoms. Given the diversity of fusion approaches, materials, and instrumentation, further inputs are required across the treatment spectrum to prevent and manage complications. This thesis comprises biomechanical studies on lumbar spinal fusion that provide new insights into spinal fusion surgery from preoperative planning to postoperative monitoring. A computational model, using the finite element method, is developed to quantify the biomechanical impact of temporal ossification on the spine, examining how the fusion mass stiffness affects loads on the implant and subsequent subsidence risk, while bony growth into the endplates affects load-distribution among the surrounding spinal structures. The computational modelling approach is extended to provide biomechanical inputs to surgical decisions regarding posterior fixation. Where a patient is not clinically pre-disposed to subsidence or pseudarthrosis, the results suggest unilateral fixation is a more economical choice than bilateral fixation to stabilise the joint. While finite element modelling can inform pre-surgical planning, effective postoperative monitoring currently remains a clinical challenge. Periodic radiological follow-up to assess bony fusion is subjective and unreliable. This thesis describes the development of a ‘smart’ interbody cage capable of taking direct measurements from the implant for monitoring fusion progression and complication risk. Biomechanical testing of the ‘smart’ implant demonstrated its ability to distinguish between graft and endplate stiffness states. The device is prepared for wireless actualisation by investigating sensor optimisation and telemetry. The results show that near-field communication is a feasible approach for wireless power and data transfer in this setting, notwithstanding further architectural optimisation required, while a combination of strain and pressure sensors will be more mechanically and clinically informative. Further work in computational modelling of the spine and ‘smart’ implants will enable personalised healthcare for low back pain, and the results presented in this thesis are a step in this direction

Development of an MRI Template and Analysis Pipeline for the Spinal Cord and Application in Patients with Spinal Cord Injury

La moelle épinière est un organe fondamental du corps humain. Étant le lien entre le cerveau et le système nerveux périphérique, endommager la moelle épinière, que ce soit suite à un trauma ou une maladie neurodégénérative, a des conséquences graves sur la qualité de vie des patients. En effet, les maladies et traumatismes touchant la moelle épinière peuvent affecter l’intégrité des neurones et provoquer des troubles neurologiques et/ou des handicaps fonctionnels. Bien que de nombreuses voies thérapeutiques pour traiter les lésions de la moelle épinière existent, la connaissance de l’étendue des dégâts causés par ces lésions est primordiale pour améliorer l’efficacité de leur traitement et les décisions cliniques associées. L’imagerie par résonance magnétique (IRM) a démontré un grand potentiel pour le diagnostic et pronostic des maladies neurodégénératives et traumas de la moelle épinière. Plus particulièrement, l’analyse par template de données IRM du cerveau, couplée à des outils de traitement d’images automatisés, a permis une meilleure compréhension des mécanismes sous-jacents de maladies comme l’Alzheimer et la Sclérose en Plaques. Extraire automatiquement des informations pertinentes d’images IRM au sein de régions spécifiques de la moelle épinière présente toutefois de plus grands défis que dans le cerveau. Il n’existe en effet qu’un nombre limité de template de la moelle épinière dans la littérature, et aucun ne couvre toute la moelle épinière ou n’est lié à un template existant du cerveau. Ce manque de template et d’outils automatisés rend difficile la tenue de larges études d’analyse de la moelle épinière sur des populations variées. L’objectif de ce projet est donc de proposer un nouveau template IRM couvrant toute la moelle épinière, recalé avec un template existant du cerveau, et intégrant des atlas de la structure interne de la moelle épinière (e.g., matière blanche et grise, tracts de la matière blanche). Ce template doit venir avec une série d’outils automatisés permettant l’extraction d’information IRM au sein de régions spécifiques de la moelle épinière. La question générale de recherche de ce projet est donc « Comment créer un template générique de la moelle épinière, qui permettrait l’analyse non biaisée et reproductible de données IRM de la moelle épinière ? ». Plusieurs contributions originales ont été proposées pour répondre à cette question et vont être décrites dans les prochains paragraphes. La première contribution de ce projet est le développement du logiciel Spinal Cord Toolbox (SCT). SCT est un logiciel open-source de traitement d’images IRM multi-parametrique de la moelle épinière (De Leener, Lévy, et al., 2016). Ce logiciel intègre notamment des outils pour la détection et la segmentation automatique de la moelle épinière et de sa structure interne (i.e., matière blanche et matière grise), l’identification et la labellisation des niveaux vertébraux, le recalage