Toward New Assessment of Knee Cartilage Degeneration.

OBJECTIVE: Assessment of human joint cartilage is a crucial tool to detect and diagnose pathological conditions. This exploratory study developed a workflow for 3D modeling of cartilage and bone based on multimodal imaging. New evaluation metrics were created and, a unique set of data was gathered from healthy controls and patients with clinically evaluated degeneration or trauma. DESIGN: We present a novel methodology to evaluate knee bone and cartilage based on features extracted from magnetic resonance imaging (MRI) and computed tomography (CT) data. We developed patient specific 3D models of the tibial, femoral, and patellar bones and cartilages. Forty-seven subjects with a history of degenerative disease, traumatic events, or no symptoms or trauma (control group) were recruited in this study. Ninety-six different measurements were extracted from each knee, 78 2D and 18 3D measurements. We compare the sensitivity of different metrics to classify the cartilage condition and evaluate degeneration. RESULTS: Selected features extracted show significant difference between the 3 groups. We created a cumulative index of bone properties that demonstrated the importance of bone condition to assess cartilage quality, obtaining the greatest sensitivity on femur within medial and femoropatellar compartments. We were able to classify degeneration with a maximum recall value of 95.9 where feature importance analysis showed a significant contribution of the 3D parameters. CONCLUSION: The present work demonstrates the potential for improving sensitivity in cartilage assessment. Indeed, current trends in cartilage research point toward improving treatments and therefore our contribution is a first step toward sensitive and personalized evaluation of cartilage condition.

Healthy Aging Within an Image: Using Muscle Radiodensitometry and Lifestyle Factors to Predict Diabetes and Hypertension.

The strong age dependency of many deleterious health outcomes likely reflects the cumulative effects from a variety of risk and protective factors that occur over one's life course. This notion has become increasingly explored in the etiology of chronic disease and associated comorbidities in aging. Our recent work has shown the robust classification of individuals at risk for cardiovascular pathophysiology using CT-based soft tissue radiodensity parameters obtained from nonlinear trimodal regression analysis (NTRA). Past and present lifestyle influences the incidence of comorbidities like hypertension (HTN), diabetes (DM) and cardiac diseases. 2,943 elderly subjects from the AGES-Reykjavik study were sorted into a three-level binary-tree structure defined by: 1) lifestyle factors (smoking and self-reported physical activity level), 2) comorbid HTN or DM, and 3) cardiac pathophysiology. NTRA parameters were extracted from mid-thigh CT cross-sections to quantify radiodensitometric changes in three tissue types: lean muscle, fat, and loose-connective tissue. Between-group differences were assessed at each binary-tree level, which were then used in tree-based machine learning (ML) models to classify subjects with DM or HTN. Classification scores for detecting HTN or DM based on lifestyle factors were excellent (AUCROC: 0.978 and 0.990, respectively). Finally, tissue importance analysis underlined the comparatively-high significance of connective tissue parameters in ML classification, while predictive models of DM onset from five-year longitudinal data gave a classification accuracy of 94.9%. Altogether, this work serves as an important milestone toward the construction of predictive tools for assessing the impact of lifestyle factors and healthy aging based on a single image.

Patient-specific mobility assessment to monitor recovery after total hip arthroplasty.

Total hip arthroplasty is a ubiquitously successful orthopedic surgical procedure, whose prevalence is rising worldwide. While many investigations focus on characterizing periprosthetic pathophysiology, the objective of our research is to develop and describe multi-metric assemblies as a first step toward creating a patient-specific mobility index that rehabilitators and orthopedic surgeons can utilize for prescribing their respective procedures. In total, 48 total hip arthroplasty patients (both cemented and uncemented) undergoing unilateral, primary surgery went through computed tomographic scans and gait analysis measurements both before and 1 year following their surgery. Altogether, the reported quantitative metrics include 11 spatial and temporal gait parameters, muscle density, and electromyography signals from the rectus femoris, vastus lateralis, and vastus medialis, and bone mineral density values from bioimage analysis around the implant stem. We found that measured parameters from a subgroup were sensitive to changes observed during patient recovery, implicating the predictive sensitivity of these patient conditions. Most post-operative gait parameters changed significantly, while electromyography data indicated few significant differences. Moreover, results from bioimage analyses indicate a general reduction of periprosthetic bone mineral density after 1 year, in association with increasing density of the quadriceps muscles. Furthermore, this work identifies which quantitative metrics undergo the greatest variation after total hip arthroplasty and demonstrates the clinical feasibility of a multimodal approach to mobility assessment that may ultimately support decision-making for post-surgical rehabilitation protocols.

Patient-specific bone mineral density distribution in the tibia of individuals with chronic spinal cord injury, derived from multi-slice peripheral Quantitative Computed Tomography (pQCT) - A cross-sectional study.

