Soft tissue structure modelling for use in orthopaedic applications and musculoskeletal biomechanics

We present our methodology for the three-dimensional anatomical and geometrical description of soft tissues, relevant for orthopaedic surgical applications and musculoskeletal biomechanics. The technique involves the segmentation and geometrical description of muscles and neurovascular structures from high-resolution computer tomography scanning for the reconstruction of generic anatomical models. These models can be used for quantitative interpretation of anatomical and biomechanical aspects of different soft tissue structures. This approach should allow the use of these data in other application fields, such as musculoskeletal modelling, simulations for radiation therapy, and databases for use in minimally invasive, navigated and robotic surgery

Multiple 2D self organising map network for surface reconstruction of 3D unstructured data

Surface reconstruction is a challenging task in reverse engineering because it must represent the surface which is similar to the original object based on the data obtained. The data obtained are mostly in unstructured type whereby there is not enough information and incorrect surface will be obtained. Therefore, the data should be reorganised by finding the correct topology with minimum surface error. Previous studies showed that Self Organising Map (SOM) model, the conventional surface approximation approach with Non Uniform Rational B-Splines (NURBS) surfaces, and optimisation methods such as Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimisation (PSO) methods are widely implemented in solving the surface reconstruction. However, the model, approach and optimisation methods are still suffer from the unstructured data and accuracy problems. Therefore, the aims of this research are to propose Cube SOM (CSOM) model with multiple 2D SOM network in organising the unstructured surface data, and to propose optimised surface approximation approach in generating the NURBS surfaces. GA, DE and PSO methods are implemented to minimise the surface error by adjusting the NURBS control points. In order to test and validate the proposed model and approach, four primitive objects data and one medical image data are used. As to evaluate the performance of the proposed model and approach, three performance measurements have been used: Average Quantisation Error (AQE) and Number Of Vertices (NOV) for the CSOM model while surface error for the proposed optimised surface approximation approach. The accuracy of AQE for CSOM model has been improved to 64% and 66% when compared to 2D and 3D SOM respectively. The NOV for CSOM model has been reduced from 8000 to 2168 as compared to 3D SOM. The accuracy of surface error for the optimised surface approximation approach has been improved to 7% compared to the conventional approach. The proposed CSOM model and optimised surface approximation approach have successfully reconstructed surface of all five data with better performance based on three performance measurements used in the evaluation

Tissue thickness measurement tool for craniofacial reconstruction

Craniofacial Reconstruction is a method of recreating the appearance of the face on the skull of a deceased individual for identification purposes. Older clay methods of reconstruction are inaccurate, time consuming and inflexible. The tremendous increase in the processing power of the computers and rapid strides in visualization can be used to perform the reconstruction, saving time and providing greater accuracy and flexibility, without the necessity for a skillful modeler.;This thesis introduces our approach to computerized 3D craniofacial reconstruction. Three phases have been identified. The first phase of the project is to generate a facial tissue thickness database. In the second phase this database along with a 3D facial components database is to be used to generate a generic facial mask which is draped over the skull to recreate the facial appearance. This face is to be identified from a database of images in the third phase.;Tissue thickness measurements are necessary to generate the facial model over the skull. The thesis emphasis is on the first phase of the project. An automated facial tissue thickness measurement tool (TTMT) has been developed to populate this database

USER DEFINED GEOMETRIC FEATURE FOR THE CREATION OF THE FEMORAL NECK ENVELOPING SURFACE

There is a growing demand for application of personalized bone implants (endoprostheses or macro-scaffolds, and fixators) which conform the anatomy of patient. Hence the need for a CAD procedure that enables fast and sufficiently accurate digital reconstruction of the traumatized bone geometry. Research presented in this paper addresses digital reconstruction of the femoral neck fracture. The results point out that User-Defined (geometric) Feature (UDF) concept is the most convenient to use in digital reconstruction of numerous variants of the same topology, such as in this kind of bone region. UDF, named FemoNeck, is developed to demonstrate capability of the chosen concept. Its geometry, controlled by a dozen of parameters, can be easily shaped according to anatomy of femoral neck region of the specific patient. That kind of CAD procedure should use minimally required set of geometric (anatomical) parameters, which can be easily captured from X-ray or Computed Tomography (CT) images. For the statistical analysis of geometry and UDF development we used CT scans of proximal femur of 24 Caucasian female and male adults. The validation of the proposed method was done by applying it for remodeling four femoral necks of four different proximal femurs and by comparing the geometrical congruency between the raw polygonal models gained directly from CT scan and reconstructed models

Study of medical image data transformation techniques and compatibility analysis for 3D printing

