Validity and reliability of 3D marker based scapular motion analysis : a systematic review

Methods based on cutaneous markers are the most popular for the recording of three dimensional scapular motion analysis. Numerous methods have been evaluated, each showing different levels of accuracy and reliability. The aim of this review was to report the metrological properties of 3D scapular kinematic measurements using cutaneous markers and to make recommendations based on metrological evidence. A database search was conducted using relevant keywords and inclusion/exclusion criteria in 5 databases. 19 articles were included and assessed using a quality score. Concurrent validity and reliability were analyzed for each method. Six different methods are reported in the literature, each based on different marker locations and post collection computations. The acromion marker cluster (AMC) method coupled with a calibration of the scapula with the arm at rest is the most studied method. Below 90–100° of humeral elevation, this method is accurate to about 5° during arm flexion and 7° during arm abduction compared to palpation (average of the 3 scapular rotation errors). Good to excellent within-session reliability and moderate to excellent between-session reliability have been reported. The AMC method can be improved using different or multiple calibrations. Other methods using different marker locations or more markers on the scapula blade have been described but are less accurate than AMC methods. Based on current metrological evidence we would recommend (1) the use of an AMC located at the junction of the scapular spine and the acromion, (2) the use of a single calibration at rest if the task does not reach 90° of humeral elevation, (3) the use of a second calibration (at 90° or 120° of humeral elevation), or multiple calibrations above 90° of humeral elevation

Towards a framework for multi class statistical modelling of shape, intensity, and kinematics in medical images

Statistical modelling has become a ubiquitous tool for analysing of morphological variation of bone structures in medical images. For radiological images, the shape, relative pose between the bone structures and the intensity distribution are key features often modelled separately. A wide range of research has reported methods that incorporate these features as priors for machine learning purposes. Statistical shape, appearance (intensity profile in images) and pose models are popular priors to explain variability across a sample population of rigid structures. However, a principled and robust way to combine shape, pose and intensity features has been elusive for four main reasons: 1) heterogeneity of the data (data with linear and non-linear natural variation across features); 2) sub-optimal representation of three-dimensional Euclidean motion; 3) artificial discretization of the models; and 4) lack of an efficient transfer learning process to project observations into the latent space. This work proposes a novel statistical modelling framework for multiple bone structures. The framework provides a latent space embedding shape, pose and intensity in a continuous domain allowing for new approaches to skeletal joint analysis from medical images. First, a robust registration method for multi-volumetric shapes is described. Both sampling and parametric based registration algorithms are proposed, which allow the establishment of dense correspondence across volumetric shapes (such as tetrahedral meshes) while preserving the spatial relationship between them. Next, the framework for developing statistical shape-kinematics models from in-correspondence multi-volumetric shapes embedding image intensity distribution, is presented. The framework incorporates principal geodesic analysis and a non-linear metric for modelling the spatial orientation of the structures. More importantly, as all the features are in a joint statistical space and in a continuous domain; this permits on-demand marginalisation to a region or feature of interest without training separate models. Thereafter, an automated prediction of the structures in images is facilitated by a model-fitting method leveraging the models as priors in a Markov chain Monte Carlo approach. The framework is validated using controlled experimental data and the results demonstrate superior performance in comparison with state-of-the-art methods. Finally, the application of the framework for analysing computed tomography images is presented. The analyses include estimation of shape, kinematic and intensity profiles of bone structures in the shoulder and hip joints. For both these datasets, the framework is demonstrated for segmentation, registration and reconstruction, including the recovery of patient-specific intensity profile. The presented framework realises a new paradigm in modelling multi-object shape structures, allowing for probabilistic modelling of not only shape, but also relative pose and intensity as well as the correlations that exist between them. Future work will aim to optimise the framework for clinical use in medical image analysis

PREDICTION OF RESPIRATORY MOTION

Radiation therapy is a cancer treatment method that employs high-energy radiation beams to destroy cancer cells by damaging the ability of these cells to reproduce. Thoracic and abdominal tumors may change their positions during respiration by as much as three centimeters during radiation treatment. The prediction of respiratory motion has become an important research area because respiratory motion severely affects precise radiation dose delivery. This study describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. In the first part of our study we review three prediction approaches of respiratory motion, i.e., model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the second part of our work we propose respiratory motion estimation with hybrid implementation of extended Kalman filter. The proposed method uses the recurrent neural network as the role of the predictor and the extended Kalman filter as the role of the corrector. In the third part of our work we further extend our research work to present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the fourth part of our work we retrospectively categorize breathing data into several classes and propose a new approach to detect irregular breathing patterns using neural networks. We have evaluated the proposed new algorithm by comparing the prediction overshoot and the tracking estimation value. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier

Simulation morpho-fonctionnelle et indices temporels quantifiés de cohérence articulaire. Application à la qualité de mouvements réels et simulés

