Optimizing computational methods of modeling vertebroplasty in experimentally augmented human lumbar vertebrae

Deep learning-based spinal canal segmentation of computed tomography image for disease diagnosis: A proposed system for spinal stenosis diagnosis

BACKGROUND

Lumbar disc herniation was regarded as an age-related degenerative disease. Nevertheless, emerging reports highlight a discernible shift, illustrating the prevalence of these conditions among younger individuals.

METHODS

This study introduces a novel deep learning methodology tailored for spinal canal segmentation and disease diagnosis, emphasizing image processing techniques that delve into essential image attributes such as gray levels, texture, and statistical structures to refine segmentation accuracy.

RESULTS

Analysis reveals a progressive increase in the size of vertebrae and intervertebral discs from the cervical to lumbar regions. Vertebrae, bearing weight and safeguarding the spinal cord and nerves, are interconnected by intervertebral discs, resilient structures that counteract spinal pressure. Experimental findings demonstrate a lack of pronounced anteroposterior bending during flexion and extension, maintaining displacement and rotation angles consistently approximating zero. This consistency maintains uniform anterior and posterior vertebrae heights, coupled with parallel intervertebral disc heights, aligning with theoretical expectations.

CONCLUSIONS

Accuracy assessment employs 2 methods: IoU and Dice, and the average accuracy of IoU is 88% and that of Dice is 96.4%. The proposed deep learning-based system showcases promising results in spinal canal segmentation, laying a foundation for precise stenosis diagnosis in computed tomography images. This contributes significantly to advancements in spinal pathology understanding and treatment.

A Finite Element Model to Investigate the Stability of Osteochondral Grafts Within a Human Tibiofemoral Joint

Osteochondral grafting has demonstrated positive outcomes for treating articular cartilage defects by replacing the damaged region with a cylindrical graft consisting of bone with a layer of cartilage. However, factors that cause graft subsidence are not well understood. The aim of this study was to develop finite element (FE) models of osteochondral grafts within a tibiofemoral joint, suitable for an investigation of parameters affecting graft stability. Cadaveric femurs were used to experimentally calibrate the bone properties and graft-bone frictional forces for use in corresponding image-based FE models, generated from µCT scan data. Effects of cartilage defects and osteochondral graft repair were measured by examining contact pressure changes using further in vitro tests. Here, six defects were created in the femoral condyles, which were subsequently treated with osteochondral autografts or metal pins. Matching image-based FE models were created, and the contact patches were compared. The bone material properties and graft-bone frictional forces were successfully calibrated from the initial tests with good resulting levels of agreement (CCC = 0.87). The tibiofemoral joint experiment provided a range of cases that were accurately described in the resultant pressure maps and were well represented in the FE models. Cartilage defects and repair quality were experimentally measurable with good agreement in the FE model pressure maps. Model confidence was built through extensive validation and sensitivity testing. It was found that specimen-specific properties were required to accurately represent graft behaviour. The final models produced are suitable for a range of parametric testing to investigate immediate graft stability.

Development of robust finite element models to investigate the stability of osteochondral grafts within porcine femoral condyles

Osteoarthritis (OA) is the most prevalent chronic rheumatic disease worldwide with knee OA having an estimated lifetime risk of approximately 14%. Autologous osteochondral grafting has demonstrated positive outcomes in some patients, however, understanding of the biomechanical function and how treatments can be optimised remains limited. Increased short-term stability of the grafts allows cartilage surfaces to remain congruent prior to graft integration. In this study methods for generating specimen specific finite element (FE) models of osteochondral grafts were developed, using parallel experimental data for calibration and validation. Experimental testing of the force required to displace osteochondral grafts by 2 mm was conducted on three porcine knees, each with four grafts. Specimen specific FE models of the hosts and grafts were created from registered μCT scans captured from each knee (pre- and post-test). Material properties were based on the μCT background with a conversion between μCT voxel brightness and Young's modulus. This conversion was based on the results of the separate testing of eight porcine condyles and optimization of specimen specific FE models. The comparison between the experimental and computational push-in forces gave a strong agreement with a concordance correlation coefficient (CCC) = 0.75, validating the modelling approach. The modelling process showed that homogenous material properties based on whole bone BV/TV calculations are insufficient for accurate modelling and that an intricate description of the density distribution is required. The robust methodology can provide a method of testing different treatment options and can be used to investigate graft stability in full tibiofemoral joints.

Using Statistical Shape and Appearance Modelling to characterise the 3D shape and material properties of human lumbar vertebrae: A proof of concept study

Patient variation affects the outcomes of a range of spinal interventions, from disc replacement to vertebral fixation and vertebroplasty. Statistical Shape and Appearance Modelling (SSAM) can be used to describe anatomical variation and pathological differences within the population. To better understand how bone density and shape variation affect load transfer with respect to surgical treatments, Finite Element (FE) models can be generated from a SSAM. The aim for this study is to understand whether geometric and density variation as well as multiple vertebral levels can be incorporated into a single SSAM and whether this can be used to investigate the relationships between, and effects of, the various modes of variation. FE models of 14 human lumbar vertebrae that had been μCT imaged and validated through experimental testing were used as input specimens for a SSAM. The validity of the SSAM was evaluated by using principal component analysis to identify the primary modes of geometric and bone density variation and comparing to those in the input set. FE models were generated from the SSAM to examine the response to loading. The mean error between the input set and generated models for volume, mean density and FE compressive stiffness were 10%, 3% and 10% respectively. Principal Component (PC) 1 captured the majority of the bone density variation. The remaining PCs described specific geometric variation. The FE models generated from the SSAM showed the variations in vertebral stiffness as a result of complex relationships between bone density and shape. The SSAM created has limited data for its input set, however, it acts as a proof of concept for the novel combination of material and shape variation into a single shape model. This approach and the tools developed can be applied to wider patient groups and treatment scenarios to improve patient stratification and to optimise treatments.

Estimating tissue-level properties of porcine talar subchondral bone

Tissue-level properties of bone play an important role when characterising apparent-level bone biomechanical behaviour and yet little is known about its effect at this hierarchical level. In combination with trabecular morphological data these properties can be used to predict bone strength, which becomes an invaluable tool for clinicians in patient treatment planning. This study developed specimen-specific micro-finite element (μFE) models using validated continuum-level models, containing grayscale-derived material properties, to indirectly establish tissue-level properties of porcine talar subchondral bone. Specimen-specific continuum finite element (hFE) models of subchondral trabecular bone were setup using μCT data of ten cylindrical specimens extracted from juvenile porcine tali. The models were validated using quasi-static uniaxial compression testing. Validated hFE models were used to calibrate the tissue modulus of corresponding μFE models by minimising the difference between the μFE and hFE stiffness values. Key trabecular morphological indices (BV/TV, DA, Conn.D, Tb.Th, EF) were evaluated. Good agreement was observed between hFE models and experiment (CCC = 0.66). Calibrated E_tiss was 504 ± 37.65 MPa. Average BV/TV and DA for μFE specimens were 0.37 ± 0.05 and 0.68 ± 0.11, respectively. BV/TV (r² = 0.667) correlated highly with μFE stiffness. The small intra-specimen variation to tissue-level properties suggests that variations to apparent-level stiffness originate from variations to microarchitecture rather than tissue mechanical properties.