Patient-specific instrumentation improves functional kinematics of minimally-invasive total knee replacements as revealed by computerized 3D fluoroscopy

DRR acceleration using inexpensive GPUs for model-image registration based joint kinematic measurements

Model-image registration methods are commonly used in research to measure three-dimensional joint kinematics from single-plane and bi-plane x-ray images. These methods have the potential to be beneficial if used clinically, but current techniques are too slow or expensive to be clinically practical. One technical element of these methods for measuring natural bone motion is the use of digitally reconstructed radiographs (DRRs). DRRs can be very expensive to compute, or require expensive and fast computer hardware. In this technical development, a numerically efficient Siddon-Jacobs algorithm for computing DRRs was implemented on a consumer-grade graphics card using a programming language for parallel architectures. Compared to traditional voxel projection algorithms with a central-processing-unit-only implementation, the parallel computation implementation on the graphics card provided speedups of 650-1546 times faster rendering, while retaining equivalent performance for joint kinematics measurements. The use of consumer grade graphics hardware may contribute to making model-image registration measurements of joint kinematics practical for clinical use.

Integration of statistical shape modeling and alternating interpolation-based model tracking technique for measuring knee kinematics in vivo using clinical interleaved bi-plane fluoroscopy

Background

A 2D fluoroscopy/3D model-based registration with statistical shape modeling (SSM)-reconstructed subject-specific bone models will help reduce radiation exposure for 3D kinematic measurements of the knee using clinical alternating bi-plane fluoroscopy systems. The current study aimed to develop such an approach and evaluate in vivo its accuracy and identify the effects of the accuracy of SSM models on the kinematic measurements.

Methods

An alternating interpolation-based model tracking (AIMT) approach with SSM-reconstructed subject-specific bone models was used for measuring 3D knee kinematics from dynamic alternating bi-plane fluoroscopy images. A two-phase optimization scheme was used to reconstruct subject-specific knee models from a CT-based SSM database of 60 knees using one, two, or three pairs of fluoroscopy images. Using the CT-reconstructed model as a benchmark, the performance of the AIMT with SSM-reconstructed models in measuring bone and joint kinematics during dynamic activity was evaluated in terms of mean target registration errors (mmTRE) for registered bone poses and the mean absolute differences (MAD) for each motion component of the joint poses.

Results

The mmTRE of the femur and tibia for one image pair were significantly greater than those for two and three image pairs without significant differences between two and three image pairs. The MAD was 1.16 to 1.22° for rotations and 1.18 to 1.22 mm for translations using one image pair. The corresponding values for two and three image pairs were 0.75 to 0.89° and 0.75 to 0.79 mm; and 0.57 to 0.79° and 0.6 to 0.69 mm, respectively. The MAD values for one image pair were significantly greater than those for two and three image pairs without significant differences between two and three image pairs.

Conclusions

An AIMT approach with SSM-reconstructed models was developed, enabling the registration of interleaved fluoroscopy images and SSM-reconstructed models from more than one asynchronous fluoroscopy image pair. This new approach had sub-millimeter and sub-degree measurement accuracy when using more than one image pair, comparable to the accuracy of CT-based methods. This approach will be helpful for future kinematic measurements of the knee with reduced radiation exposure using 3D fluoroscopy with clinically alternating bi-plane fluoroscopy systems.

Mechanical alignment tolerance of a cruciate-retaining knee prosthesis under gait loading—A finite element analysis

Component alignment is one of the most crucial factors affecting total knee arthroplasty’s clinical outcome and survival. This study aimed to investigate how coronal, sagittal, and transverse malalignment affects the mechanical behavior of the tibial insert and to determine a suitable alignment tolerance on the coronal, sagittal, and transverse planes. A finite element model of a cruciate-retaining knee prosthesis was assembled with different joint alignments (−10°, −7°, −5°, −3°, 0°, 3°, 5°, 7°, 10°) to assess the effect of malalignment under gait loading. The results showed that varus or valgus, extension, internal rotation, and excessive external rotation malalignments increased the maximum Von Mises stress and contact pressure on the tibial insert. The mechanical alignment tolerance of the studied prosthesis on the coronal, sagittal, and transverse planes was 3° varus to 3° valgus, 0°–10° flexion, and 0°–5° external rotation, respectively. This study suggests that each prosthesis should include a tolerance range for the joint alignment angle on the three planes, which may be used during surgical planning.