The muscoloskeletal system is the subject of several studies, on the one hand to increase the basic medical knowledge, on the other hand for morphological or functional parameters to be taken into account as part of routine clinical rehabilitation or protocols of navigated surgery. The main objective of this thesis is to better describe and quantify the behavior of joint reports during a movement. We have decided to describe the joint by the bias of the geometry adopting morpho-functional concept that links the morphology of the joint surface to the function of the joint. We proposed an original kinematic modeling of the movement of flexion/extension of the knee based solely on the 3D model of the joint obtained by CT scan or MRI. This model is based mainly on the assumption that the knee does not have a single fixed axis of rotation passing through the condyles but an axis of rotation which varies during movement . The geometric approach is also the basis of our method to qualify and quantify joint's congruence during movement. Thus to quality a motion we performed a time analysis of the relative positions of the bones in the joint looking specifically temporal distributions of distance between the articular surfaces . All these temporal distributions are grouped on the original graph called Figure of Articular Coherence (FoAC). To quantify the observations related to this qualitative tool (FoAC) we completed the implementation of a second original descriptor : the Index of articular Articular (IoAC). These tools are information carriers such as the presence of collision or dislocation during movement and were used as well to account for the quality of a joint motion or for comparing different surgery protocols. The description of joint have been treated from the point of view of the kinematic, this work could be coupled with dynamic models taking into account external forces and constraints imposed by muscles and ligaments.L'appareil locomoteur humain fait l'objet d'un grand nombre d'études, d'une part pour augmenter la connaissance médicale fondamentale, d'autre part pour obtenir des paramètres morphologiques ou fonctionnels à prendre en compte dans le cadre de routines cliniques de rééducation ou de protocoles de chirurgie naviguée. L'objectif principal de ces travaux de thèse est de mieux décrire et quantifier le comportement des rapports articulaires au cours d'un mouvement. La description a été menée du point de vue de la géométrie des structures osseuses en adoptant le concept morpho-fonctionnel qui unit la morphologie de la surface articulaire à la fonction de l'articulation. Nous avons ainsi proposé une modélisation cinématique originale du mouvement de flexion/extension du genou en nous basant uniquement sur le modèle 3D de l'articulation obtenu par le biais d'imageurs IRM ou Scanner. Cette modélisation repose principalement sur l'hypothèse que le genou ne possède pas un seul axe fixe de rotation passant par les extrémités des condyles mais un axe de rotation qui varie au cours du mouvement. L'approche géométrique est également à la base de notre méthode de quantification des rapports articulaires au cours du mouvement. Ainsi pour rendre compte de la qualité d'un mouvement nous avons effectué une analyse temporelle des positions relatives des os dans l'articulation en regardant plus précisément les distributions temporelles des distances entre les surfaces articulaires. L'ensemble de ces distributions temporelles sont regroupées sur un graphique original appelé Figure de Cohérence Articulaire (FoAC). Afin de quantifier les observations liées à cet outil qualitatif (FoAC) nous l¿avons complété par la mise en place d¿un deuxième descripteur original l'Indice de Cohérence Articulaire (IoAC). Ces outils sont porteurs d'informations telles que la présence de collision ou de dislocation au cours du mouvement et ont été utilisés aussi bien pour rendre compte de la qualité d'un mouvement articulaire que pour comparer différents protocoles d'acquisitions cinématiques ou différents protocoles chirurgicaux. La description des rapports articulaires ayant été traitée du point de vue de la cinématique, ces travaux pourront être couplés à des modèles dynamiques tenant compte aussi bien des forces extérieures que des contraintes imposées par les muscles et les ligaments

Building and tracking root shapes.

An algorithm aiming at robust and simultaneous registrations of a sequence of 3-D shapes was recently presented by Jacq et al. [IEEE Trans. Biomed. Eng., vol. 55, no. 5, 2008]. This algorithm has to carry out an implicit representation of their common root shape (RS). A particular emphasis was put on the median consensus shape, which is a specific type of RS. Unlike this previous study, mainly focusing on the algorithm foundations while dealing with very specific applications examples, this paper attempts to show the versatility of the RS concept through a set of three problems involving a wider scope of application. The first problem copes with the design of prosthetic cortical plates for the hip joint. It shows how an explicit reconstruction of the RS, coming with its consensus map, could bring out an intermediary anatomical support from which pragmatic choices could be made, thereby performing a tradeoff between morphological, surgical, and production considerations. The second problem addresses in vivo real-time shoulder biomechanics through a miniature 3-D video camera. This new protocol implicitly operates through RS tracking of the content of virtual spotlights. It is shown that the current medical-oriented protocol, while operating within expert offices through low-cost equipments, could challenge high-end professional equipments despite some limitations of the 3-D video cameras currently available. The last problem deals with respiratory motions. This is an auxiliary measurement required by some medical imaging systems that can be handled as a basic application case of the former new protocol