BACKGROUND: The high risk of fracture associated with chronic spinal cord injury (SCI) is attributed to extensive disuse-related bone loss in previously weight-bearing long bones. Changes in bone mineral density (BMD) after SCI have been documented extensively for the epiphyses of the tibia and femur, fracture-prone sites in this patient group. Less attention has been given to patterns of cortical bone loss in the diaphyses, but variability in BMD distributions throughout the long bones may contribute to some patients' increased susceptibility to shaft fractures in chronic SCI. AIM: A cross-sectional study was carried out to determine whether BMD distributions along the tibia differ between individuals with chronic SCI and healthy able-bodied (AB) controls, in both the trabecular and cortical bone compartments. The effects of time post-injury and gender on BMD distribution were also explored. METHODS: Individuals with chronic (≥6months post-injury) motor-complete SCI were recruited from the Queen Elizabeth National Spinal Injuries Unit (Glasgow, UK). AB control subjects were recruited to achieve similar age and gender profiles for the SCI and control groups. Multi-slice pQCT (XCT3000, Stratec) was performed along the length of the tibia (2mm thickness, 0.5mm voxel size), at 1% intervals in the epiphyses and 5% intervals in the diaphysis (34 slices in total). These were used to reconstruct full 3-D subject-specific models (Mimics, Materialise) of BMD distribution, by interpolating between slices. Subjects with chronic SCI were subdivided into 'early' (<4years post-injury) and 'established' SCI (≥4years post-injury). Subject-specific BMD distribution was described according to new parameters determined from the 3-D patient-specific models, quantifying descriptors of the trabecular and cortical BMD regions separately (volume, peak BMD, half-peak width, area under the curve). These were compared between sub-groups (using independent-samples t-tests or Mann-Whitney tests, significance level of 5%). RESULTS: 11 men (age range 17-59years old; mean 35.7±10.6) and 3 post-menopausal women (age range 56-58years old; mean 56.7±1.2years) with motor-complete SCI (ranging from 6months to 27years post-injury) were recruited; 6 men (age range 20-56years old; 33.0±12.7years) and 1 post-menopausal woman (56years) formed the AB control group. Overall, SCI resulted in lower BMD at both trabecular and cortical regions of the tibia. In men, longer time since injury resulted in greater BMD differences when compared to AB, throughout the tibia. For the post-menopausal women, differences in BMD between SCI and AB were greater in cortical bone than in trabecular bone. From the models, individual BMD distribution curves showed healthy double-peaks in AB subjects: one trabecular peak (around 200-300mg/cm3) and the other cortical (around 1000-1100mg/cm3). In most subjects with established SCI, trabecular peaks were exaggerated whilst the cortical peaks were barely discernible, with crucially some individuals already exhibiting a diminishing cortical BMD peak even <4years post-injury. CONCLUSIONS: These findings may have implications for determining the fracture susceptibility of the long bones in individual patients with SCI. Epiphyseal fractures associated with low trabecular BMD are well characterised, but our data show that some individuals with SCI may also be at higher risk of shaft fractures. The proposed BMD distribution description parameters, determined from patient-specific models, could be used to identify patients with a weakened diaphysis who may be susceptible to fractures of the tibial shaft, but this requires validation.

Prediction of risk of fracture in the tibia due to altered bone mineral density distribution resulting from disuse: a finite element study.

The disuse-related bone loss that results from immobilisation following injury shares characteristics with osteoporosis in post-menopausal women and the aged, with decreases in bone mineral density leading to weakening of the bone and increased risk of fracture. The aim of this study was to use the finite element method to: (i) calculate the mechanical response of the tibia under mechanical load and (ii) estimate of the risk of fracture; comparing between two groups, an able-bodied group and spinal cord injury patients group suffering from varying degrees of bone loss. The tibiae of eight male subjects with chronic spinal cord injury and those of four able-bodied age-matched controls were scanned using multi-slice peripheral quantitative computed tomography. Images were used to develop full three-dimensional models of the tibiae in Mimics (Materialise) and exported into Abaqus (Simulia) for calculation of stress distribution and fracture risk in response to specified loading conditions - compression, bending and torsion. The percentage of elements that exceeded a calculated value of the ultimate stress provided an estimate of the risk of fracture for each subject, which differed between spinal cord injury subjects and their controls. The differences in bone mineral density distribution along the tibia in different subjects resulted in different regions of the bone being at high risk of fracture under set loading conditions, illustrating the benefit of creating individual material distribution models. A predictive tool can be developed based on these models, to enable clinicians to estimate the amount of loading that can be safely allowed onto the skeletal frame of individual patients who suffer from extensive musculoskeletal degeneration (including spinal cord injury, multiple sclerosis and the ageing population). The ultimate aim is to reduce fracture occurrence in these vulnerable groups.