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

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

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

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

Modeling and visualization of medical anesthesiology acts

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaIn recent years, medical visualization has evolved from simple 2D images on a light board to 3D computarized images. This move enabled doctors to find better ways of planning surgery and to diagnose patients. Although there is a great variety of 3D medical imaging software, it falls short when dealing with anesthesiology acts. Very little anaesthesia related work has been done. As a consequence, doctors and medical students have had little support to study the subject of anesthesia in the human body. We all are aware of how costly can be setting medical experiments, covering not just medical aspects but ethical and financial ones as well. With this work we hope to contribute for having better medical visualization tools in the area of anesthesiology. Doctors and in particular medical students should study anesthesiology acts more efficiently. They should be able to identify better locations to administrate the anesthesia, to study how long does it take for the anesthesia to affect patients, to relate the effect on patients with quantity of anaesthesia provided, etc. In this work, we present a medical visualization prototype with three main functionalities: image pre-processing, segmentation and rendering. The image pre-processing is mainly used to remove noise from images, which were obtained via imaging scanners. In the segmentation stage it is possible to identify relevant anatomical structures using proper segmentation algorithms. As a proof of concept, we focus our attention in the lumbosacral region of the human body, with data acquired via MRI scanners. The segmentation we provide relies mostly in two algorithms: region growing and level sets. The outcome of the segmentation implies the creation of a 3D model of the anatomical structure under analysis. As for the rendering, the 3D models are visualized using the marching cubes algorithm. The software we have developed also supports time-dependent data. Hence, we could represent the anesthesia flowing in the human body. Unfortunately, we were not able to obtain such type of data for testing. But we have used human lung data to validate this functionality

Low Back Pain (LBP)

Low back pain (LBP) is a major public health problem, being the most commonly reported musculoskeletal disorder (MSD) and the leading cause of compromised quality of life and work absenteeism. Indeed, LBP is the leading worldwide cause of years lost to disability, and its burden is growing alongside the increasing and aging population. The etiology, pathogenesis, and occupational risk factors of LBP are still not fully understood. It is crucial to give a stronger focus to reducing the consequences of LBP, as well as preventing its onset. Primary prevention at the occupational level remains important for highly exposed groups. Therefore, it is essential to identify which treatment options and workplace-based intervention strategies are effective in increasing participation at work and encouraging early return-to-work to reduce the consequences of LBP. The present Special Issue offers a unique opportunity to update many of the recent advances and perspectives of this health problem. A number of topics will be covered in order to attract high-quality research papers, including the following major areas: prevalence and epidemiological data, etiology, prevention, assessment and treatment approaches, and health promotion strategies for LBP. We have received a wide range of submissions, including research on the physical, psychosocial, environmental, and occupational perspectives, also focused on workplace interventions

Segmentation of nerve bundles and ganglia in spine MRI using particle filters

14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part IIIAutomatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.National Institutes of Health (U.S.) (NAMIC award U54-EB005149)National Science Foundation (U.S.) (CAREER grant 0642971

Quantitative body shape analysis for obesity evaluation

Obesity is a public health concern as it is associated with a number of diseases, such as diabetes mellitus type 2, cardiovascular disease, some forms of renal failure and certain types of cancers. Growing evidence suggests that it is not only the amount of fat, but also its distribution in the body that is important to predict metabolic risk factors and adverse changes in organs. In this respect, it is necessary to develop convenient and inexpensive measures to characterize human body fat distribution and to investigate the unknown linkage between intrinsic adiposity and external body shape. This dissertation research aims to improve the obesity assessment by developing new quantitative measurements that comprehensively characterize body shape, and are highly relevant to intrinsic abdominal adiposity conditions. The proposed body shape descriptors were defined based on three-dimensional body images reconstructed from a custom-made stereovision body imaging system, which is particularly suitable for clinical use as an obesity monitoring equipment for its high portability and affordability. In this study, we developed a fully-automated algorithm to process T1-weighted magnetic resonance imaging (MRI) slices for abdominal adiposity measurements. This algorithm dramatically reduces the processing time and workload compared with traditional manual or semi-automatic methods for MRI processing, and greatly improves the repeatability and objectivity of fat assessments. A new obesity categorization method was then defined based on MRI adiposity data to depict characteristics of abdominal fat distribution, and the associations between the body shape descriptors and the MRI abdominal adiposity were explored. It was shown that the proposed body shape descriptors are able to capture the body shape differences between the subjects with dissimilar internal fat distribution (i.e., different categories), and to provide excellent prediction for the category of fat distribution through an optimized support-vector-machine classifier. The predictive models established in this dissertation demonstrate that the novel body shape descriptors were also effective for prediction of the volumes of abdominal visceral fat and subcutaneous fat accumulated in male and female adults. This dissertation introduces an innovative approach to assess obesity and fat distribution based on newly defined shape descriptors, and provides new findings that reveal the associations of intrinsic fat distribution with external body shapes, which enable both qualitative and quantitative assessment of obesity from body shape measurements.Biomedical Engineerin

