Statistical shape modelling of the first carpometacarpal joint reveals high variation in morphology

The first carpometacarpal (CMC) joint, located at the base of the thumb and formed by the junction between the first metacarpal and trapezium, is a common site for osteoarthritis of the hand. The shape of both the first metacarpal and trapezium contributes to the intrinsic bony stability of the jointandvariability in the morphology of both these bones can affect the joint’s function. The objectivesof this study wereto quantify the morphological variation of the complete metacarpal and trapeziumand determine anycorrelation between anatomical features ofthese two components of the first CMC joint. A multi-object statistical shape modelling pipeline, consisting of scaling, hierarchical rigid registration, non-rigid registration and projection pursuit principal component analysis, was implemented. Four anatomical measureswere quantified from the shape model, namely the first metacarpal articular tilt and torsion angles and the trapeziumlength and width.Variationsin the first metacarpal articulartilt angle (-6.3°<θ<12.3°) and trapezium width (10.28mm <<11.13mm)wereidentified in the firstprincipal component. In the second principal component, variationsin the first metacarpal14torsion angle (0.2°<α<14.2°), first metacarpal articular tilt angle (1.0°<θ<6.4°) and trapezium length (12.25mm <ℓ<17.33mm)weredetermined. Due to their implications for joint stability, the first metacarpal articular tilt angle and trapezium width maybe important anatomical features which couldbe used toadvance early detectionand treatment offirst CMC joint osteoarthritis

DNSS: Dual-Normal-Space Sampling for 3-D ICP Registration

Rigid registration is a fundamental process in many applications that require alignment of different datasets. Iterative closest point (ICP) is a widely used algorithm that iteratively finds point correspondences and updates the rigid transformation. One of the key variants of ICP to its success is the selection of points, which is directly related to the convergence and robustness of the ICP algorithm. Besides uniform sampling, there are a number of normal-based and feature-based approaches that consider normal, curvature, and/or other signals in the point selection. Among them, normal-space sampling (NSS) is one of the most popular techniques due to its simplicity and low computational cost. The rationale of NSS is to sample enough constraints to determine all the components of transformation, but this paper finds that NSS actually can constrain the translational normal space only. This paper extends the fundamental idea of NSS and proposes Dual NSS (DNSS) to sample points in both translational and rotational normal spaces. Compared with NSS, this approach has similar simplicity and efficiency without any need of additional information, but has a much better effectiveness. Experimental results show that DNSS can outperform the normal-based and feature-based methods in terms of convergence and robustness. For example, DNSS can achieve convergence from an orthogonal initial position while no other methods can achieve. Note to Practitioners-ICP is commonly used to align different data to a same coordination system. While NSS is often used to speed up the alignment process by down-sampling the data uniformly in the normal space. The implementation of NSS only has three steps: 1) construct a set of buckets in the normal-space; 2) put all points of the data into buckets based on their normal direction; and 3) uniformly pick points from all the buckets until the desired number of points is selected. The algorithm is simple and fast, so that it is still the common practice. However, the weakness of NSS comes from the reason that it cannot handle rotational uncertainties. In this paper, a new algorithm called DNSS is developed to constrain both translation and rotation at the same time by introducing a dual-normal space. With a new definition of the normal space, the algorithm complexity of DNSS is the same as that of NSS, and it can be readily implemented in all types of application that are currently using ICP. The experimental results show that DNSS has better efficiency, quality, and reliability than both normal-based and feature-based methods have

Statistical shape modelling of the thoracic spine for the development of pedicle screw insertion guides

Spinal fixation and fusion are surgical procedures undertaken to restore stability in the spine and restrict painful or degenerative motion. Malpositioning of pedicle screws during these procedures can result in major neurological and vascular damage. Patient-specific surgical guides offer clear benefits, reducing malposition rates by up to 25%. However, they suffer from long lead times and the manufacturing process is dependent on third-party specialists. The development of a standard set of surgical guides may eliminate the issues with the manufacturing process. To evaluate the feasibility of this option, a statistical shape model (SSM) was created and used to analyse the morphological variations of the T4–T6 vertebrae in a population of 90 specimens from the Visible Korean Human dataset (50 females and 40 males). The first three principal components, representing 39.7% of the variance within the population, were analysed. The model showed high variability in the transverse process (~ 4 mm) and spinous process (~ 4 mm) and relatively low variation (< 1 mm) in the vertebral lamina. For a Korean population, a standardised set of surgical guides would likely need to align with the lamina where the variance in the population is lower. It is recommended that this standard set of surgical guides should accommodate pedicle screw diameters of 3.5–6 mm and transverse pedicle screw angles of 3.5°–12.4°