A chemo-mechano-biological modeling framework for cartilage evolving in health, disease, injury, and treatment.

BACKGROUND AND OBJECTIVE: Osteoarthritis (OA) is a pervasive and debilitating disease, wherein degeneration of cartilage features prominently. Despite extensive research, we do not yet understand the cause or progression of OA. Studies show biochemical, mechanical, and biological factors affect cartilage health. Mechanical loads influence synthesis of biochemical constituents which build and/or break down cartilage, and which in turn affect mechanical loads. OA-associated biochemical profiles activate cellular activity that disrupts homeostasis. To understand the complex interplay among mechanical stimuli, biochemical signaling, and cartilage function requires integrating vast research on experimental mechanics and mechanobiology-a task approachable only with computational models. At present, mechanical models of cartilage generally lack chemo-biological effects, and biochemical models lack coupled mechanics, let alone interactions over time. METHODS: We establish a first-of-its kind virtual cartilage: a modeling framework that considers time-dependent, chemo-mechano-biologically induced turnover of key constituents resulting from biochemical, mechanical, and/or biological activity. We include the "minimally essential" yet complex chemical and mechanobiological mechanisms. Our 3-D framework integrates a constitutive model for the mechanics of cartilage with a novel model of homeostatic adaptation by chondrocytes to pathological mechanical stimuli, and a new application of anisotropic growth (loss) to simulate degradation clinically observed as cartilage thinning. RESULTS: Using a single set of representative parameters, our simulations of immobilizing and overloading successfully captured loss of cartilage quantified experimentally. Simulations of immobilizing, overloading, and injuring cartilage predicted dose-dependent recovery of cartilage when treated with suramin, a proposed therapeutic for OA. The modeling framework prompted us to add growth factors to the suramin treatment, which predicted even better recovery. CONCLUSIONS: Our flexible framework is a first step toward computational investigations of how cartilage and chondrocytes mechanically and biochemically evolve in degeneration of OA and respond to pharmacological therapies. Our framework will enable future studies to link physical activity and resulting mechanical stimuli to progression of OA and loss of cartilage function, facilitating new fundamental understanding of the complex progression of OA and elucidating new perspectives on causes, treatments, and possible preventions.

Degeneration Affects Three-Dimensional Strains in Human Menisci: In situ MRI Acquisition Combined With Image Registration.

Degenerative changes of menisci contribute to the evolution of osteoarthritis in the knee joint, because they alter the load transmission to the adjacent articular cartilage. Identifying alterations in the strain response of meniscal tissue under compression that are associated with progressive degeneration may uncover links between biomechanical function and meniscal degeneration. Therefore, the goal of this study was to investigate how degeneration effects the three-dimensional (3D; axial, circumferential, radial) strain in different anatomical regions of human menisci (anterior and posterior root attachment; anterior and posterior horn; pars intermedia) under simulated compression. Magnetic resonance imaging (MRI) was performed to acquire image sequences of 12 mild and 12 severe degenerated knee joints under unloaded and loaded [25%, 50% and 100% body weight (BW)] conditions using a customized loading device. Medial and lateral menisci as well as their root attachments were manually segmented. Intensity-based rigid and non-rigid image registration were performed to obtain 3D deformation fields under the respective load levels. Finally, the 3D voxels were transformed into hexahedral finite-element models and direction-dependent local strain distributions were determined. The axial compressive strain in menisci and meniscal root attachments significantly increased on average from 3.1% in mild degenerated joints to 7.3% in severe degenerated knees at 100% BW (p ≤ 0.021). In severe degenerated knee joints, the menisci displayed a mean circumferential strain of 0.45% (mild: 0.35%) and a mean radial strain of 0.41% (mild: 0.37%) at a load level of 100% BW. No significant changes were observed in the circumferential or radial directions between mild and severe degenerated knee joints for all load levels (p > 0.05). In conclusion, high-resolution MRI was successfully combined with image registration to investigate spatial strain distributions of the meniscus and its attachments in response to compression. The results of the current study highlight that the compressive integrity of the meniscus decreases with progressing tissue degeneration, whereas the tensile properties are maintained.

Automatic segmentation of knee menisci - A systematic review.

Magnetic resonance imaging (MRI) has proved to be an invaluable component of pathogenesis research in osteoarthritis. Nevertheless, the detection of a meniscal lesion from magnetic resonance (MR) images is always challenging for both clinicians and researchers, because the surrounding tissues lead to similar signals within MR measurements, thus being difficult to discriminate. Moreover, the size and shape of osteoarthritic and non-osteoarthritic menisci vary to a large extent between individuals of same features, e.g. height, weight, age, etc. An effective way to visualize the entire volume of knee menisci is to segment the menisci voxels from the MR images, which is also useful to evaluate particular properties quantitatively. However, segmentation is a tedious and time-consuming task, and requires adequate training for being done properly. With the advancement of both MRI technology and computer methods, researchers have developed several algorithms to automate the task of meniscus segmentation of the individual knee during the last two decades. The objective of this systematic review was to present available fully automatic and semi-automatic segmentation methods of the knee meniscus published in different scientific articles according to the PRISMA statement. This review should provide a vivid description of the scientific advancements to clinicians and researchers in this field to help developing novel automated methods for clinical applications.