d’images IRM multimodales sur un template générique de la moelle épinière (précédemment le template MNI-Poly-AMU, maintenant le template PAM50, proposé içi). En se basant sur un atlas de la moelle, SCT intègre également des outils pour extraire des données IRM de régions spécifiques de la moelle épinière, comme la matière blanche et grise et les tracts de la matière blanche, ainsi que sur des niveaux vertébraux spécifiques. D’autres outils additionnels ont aussi été proposés, comme des outils de correction de mouvement et de traitement basiques d’images appliqués le long de la moelle épinière. Chaque outil intégré à SCT a été validé sur un jeu de données multimodales. La deuxième contribution de ce projet est le développement d’une nouvelle méthode de recalage d’images IRM de la moelle épinière (De Leener, Mangeat, et al., 2017). Cette méthode a été développée pour un usage particulier : le redressement d’images IRM de la moelle épinière, mais peut également être utilisé pour recaler plusieurs images de la moelle épinière entre elles, tout en tenant compte de la distribution vertébrale de chaque sujet. La méthode proposée se base sur une approximation globale de la courbure de la moelle épinière dans l’espace et sur la résolution analytique des champs de déformation entre les deux images. La validation de cette nouvelle méthode a été réalisée sur une population de sujets sains et de patients touchés par une compression de la moelle épinière. La contribution majeure de ce projet est le développement d’un système de création de template IRM de la moelle épinière et la proposition du template PAM50 comme template de référence pour les études d’analyse par template de données IRM de la moelle épinière. Le template PAM50 a été créé à partir d’images IRM tiré de 50 sujets sains, et a été généré en utilisant le redressement d’images présenté ci-dessus et une méthode de recalage d’images itératif non linéaire, après plusieurs étapes de prétraitement d’images. Ces étapes de prétraitement incluent la segmentation automatique de la moelle épinière, l’extraction manuelle du bord antérieur du tronc cérébral, la détection et l’identification des disques intervertébraux, et la normalisation d’intensité le long de la moelle. Suite au prétraitement, la ligne centrale moyenne de la moelle et la distribution vertébrale ont été calculées sur la population entière de sujets et une image initiale de template a été générée. Après avoir recalé toutes les images sur ce template initial, le template PAM50 a été créé en utilisant un processus itératif de recalage d’image, utilisé pour générer des templates de cerveau. Le PAM50 couvre le tronc cérébral et la moelle épinière en entier, est disponible pour les contrastes IRM pondérés en T1, T2 et T2*, et intègre des cartes probabilistes et atlas de la structure interne de la moelle épinière. De plus, le PAM50 a été recalé sur le template ICBM152 du cerveau, permettant ainsi la tenue d’analyse par template simultanément dans le cerveau et dans la moelle épinière. Finalement, plusieurs résultats complémentaires ont été présentés dans cette dissertation. Premièrement, une étude de validation de la répétabilité et reproductibilité de mesures de l’aire de section de la moelle épinière a été menée sur une population de patients touchés par la sclérose en plaques. Les résultats démontrent une haute fiabilité des mesures ainsi que la possibilité de détecter des changements très subtiles de l’aire de section transverse de la moelle, importants pour mesurer l’atrophie de la moelle épinière précoce due à des maladies neurodégénératives comme la sclérose en plaques. Deuxièmement, un nouveau biomarqueur IRM des lésions de la moelle épinière a été proposé, en collaboration avec Allan Martin, de l’Université de Toronto. Ce biomarqueur, calculé à partir du ratio d’intensité entre la matière blanche et grise sur des images IRM pondérées en T2*, utilise directement les développements proposés dans ce projet, notamment en utilisant le recalage du template de la moelle épinière et les méthodes de segmentation de la moelle. La faisabilité d’extraire des mesures de données IRM multiparamétrique dans des régions spécifiques de la moelle épinière a également été démontrée, permettant d’améliorer le diagnostic et pronostic de lésions et compression de la moelle épinière. Finalement, une nouvelle méthode d’extraction de la morphométrie de la moelle épinière a été proposée et utilisée sur une population de patients touchés par une compression asymptomatique de la moelle épinière, démontrant de grandes capacités de diagnostic (> 99%). Le développement du template PAM50 comble le manque de template de la moelle épinière dans la littérature mais présente cependant plusieurs limitations. En effet, le template proposé se base sur une population de 50 sujets sains et jeunes (âge moyen = 27 +- 6.5) et est donc biaisée vers cette population particulière. Adapter les analyses par template pour un autre type de population (âge, race ou maladie différente) peut être réalisé directement sur les méthodes d’analyse mais aussi sur le template en lui-même. Tous le code pour générer le template a en effet été mis en ligne (https://github.com/neuropoly/template) pour