Various applications exist for additive manufacturing (AM) and reverse engineering (RE) within the medical sector. One of the significant challenges identified in the literature is the accuracy of 3D printed medical models compared to their original CAD models. Some studies have reported that 3D printed models are accurate, while others claim the opposite. This thesis aims to highlight the medical applications of AM and RE, study medical image reconstruction techniques into a 3D printable file format, and the deviations of a 3D printed model using RE. A case study on a human femur bone was conducted through medical imaging, 3D printing, and RE for comparative deviation analysis. In addition, another medical application of RE has been presented, which is for solid modelling. Segmentation was done using opensource software for trial and training purposes, while the experiment was done using commercial software. The femur model was 3D printed using an industrial FDM printer. Three different non-contact 3D scanners were investigated for the RE process. Post-processing of the point cloud was done in the VX Elements software environment, while mesh analysis was conducted in MeshLab. The scanning performance was measured using the VX Inspect environment and MeshLab. Both relative and absolute metrics were used to determine the deviation of the scanned models from the reference mesh. The scanners' range of deviations was approximately from -0.375 mm to 0.388 mm (range of about 0.763mm) with an average RMS of about 0.22 mm. The results showed that the mean deviation of the 3D printed model (based on 3D scanning) has an average range of about 0.46mm, with an average mean value of about 0.16 mm

Computational Techniques to Predict Orthopaedic Implant Alignment and Fit in Bone

Among the broad palette of surgical techniques employed in the current orthopaedic practice, joint replacement represents one of the most difficult and costliest surgical procedures. While numerous recent advances suggest that computer assistance can dramatically improve the precision and long term outcomes of joint arthroplasty even in the hands of experienced surgeons, many of the joint replacement protocols continue to rely almost exclusively on an empirical basis that often entail a succession of trial and error maneuvers that can only be performed intraoperatively. Although the surgeon is generally unable to accurately and reliably predict a priori what the final malalignment will be or even what implant size should be used for a certain patient, the overarching goal of all arthroplastic procedures is to ensure that an appropriate match exists between the native and prosthetic axes of the articulation. To address this relative lack of knowledge, the main objective of this thesis was to develop a comprehensive library of numerical techniques capable to: 1) accurately reconstruct the outer and inner geometry of the bone to be implanted; 2) determine the location of the native articular axis to be replicated by the implant; 3) assess the insertability of a certain implant within the endosteal canal of the bone to be implanted; 4) propose customized implant geometries capable to ensure minimal malalignments between native and prosthetic axes. The accuracy of the developed algorithms was validated through comparisons performed against conventional methods involving either contact-acquired data or navigated implantation approaches, while various customized implant designs proposed were tested with an original numerical implantation method. It is anticipated that the proposed computer-based approaches will eliminate or at least diminish the need for undesirable trial and error implantation procedures in a sense that present error-prone intraoperative implant insertion decisions will be at least augmented if not even replaced by optimal computer-based solutions to offer reliable virtual “previews” of the future surgical procedure. While the entire thesis is focused on the elbow as the most challenging joint replacement surgery, many of the developed approaches are equally applicable to other upper or lower limb articulations

Improving the forward model for electrical impedance tomography of brain function through rapid generation of subject specific finite element models

Electrical Impedance Tomography (EIT) is a non-invasive imaging method which allows internal electrical impedance of any conductive object to be imaged by means of current injection and surface voltage measurements through an array of externally applied electrodes. The successful generation of the image requires the simulation of the current injection patterns on either an analytical or a numerical model of the domain under examination, known as the forward model, and using the resulting voltage data in the inverse solution from which images of conductivity changes can be constructed. Recent research strongly indicates that geometric and anatomical conformance of the forward model to the subject under investigation significantly affects the quality of the images. This thesis focuses mainly on EIT of brain function and describes a novel approach for the rapid generation of patient or subject specific finite element models for use as the forward model. After introduction of the topic, methods of generating accurate finite element (FE) models using commercially available Computer-Aided Design (CAD) tools are described and show that such methods, though effective and successful, are inappropriate for time critical clinical use. The feasibility of warping or morphing a finite element mesh as a means of reducing the lead time for model generation is then presented and demonstrated. This leads on to the description of methods of acquiring and utilising known system geometry, namely the positions of electrodes and registration landmarks, to construct an accurate surface of the subject, the results of which are successfully validated. The outcome of this procedure is then used to specify boundary conditions to a mesh warping algorithm based on elastic deformation using well-established continuum mechanics procedures. The algorithm is applied to a range of source models to empirically establish optimum values for the parameters defining the problem which can successfully generate meshes of acceptable quality in terms of discretization errors and which more accurately define the geometry of the target subject. Further validation of the algorithm is performed by comparison of boundary voltages and image reconstructions from simulated and laboratory data to demonstrate that benefits in terms of image artefact reduction and localisation of conductivity changes can be gained. The processes described in the thesis are evaluated and discussed and topics of further work and application are described