Statistical shape-kinematics models of the skeletal joints: Application to the shoulder complex

Patient-specific biomechanical simulations of joints require accurate reconstruction of bony anatomy from medical image data. The articular geometries of the joints may influence their biomechanics. Statistical shape models (SSMs) have become ubiquitous in the literature and aim to capture the natur...

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Veröffentlicht in:Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jg. 2019; S. 4815 - 4818
Hauptverfasser: Fouefack, Jean-Rassaire, Alemneh, Tewodros, Borotikar, Bhushan, Burdin, Valerie, Douglas, Tania S., Mutsvangwa, Tinashe
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.07.2019
Schlagworte:
Biological system modeling
Biomechanical Phenomena
Biomechanics
Humans
Joints
Kernel
Kinematics
Models, Biological
Models, Statistical
Scapula
Shape
Shoulder
Shoulder - physiopathology
Tomography, X-Ray Computed
ISSN:1557-170X, 2694-0604, 1558-4615, 2694-0604
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Zusammenfassung:Patient-specific biomechanical simulations of joints require accurate reconstruction of bony anatomy from medical image data. The articular geometries of the joints may influence their biomechanics. Statistical shape models (SSMs) have become ubiquitous in the literature and aim to capture the natural variation of biological objects. They work by learning the variation from training examples to define the space of valid biological shapes. However, the kinematic information descriptive of the anato-physiological relationship of two interacting objects is not generally encoded in the SSM. Here, we propose a framework for developing combined statistical shape and kinematics models (SSKMs) as Gaussian process morphable models to analyse the shape and kinematics relationship. We demonstrate the framework on a three-dimensional (3D) image data set consisting of ten right-handed cadaveric shoulder joints acquired using computed tomography. Additionally, we simulate specific bone motions to encode kinematics in the combined model. Our SSKM built from shoulder data (matching scapulae and humeri) correctly depicts a correlation between the shape and kinematics as hypothesized. We furthermore demonstrate the ability to marginalize from the SSKM to obtain shape-only variation and kinematics-only variation. Future work aims to use the SSKM framework to understand the relationships between kinematics and shape for various joints as well as to develop patient-specific computational models to evaluate joint biomechanics.
Bibliographie:ObjectType-Article-1
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ISSN:1557-170X
2694-0604
1558-4615
2694-0604
DOI:10.1109/EMBC.2019.8857528