Introduction

Although total knee arthroplasty (TKA) is generally considered successful, 16–30% of patients are dissatisfied. There are multiple reasons for this, but some of the most frequent reasons for revision are instability and joint stiffness. A possible explanation for this is that the implant alignment is not optimized to ensure joint stability in the individual patient. In this work, we used an artificial neural network (ANN) to learn the relation between a given standard cruciate-retaining (CR) implant position and model-predicted post-operative knee kinematics. The final aim was to find a patient-specific implant alignment that will result in the estimated post-operative knee kinematics closest to the native knee.

Introduction

Loosening of the baseplate is one of the most common causes of failure in Reverse Shoulder Arthroplasty. To allow osteo-integration to occur and thus provide long-term stability, initial screws fixation plays a pivotal role. In particular, tightening torque and force of nonlocking screws are two parameters that are considered to have a clear impact on implant stability, yet the relation is not fully understood. For this reason, this study aims to define an experimental set-up, to measure force and torque in artificial bone samples of different quality, in order to estimate ranges of optimal surgical values and give guidelines to maximize screw fixation and therefore initial implant stability.

Joint laxity assessments have been a valuable resource in order to understand the biomechanics and pathologies of the knee. Clinical laxity tests like the Lachman test, Pivot-shift test and Drawer test are, however, subjective of nature and will often only provide basic information of the joint. Stress radiography is another option for assessing knee laxity; however, this method is also limited in terms of quantifiability and one-dimensionality.

This study proposes a novel non-invasive low-dose radiation method to accurately measure knee joint laxity in 3D. A method that combines a force controlled parallel manipulator device, a medical image and a biplanar x-ray system.

As proof-of-concept, a cadaveric knee was CT scanned and subsequently mounted at 30 degrees of flexion in the device and placed inside a biplanar x-ray scanner. Biplanar x-rays were obtained for eleven static load cases.

The preliminary results from this study display that the device is capable of measuring primary knee laxity kinematics similar to what have been reported in previous studies. Additionally, the results also display that the method is capable of capturing coupled motions like internal/external rotation when anteroposterior loads are applied.

We have displayed that the presented method is capable of obtaining knee joint laxity in 3D. The method is combining concepts from robotic arthrometry and stress radiography into one unified solution that potentially enables unprecedented 3D joint laxity measurements non-invasively. The method potentially eliminates limitations present in previous methods and significantly reduces the radiation exposure of the patient compared to conventional stress radiography.

Children with cerebral palsy (CP) often present femoral bone deformities not accounted for in generic musculoskeletal models [1,2]. MRI-based models can be used to include subject-specific muscle paths [3,4], although this is a time-demanding process. Recently, non-rigid deformation techniques have been used to transform generic bone geometry, including muscle points, onto personalized bones [5]. However, it is still unknown to what extent such an approximation of subject-specific detail affects calculated hip contact forces (HCFs) during gait in CP children.

Seven children diagnosed with diplegic CP walked independently at self-selected speed. 3D marker trajectories were captured using Vicon (Oxford Metrics, UK) and force data was measured using two AMTI force platforms (Watertown, MA). MR-images were acquired (Philips Ingenia 1.5T) of all subjects lying supine. Firstly, a generic model [6] was scaled using the marker positions of a static pose. Secondly, a MRI-model containing the subject-specific bone structures and muscle paths of all hip and upper leg muscles was created [3]. Thirdly, the generic femur and pelvis geometries and muscle points were transformed onto the image-based femur and pelvis using an advanced non-rigid deformation procedure (Materialise N.V.). For all models, further analyses were performed in OpenSim 3.1 [7]. A kalman smoother procedure was used to calculate joint angles [8]. Muscle forces were calculated using a static optimization minimizing the sum of squared muscle activities. Next, HCFs were calculated and normalized to body weight (BW). First and second peak HCFs were determined and used for a Kruskal-Wallis test to determine differences between models. In case of a significant difference, a post-hoc rank-based multiple comparison test with Bonferonni adjustment was used. Further, average absolute differences in muscle points between the models was calculated, as well as average differences in moment arm lengths (MALs), reflecting muscle function.

Where the scaled generic muscle points differed on average 2.49cm from the MRI points, the non-rigidly deformed points differed 1.54cm from the MRI muscle points. Specifically, the tensor fascia latae differed most between the deformed and MRI models (11.7cm). When considering MALs, the gluteii muscles present an altered function for the generic and deformed models compared to the MRI model for all degrees of freedom of the hip at the time of both HCF peaks. The differences between models resulted in a significantly increased second peak HCF for the MRI models compared to the generic models (first peak average HCF: 3.88BW, 3.95BW and 4.90BW; second peak average HCF: 3.03BW, 4.89BW and 5.32BW for the generic, MRI and non-rigidly deformed models respectively). Although not significantly different, the deformed models calculated slightly increased HCFs compare to the MRI models.

