Image-based biomechanical models of the musculoskeletal system

Abstract

Finite element modeling is a precious tool for the investigation of the biomechanics of the musculoskeletal system. A key element for the development of anatomically accurate, state-of-the art finite element models is medical imaging. Indeed, the workflow for the generation of a finite element model includes steps which require the availability of medical images of the subject of interest: segmentation, which is the assignment of each voxel of the images to a specific material such as bone and cartilage, allowing for a three-dimensional reconstruction of the anatomy; meshing, which is the creation of the computational mesh necessary for the approximation of the equations describing the physics of the problem; assignment of the material properties to the various parts of the model, which can be estimated for example from quantitative computed tomography for the bone tissue and with other techniques (elastography, T1rho, and T2 mapping from magnetic resonance imaging) for soft tissues. This paper presents a brief overview of the techniques used for image segmentation, meshing, and assessing the mechanical properties of biological tissues, with focus on finite element models of the musculoskeletal system. Both consolidated methods and recent advances such as those based on artificial intelligence are described.

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

“…SportRχiv preprint, doi: 10.31236/osf.io/w734a ing (done infrequently) vs monitoring (done often) variables used by some S&C coaches [667]. Some of the measures such as blood sampling and dual-energy X-ray absorptiometry (DXA) currently seen as testing measures might become monitoring measures with the introduction of lowcost and accurate future alternatives [252,303,333,456,462,633,814]. Some functional test measurements such as isometric mid-thigh pull (IMTP), might be replaced with something less physically demanding to make their use more frequent as a monitoring measure.…”

Finite element method in equine orthopedics

“…There are a large number of publications that have dedicated themselves to determining these properties in the most diverse types of biological tissues. Currently available imaging methods, such as computed tomography or magnetic resonance imaging, provide an assessment of bone architecture, which allows precise reconstruction of its geometries [12,13]. These imaging methods provide not only the external contours, but also the internal geometry of the anatomical structures.…”

“…From the DICOM iles (Digital Imaging and Communications in Medicine) generated by the imaging methods of the region of interest, we can apply software to convert them into a threedimensional ile. The following steps for constructing the inite element can be summarized in: segmentation and discretization, which is the attribution of each pixel/voxel of the images to a speci ic material (cortical or spongy bone, cartilage ...); three-dimensional reconstruction of the geometry of the various components of the model, followed by the application of enhancement ilters (surface smoothing); the creation of the computational mesh (geometry is subdivided into a large inite number of geometrically simple domains (elements) connected at its vertices (nodes)); and the attribution of material properties to the various parts of the model (Young's modulus and Poisson's coef icient) [12,16,18].…”

Intra-operative Update of Boundary Conditions for Patient-Specific Surgical Simulation

“…The predictive power of such PBMs highly relies on their accurate parametrization, which has to be tailored to each new patient. Patient-specific geometry and tissues' elastic properties can be extracted from pre-operative anatomical images or using ad-hoc modalities such as elastographic techniques, allowing to build a PBM with personalized properties [7]. However, information available before the intervention is often insufficient to fully characterize PBMs to the extent required to achieve clinically accepted accuracy.…”