Validation of an MRI Technique for the 6-DOF Knee Kinematics Measurement

Background: For total knee arthroplasty (TKA), the optimal rotational position of the femoral component is felt to be critically important. The current knee joint kinematics measurement technology is unable to identify the exact rotation axis of the knee joint, the main reasons being low measurement accuracy and insufficient three-dimensional data (2D-3D image matching technology). In order to improve the effect of TKA surgery, we proposed a knee joint kinematics measurement method, based on the MRI technology, and verified its measurement accuracy. We then employed this method to identify the personalized optimal rotation axis of the knee joint for TKA patients. Purposes: The purpose of the study was 1) to propose a method for measuring knee joint kinematics and verify its accuracy and 2) to propose a method for determining the optimal rotation axis of knee joint for TKA surgery, based on accurate kinematic measurement results. Materials and Methods: The experiment was divided into two parts: in vitro and in vivo. The purpose of the in vitro experiment was to verify the measurement accuracy of our method. We fixed two aquarium stones (approximately 10 cm * 10 cm * 10 cm in size, close to the size of the distal femur and proximal tibia) firmly on the fixed and moving arms of the goniometer/vernier caliper with glue and immersed the aquarium stones in the water to capture MRI images. The MRI images were then processed with MATLAB software, and the relative motion of the two aquarium stones was measured. The measurement accuracy of our method was verified via the scale reading of the moving arm on the goniometer/vernier caliper. In vivo, 36 healthy elderly participants (22 females, 14 males) were recruited from the local community; our method was then employed to measure the relative motion of the tibia and femur and to observe the rollback and screw home motion of the medial/lateral condyle of the femur, which was identified as specific kinematic features of the knee joint. Results: In vitro, all measurements were accurate to <1 mm and <1°. In vivo, all knee measurements showed rollback motion (the rollback distance of the medial femoral condyle was 18.1 ± 3.7 mm and that of the lateral condyle was 31.1 ± 7.3 mm) and screw home motion. Conclusion: In the application scenario of knee joint kinematics measurement, our method has an accuracy of <1° of rotation angle and <1 mm of translation for all reference points, and it can be employed to identify the most stable axis of the knee joint. Significance: Using our method to accumulate data on the knee rotation axis of more subjects to establish an average rotation axis of a given population may help in knee prosthesis design and reduce the patient dissatisfaction rate. Individually measuring the patient's rotation axis before TKA surgery and adjusting the prosthesis installation in TKA may further reduce the patient dissatisfaction rate, and automatic computer measurement may be realized in the future, but it is still time-consuming for now.

CEL-Unet: a novel CNN architecture for 3D Segmentation of Knee Bones affected by Severe Osteoarthritis for PSI-Based Surgical Planning

Unet architectures are promising deep learning networks exploited to perform the automatic segmentation of bone CT images, in line with their ability to deal with pathological deformations and size-varying anatomies. However, bone degeneration, like the development of irregular osteophytes as well as mineral density alterations might interfere with this automated process and demand extensive manual refinement. The aim of this work is to implement an innovative Unet variant, the CEL-Unet, to improve the femur and tibia segmentation outcomes in osteoarthritic knee joints. In this network the decoding path is split into a region and contour-aware branch to increase the prediction reliability in such pathological conditions. The comparison between the segmentation results achieved with a standard Unet and its novel variant (CEL-Unet) was performed as follows: the Unet was trained with 5 different loss functions: Dice Loss, Focal Loss, Exponential Logarithmic Loss, Double Cross Entropy Loss and Distanced Cross Entropy loss. The CEL-Unet was instead trained with two loss functions, one for each of the network outputs, namely Mask and Edge, yielding the so-called Combined Edge Loss (CEL) function. A set of 259 knee CT scans was used to train the model and test segmentation performance. The CEL-Unet outperformed all other Unet-based models, reaching the highest Jaccard values of about 0.97 and 0.96 on femur and tibia, respectively. Clinical Relevance- With the increasing rate of Total Knee Arthoplasty deep learning-based methods can achieve fast accurate and automatic 3D segmentation of the knee joint bones to enhance new costumized pre-operative planning.