permettre à tout groupe de recherche de développer son propre template. Une autre limitation de ce projet est le choix d’un système de coordonnées basé sur la position des vertèbres. En effet, les vertèbres ne représentent pas complètement le caractère fonctionnel de la moelle épinière, à cause de la différence entre les niveaux vertébraux et spinaux. Le développement d’un système de coordonnées spinal, bien que difficile à caractériser dans des images IRM, serait plus approprié pour l’analyse fonctionnelle de la moelle épinière. Finalement, il existe encore de nombreux défis pour automatiser l’ensemble des outils développés dans ce projet et les rendre robuste pour la majorité des contrastes et champs de vue utilisés en IRM conventionnel et clinique. Ce projet a présenté plusieurs développements importants pour l’analyse de données IRM de la moelle épinière. De nombreuses améliorations du travail présenté sont cependant requises pour amener ces outils dans un contexte clinique et pour permettre d’améliorer notre compréhension des maladies affectant la moelle épinière. Les applications cliniques requièrent notamment l’amélioration de la robustesse et de l’automatisation des méthodes d’analyse d’images proposées. La caractérisation de la structure interne de la moelle épinière, incluant la matière blanche et la matière grise, présente en effet de grands défis, compte tenu de la qualité et la résolution des images IRM standard acquises en clinique. Les outils développés et validés au cours de ce projet ont un grand potentiel pour la compréhension et la caractérisation des maladies affectant la moelle épinière et aura un impact significatif sur la communauté de la neuroimagerie.----------ABSTRACT The spinal cord plays a fundamental role in the human body, as part of the central nervous system and being the vector between the brain and the peripheral nervous system. Damaging the spinal cord, through traumatic injuries or neurodegenerative diseases, can significantly affect the quality of life of patients. Indeed, spinal cord injuries and diseases can affect the integrity of neurons, and induce neurological impairments and/or functional disabilities. While various treatment procedures exist, assessing the extent of damages and understanding the underlying mechanisms of diseases would improve treatment efficiency and clinical decisions. Over the last decades, magnetic resonance imaging (MRI) has demonstrated a high potential for the diagnosis and prognosis of spinal cord injury and neurodegenerative diseases. Particularly, template-based analysis of brain MRI data has been very helpful for the understanding of neurological diseases, using automated analysis of large groups of patients. However, extracting MRI information within specific regions of the spinal cord with minimum bias and using automated tools is still a challenge. Indeed, only a limited number of MRI template of the spinal cord exists, and none covers the full spinal cord, thereby preventing large multi-centric template-based analysis of the spinal cord. Moreover, no template integrates both the spinal cord and the brain region, thereby preventing simultaneous cerebrospinal studies. The objective of this project was to propose a new MRI template of the full spinal cord, which allows simultaneous brain and spinal cord studies, that integrates atlases of the spinal cord internal structures (e.g., white and gray matter, white matter pathways) and that comes with tools for extracting information within these subregions. More particularly, the general research question of the project was “How to create generic MRI templates of the spinal cord that would enable unbiased and reproducible template-based analysis of spinal cord MRI data?”. Several original contributions have been made to answer this question and to enable template-based analysis of spinal cord MRI data. The first contribution was the development of the Spinal Cord Toolbox (SCT), a comprehensive and open-source software for processing multi-parametric MRI data of the spinal cord (De Leener, Lévy, et al., 2016). SCT includes tools for the automatic segmentation of the spinal cord and its internal structure (white and gray matter), vertebral labeling, registration of multimodal MRI data (structural and non-structural) on a spinal cord MRI template (initially the MNI-Poly-AMU template, later the PAM50 template), co-registration of spinal cord MRI images, as well as the robust extraction of MRI metric within specific regions of the spinal cord (i.e., white and gray matter, white matter tracts, gray matter subregions) and specific vertebral levels using a spinal cord atlas (Lévy et al., 2015). Additional tools include robust motion correction and image processing along the spinal cord. Each tool included in SCT has been validated on a multimodal dataset. The second contribution of this project was the development of a novel registration method dedicated to spinal cord images, with an interest in the straightening of the spinal cord, while preserving its topology (De Leener, Mangeat et al., 2017). This method is based on the global approximation of the spinal cord and the analytical computation of deformation fields