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

Quantification of bone using a 3.0 tesla clinical magnetic resonance scanner

The work in this thesis examines the potential of using magnetic resonance imaging and spectroscopy (MRI & MRS) as a quantitative tool for diagnosing bone abnormalities at multiple skeletal sites, which could be used in conjunction with routine clinical imaging.MRI and MRS are routinely used in the clinical setting for the diagnosis of various types of diseases and abnormalities due to its advantages of providing excellent soft tissue contrast and also providing physiological and metabolic information. The use of MRI and MRS as a direct diagnostic tool for bone abnormalities is very limited at the moment due to issues of costs and standardisation. The aim in this thesis was to use the clinical 3.0 T MR scanner to acquire data from bone and bone marrow for identification of structural and chemical properties and to use those features to identify differences in bone strength and condition. The volunteers in this thesis were part of the high bone mass (HBM) study and they had additional acquisitions from dual-energy X-ray absorptiometry (DEXA) and peripheral quantitative computed tomography (pQCT).MR acquisition protocols have been successfully optimised for each type of bone region and in-house software has also been created to process the acquired data and quantify various types of structural and chemical properties.The MR data from distal radius and tibia demonstrated good correlation with pQCT data (e.g. Figure 8-2 & Figure 8-3) and were also able to differentiate between HBM-affected and control populations (e.g. Figure 8-26). The MR data from lumbar vertebrae also demonstrated good correlation with DEXA data and some of the measurements were also able to differentiate between the HBM-affected and control populations.The combined results from this thesis demonstrate that both MRI and MRS are sensitive techniques for measurement of bone quantity and quality, and they are ready to be applied for clinical investigation as part of routine clinical imaging to identify bone strength in relation to abnormalities and treatments

Adultes avec déformation rachidienne : traitement chirurgical et évaluation musculaire

Adult spinal deformity(ASD) refers to abnormal curvatures of the spine in patients who have completed their growth. Due to its prevalence, clinical impact, and the relatively high rate of surgical failures, they represent a therapeutic challenge. Research has been able to demonstrate that the preservation or the restoration of the sagittal alignment, are key objectives of surgical treatment. The objective of this thesis is to analyze the treatment of ASD patients, with particular interest in restoration of sagittal alignment and to develop tools to assess the spino-pelvic musculature of ASD patients. Based on an analysis of a multicenter database, the first two articles present an evaluation of the surgical treatment in term of clinical effectiveness and radiographic realignment. In addition, the discrepancies between surgical preoperative planning and operative execution have been studied with a prospective data collection, and have highlighted the necessity to understand better the role of the muscles in the maintaining of the posture. Therefore two methods for the characterization of the muscles involved in the sagittal alignment have been validated. Both methods are based on manual segmentation of specific MRI acquisition (Dixon methods) in order to obtain precise fat infiltration quantification in addition to muscular volume. One method permits to obtain 3D reconstruction able to generate patient–specific musculoskeletal model. The other one open the path to a clinical purpose, because necessitate only segmentation of four slices to obtain an relevant evaluation of the muscular system. Finally, thanks to the first protocol the muscular system of ASD patients have been described.Les déformations rachidiennes se réfèrent aux patients avec une courbure anormal de la colonne vertébrale qui ont terminé leur croissance. Par leur prévalence, leur impact clinique, et le taux relativement élevé d'échecs chirurgicaux, elles représentent un défi thérapeutique. La recherche a permis de démontrer que la préservation ou la restauration de l'alignement, sont des éléments clé du traitement chirurgical. L'objectif de cette thèse était d'analyser le traitement des patients avec DR, avec un intérêt particulier pour la restauration de l'alignement sagittal et l'évaluation musculaire. Fondé sur une analyse rétrospective d'une base de données multicentriques, les deux premiers articles présentent une évaluation du traitement chirurgical en termes d'efficacité clinique et de réalignement radiographique. Les écarts entre la planification préopératoire et l'exécution opérationnelle ont aussi été étudiés avec une collecte de données prospectives, et ont mis en évidence la nécessité de mieux comprendre le rôle des muscles dans le maintien de la posture. Par conséquent, deux protocoles pour la caractérisation des principaux muscles impliqués dans l'alignement sagittal ont été validés. Les deux méthodes sont basées sur la segmentation manuelle d'acquisition IRM spécifique (méthode de Dixon) afin d'obtenir l'infiltration graisseuse en plus du volume musculaire. Une des méthodes permet d'obtenir la reconstruction 3D des muscles et donc de générer des modèles musculo-squelettiques personnalisés. L'autre ouvre la voie à une pratique clinique car nécessite seulement la segmentation de quatre coupes pour obtenir une évaluation des principaux groupes musculaires. Enfin, à partir de la première méthode, le système musculaire de patients avec DR a été décrit