The generic models underestimated HCFs compared to the MRI models, while the non-rigidly deformed models slightly overestimated HCFs. However, differences between the deformed and MRI models in terms of muscle points and MALs remain, specifically for the gluteii muscles. Therefore, further user-guided modification of the model based on MR-images will be necessary.

The use of 3D imaging methodologies in orthopaedics has allowed the introduction of new technologies, such as the design of patient-specific implants or surgical instrumentation. This has introduced the need for high accuracy, in addition to a correct diagnosis. Until recently, little was known about the accuracy of MR imaging to reconstruct 3D models of the skeletal anatomy. This study was conducted to quantify the accuracy of MRI-based segmentation of the knee joint.

Nine knees of unfixed human cadavers were used to compare the accuracy of MR imaging to an optical scan. MR images of the specimens were obtained with a 1.5T clinical MRI scanner (GE Signa HDxt), using a slice thickness of 2 mm and a pixel size of 0.39 mm × 0.39 mm. Manual segmentation of the images was done using Mimics® (Materialise NV, Leuven, Belgium). The specimens were cleaned using an acetone treatment to remove soft-tissue but to keep the cartilage intact. The cleaned bones were optically scanned using a white-light optical scanner (ATOS II by GOM mbH, Braunschweig, Germany) having a resolution of 1.2 million pixels per measuring volume, yielding an accuracy of 0.02 mm. The optical scan of each bone reflects the actual dimensions of the bone and is considered as a ground truth measurement. First, a registration of the optical scan and the MRI-based 3D reconstruction was performed. Then, the optical scan was compared to the 3D model of the bone by calculating the distance of the vertices of the optical scan to the reconstructed 3D object.

Comparison of the 3D reconstruction using MRI images and the optical scans resulted in an average absolute error of 0.67 mm (± 0.52 mm standard deviation) for segmentation of the cartilage surface, with an RMS value of circa twice the pixel size. Segmenting the bone surface resulted in an average absolute error of 0.42 mm (± 0.38 mm standard deviation) and an RMS error of 1.5 times the pixel size. This accuracy is higher than reported previously by White, who compared MRI and CT imaging by looking at the positioning of landmarks on 3D printed models of the segmented images using a calliper [White, 2008]. They reported an average accuracy of 2.15 mm (± 2.44 mm) on bone using MRI images. In comparison, Rathnayaka compared both CT- and MRI-based 3D models to measurements of the real bone using a mechanical contact scanner [Rathnayaka, 2012]. They listed an accuracy of 0.23 mm for MRI segmentation using five ovine limbs.

This study is one of the first to report on the segmentation accuracy of MRI technology on knee cartilage, using human specimens and a clinical scanning protocol. The results found for both bone and cartilage segmentation demonstrate the feasibility of accurate 3D reconstructions of the knee using MRI technology.

In recent years 3D preoperative planning has become increasingly popular with orthopaedic surgeons. One technique that has shown to be successful in transferring this preoperative plan to the operating room is based on surgical templates that guide various surgical instruments. Such a patient-specific template is designed using both the 3D reconstructed anatomy and the preoperative plan and is then typically produced via additive manufacturing technology. The combination of a preoperative plan and a surgical template has the potential to result in a more accurate procedure than an unguided one, when the following three criteria are met: the template needs to achieve a stable fit on the surgical field, it needs to fit in a unique position, and the surgeon needs to be able to determine the correct, planned position during the surgery. When the template fails one of these conditions, it can be used incorrectly. Consequently the process could result in an inaccurate outcome.

This research focuses on modelling the stability of a surgical template on bone. The relationship between the contact surface of the template and the resulting stability is investigated with a focus on methods to quantify the template stability. The model calculates a quality score on the designed contact surface, which reflects the likelihood of positioning the template on the bone in a stable position. The model used in this study has been experimentally validated to verify its ability to provide a reliable indication of the template stability. This was analysed using finite element analysis where multiple templates and support models with different contact surface shapes were created. The application of forces and moments in varying directions was simulated. Stability is then defined as the ability of a template to resist an applied force or moment. The displacements of the templates were computed and analysed. The results show a minimal displacement of less than 0.01 mm and a maximal displacement larger than 10 mm. The former is considered to be a very stable template design; the latter to be very unstable and hence, would result in an insecure contact.

The geometry of the contact surface had a clear influence on the template stability. Overall, the coverage of curvature variations improved the stability of the template. The displacements of the different finite element simulations were used as criterion for ranking the tested template designs according to their stability on their corresponding model surface. This ranking is then compared to that resulting from the quality score of the stability model. Both rankings showed a similar trend. This evaluation phase thus indicates that the developed stability model can be used to predict the stability of a surgical template during the preoperative design process.