perpendicular to the centerline. Validation included calculation of distance measurements after straightening on a population of healthy subjects and patients with spinal cord compression. The major contribution of this project was the development of a framework for generating MRI template of the spinal cord and the PAM50 template, an unbiased and symmetrical MRI template of the brainstem and full spinal cord. Based on 50 healthy subjects, the PAM50 template was generated using an iterative nonlinear registration process, after applying normalization and straightening of all images. Pre-processing included segmentation of the spinal cord, manual delineation of the brainstem anterior edge, detection and identification of intervertebral disks, and normalization of intensity along the spinal cord. Next, the average centerline and vertebral distribution was computed to create an initial straight template space. Then, all images were registered to the initial template space and an iterative nonlinear registration framework was applied to create the final symmetrical template. The PAM50 covers the brainstem and the full spinal cord, from C1 to L2, is available for T1-, T2- and T2*-weighted contrasts, and includes probabilistic maps of the white and the gray matter and atlases of the white matter pathways and gray matter subregions. Additionally, the PAM50 template has been merged with the ICBM152 brain template, thereby allowing for simultaneous cerebrospinal template-based analysis. Finally, several complementary results, focused on clinical validation and applications, are presented. First, a reproducibility and repeatability study of cross-sectional area measurements using SCT (De Leener, Granberg, Fink, Stikov, & Cohen-Adad, 2017) was performed on a Multiple Sclerosis population (n=9). The results demonstrated the high reproducibility and repeatability of SCT and its ability to detect very subtle atrophy of the spinal cord. Second, a novel biomarker of spinal cord injury has been proposed. Based on the T2*-weighted intensity ratio between the white and the gray matter, this new biomarker is computed by registering MRI images with the PAM50 template and extracting metrics using probabilistic atlases. Additionally, the feasibility of extracting multiparametric MRI metrics from subregions of the spinal cord has been demonstrated and the diagnostic potential of this approach has been assessed on a degenerative cervical myelopathy (DCM) population. Finally, a method for extracting shape morphometrics along the spinal cord has been proposed, including spinal cord flattening, indentation and torsion. These metrics demonstrated high capabilities for the diagnostic of asymptomatic spinal cord compression (AUC=99.8% for flattening, 99.3% for indentation, and 98.4% for torsion). The development of the PAM50 template enables unbiased template-based analysis of the spinal cord. However, the PAM50 template has several limitations. Indeed, the proposed template has been generated with multimodal MRI images from 50 healthy and young individuals (age = 27+/- 6.5 y.o.). Therefore, the template is specific to this particular population and could not be directly usable for age- or disease-specific populations. One solution is to open-source the templategeneration code so that research groups can generate and use their own spinal cord MRI template. The code is available on https://github.com/neuropoly/template. While this project introduced a generic referential coordinate system, based on vertebral levels and the pontomedullary junction as origin, one limitation is the choice of this coordinate system. Another coordinate system, based spinal segments would be more suitable for functional analysis. However, the acquisition of MRI images with high enough resolution to delineate the spinal roots is still challenging. Finally, several challenges in the automation of spinal cord MRI processing remains, including the robust detection and identification of vertebral levels, particularly in case of small fields-of-view. This project introduced key developments for the analysis of spinal cord MRI data. Many more developments are still required to bring them into clinics and to improve our understanding of diseases affecting the spinal cord. Indeed, clinical applications require the improvement of the robustness and the automation of the proposed processing and analysis tools. Particularly, the detection and segmentation of spinal cord structures, including vertebral labeling and white/gray matter segmentation, is still challenging, given the lowest quality and resolution of standard clinical MRI acquisition. The tools developed and validated here have the potential to improve our understanding and the characterization of diseases affecting the spinal cord and will have a significant impact on the neuroimaging community

The assessment of bone health in young women with childhood-onset type one diabetes mellitus

The risk of hip fracture in people with type one diabetes mellitus (T1DM) is reported to be 7 to 12 times greater than in those without T1DM, and this increased risk is evident in both children and young adults. This fracture risk is higher than expected bone mineral density (BMD) measurements, which indicates the likelihood that other skeletal factors, not captured by DXA, may contribute toward increased fracture risk. There is increasing evidence that alteration in trabecular bone microarchitecture and increased bone marrow adiposity (BMA) are causes for excess skeletal fragility, yet these data are lacking in people with T1DM. Recent technological advances in magnetic resonance imaging (MRI) have allowed the quantification of trabecular bone architecture. In addition, MRI can quantify the amount of intra-abdominal fat, and magnetic resonance spectroscopy (MRS) can also be used to assess BMA. These advances may enhance our understanding of the underlying causes of diabetic osteopathy which may lead to improved fracture risk predictors and preventive measures in patients with T1DM beyond that provided by dual energy x-ray absorptiometry (DXA). The overall objective of this thesis was to improve the understanding of the bone pathology of young adult women with childhood-onset T1DM by using high resolution MRI. A cross-sectional study was first carried out to assess trabecular bone microarchitecture of the tibia, vertebral BMA and abdominal adiposity in patients with childhood onset T1DM (n=30) compared with healthy controls (n=28). Additionally, the biochemical markers of bone turnover, adiposity and GH/IGF-1 axis (IGF-1, IGFBP3, and ALS) were examined to evaluate the underlying mechanism that might result in bone deficit in this group of people. We found that young women with childhood onset T1DM had reduced apparent trabecular bone volume (appBV/TV) and apparent trabecular number (appTbN) and greater apparent trabecular separation (appTbSp) than women without T1DM. Interestingly, these differences remained significant after adjustment for multiple confounders. Furthermore, these abnormalities were markedly obvious in those with microvascular complication compared with those without microvascular complication. Although women with T1DM had greater abdominal adiposity compared with healthy controls, there was no significant difference in BMA between the groups. However, BMA showed positive significant association with current glycaemic control (r= 0.45, p=0.02). Women with T1DM had lower bone turnover and decreased GH/IGF axis compared with healthy controls. Osteocalcin and ALS were negatively correlated with trabecular separation in women with T1DM. III Next, a one-year prospective study was conducted in a subset (n=28) of the participants involved in the cross-sectional study. The aim of this study was to compare one year changes in trabecular bone microarchitecture and BMA in women with and without T1DM. Additionally, the study aimed to evaluate the effect of glycaemic control on these changes over this period. After adjustment for relevant confounders, the cases (n=17) had a lower median appTbN and a higher median appTbSp at baseline and 12 months compared with healthy controls (n=11). Although the sample size was small at follow-up, the trabecular bone deficits were clearly noticeable in those with retinopathy compared with those without retinopathy. Similarly, there was no difference in median BMA which was 26.2% (12.1, 62.1) and 22.4% (9.6, 41.9) in cases and controls, respectively (p=0.57). Additionally, over the 12 month period, there was no significant change in MRI-measured parameters in cases or in controls, and no differences in the change of these variables between the two groups. Mixed model effect analysis showed that age was a negative predictor of percent changes of appBV/TV, appTbN and appTbSp in both cases and controls (p=0.02, p=0.02, p=0.002, respectively). Interestingly, there was a strong correlation between change in HbA1c and change in BMA (r=0.8; p=0.002). In the third study, we aimed to assess adiposity-based determinants of bone mineral density and bone microarchitecture in healthy young women and women with T1DM. Additionally, we aimed to compare the feasibility of using DXA and MRI-measured bone parameters to differentiate women with and without T1DM. In addition to high resolution MRI we used DXA scans to measure BMD and body composition from the same participants (n=26) involved in the longitudinal study. Vertebral BMA was positively correlated with VAT. Additionally, we demonstrated evidence of an inverse association of vertebral BMA and DXA-measured bone parameters of femoral neck, lumbar spine and total body independent of demographics and body composition in healthy young women and women with T1DM. These finding support the hypothesis that BMA is linked with low bone density, and may contribute to excess bone fragility. Moreover, this study suggested that MRI-measured trabecular bone measurements were able to differentiate between T1DM with and without microvascular complication compared with DXA-measured BMD. In summary, differences in MRI-measured trabecular microarchitecture parameters identified in this body of work provide preliminary explanations for elevated fracture risk in young women with childhood onset T1DM. Additionally, these findings provide potential insight into a number of possible underlying mechanisms of diabetic osteopathy