Performance of local optimization in single-plane fluoroscopic analysis for total knee arthroplasty

Fully automatic tracking of native knee kinematics from stereo-radiography with digitally reconstructed radiographs

Precise measurement of joint-level motion from stereo-radiography facilitates understanding of human movement. Conventional procedures for kinematic tracking require significant manual effort and are time intensive. The current work introduces a method for fully automatic tracking of native knee kinematics from stereo-radiography sequences. The framework consists of three computational steps. First, biplanar radiograph frames are annotated with segmentation maps and key points using a convolutional neural network. Next, initial bone pose estimates are acquired by solving a polynomial optimization problem constructed from annotated key points and anatomic landmarks from digitized models. A semidefinite relaxation is formulated to realize the global minimum of the non-convex problem. Pose estimates are then refined by registering computed tomography-based digitally reconstructed radiographs to masked radiographs. A novel rendering method is also introduced which enables generating digitally reconstructed radiographs from computed tomography scans with inconsistent slice widths. The automatic tracking framework was evaluated with stereo-radiography trials manually tracked with model-image registration, and with frames which capture a synthetic leg phantom. The tracking method produced pose estimates which were consistently similar to manually tracked values; and demonstrated pose errors below 1.0 degree or millimeter for all femur and tibia degrees of freedom in phantom trials. Results indicate the described framework may benefit orthopaedics and biomechanics applications through acceleration of kinematic tracking.

Fully automatic tracking of native glenohumeral kinematics from stereo-radiography

The current work introduces a system for fully automatic tracking of native glenohumeral kinematics in stereo-radiography sequences. The proposed method first applies convolutional neural networks to obtain segmentation and semantic key point predictions in biplanar radiograph frames. Preliminary bone pose estimates are computed by solving a non-convex optimization problem with semidefinite relaxations to register digitized bone landmarks to semantic key points. Initial poses are then refined by registering computed tomography-based digitally reconstructed radiographs to captured scenes, which are masked by segmentation maps to isolate the shoulder joint. A particular neural net architecture which exploits subject-specific geometry is also introduced to improve segmentation predictions and increase robustness of subsequent pose estimates. The method is evaluated by comparing predicted glenohumeral kinematics to manually tracked values from 17 trials capturing 4 dynamic activities. Median orientation differences between predicted and ground truth poses were 1.7∘ and 8.6∘ for the scapula and humerus, respectively. Joint-level kinematics differences were less than 2∘ in 65%, 13%, and 63% of frames for XYZ orientation DoFs based on Euler angle decompositions. Automation of kinematic tracking can increase scalability of tracking workflows in research, clinical, or surgical applications.

Fluoroscopic and radiostereometric analysis of a bicruciate-retaining versus a posterior cruciate-retaining total knee arthroplasty: a randomized controlled trial

AIMS

The aim of this study was to compare a bicruciate-retaining (BCR) total knee arthroplasty (TKA) with a posterior cruciate-retaining (CR) TKA design in terms of kinematics, measured using fluoroscopy and stability as micromotion using radiostereometric analysis (RSA).

METHODS

A total of 40 patients with end-stage osteoarthritis were included in this randomized controlled trial. All patients performed a step-up and lunge task in front of a monoplane fluoroscope one year postoperatively. Femorotibial contact point (CP) locations were determined at every flexion angle and compared between the groups. RSA images were taken at baseline, six weeks, three, six, 12, and 24 months postoperatively. Clinical and functional outcomes were compared postoperatively for two years.

RESULTS

The BCR-TKA demonstrated a kinematic pattern comparable to the natural knee's screw-home mechanism in the step-up task. In the lunge task, the medial CP of the BCR-TKA was more anterior in the early flexion phase, while laterally the CP was more posterior during the entire movement cycle. The BCR-TKA group showed higher tibial migration. No differences were found for the clinical and functional outcomes.

CONCLUSION

The BCR-TKA shows a different kinematic pattern in early flexion/late extension compared to the CR-TKA. The difference between both implants is mostly visible in the flexion phase in which the anterior cruciate ligament is effective; however, both designs fail to fully replicate the motion of a natural knee. The higher migration of the BCR-TKA was concerning and highlights the importance of longer follow-up.Cite this article: Bone Joint J 2023;105-B(1):35-46.

Influence of the Anterolateral Ligament on Knee Laxity: A Biomechanical Cadaveric Study Measuring Knee Kinematics in 6 Degrees of Freedom Using Dynamic Radiostereometric Analysis

Background:

An anterior cruciate ligament (ACL) rupture often occurs during rotational trauma to the knee and may be associated with damage to extracapsular knee rotation–stabilizing structures such as the anterolateral ligament (ALL).

Purpose:

To investigate ex vivo knee laxity in 6 degrees of freedom with and without ALL reconstruction as a supplement to ACL reconstruction.

Study Design:

Controlled laboratory study.

Methods:

Cadaveric knees (N = 8) were analyzed using dynamic radiostereometry during a controlled pivotlike dynamic movement simulated by motorized knee flexion (0° to 60°) with 4-N·m internal rotation torque. We tested the cadaveric specimens in 5 successive ligament situations: intact, ACL lesion, ACL + ALL lesion, ACL reconstruction, and ACL + ALL reconstruction. Anatomic single-bundle reconstruction methods were used for both the ACL and the ALL, with a bone-tendon quadriceps autograft and gracilis tendon autograft, respectively. Three-dimensional kinematics and articular surface interactions were used to determine knee laxity.

Results:

For the entire knee flexion motion, an ACL + ALL lesion increased the mean knee laxity (P < .005) for internal rotation (2.54°), anterior translation (1.68 mm), and varus rotation (0.53°). Augmented ALL reconstruction reduced knee laxity for anterior translation (P = .003) and varus rotation (P = .047) compared with ACL + ALL–deficient knees. Knees with ACL + ALL lesions had more internal rotation (P < .001) and anterior translation (P < .045) at knee flexion angles below 40° and 30°, respectively, compared with healthy knees. Combined ACL + ALL reconstruction did not completely restore native kinematics/laxity at flexion angles below 10° for anterior translation and below 20° for internal rotation (P < .035). ACL + ALL reconstruction was not found to overconstrain the knee joint.

Conclusion:

Augmented ALL reconstruction with ACL reconstruction in a cadaveric setting reduces internal rotation, varus rotation, and anterior translation knee laxity similar to knee kinematics with intact ligaments, except at knee flexion angles between 0° and 20°.

Clinical Relevance:

Patients with ACL injuries can potentially achieve better results with augmented ALL reconstruction along with ACL reconstruction than with stand-alone ACL reconstruction. Furthermore, dynamic radiostereometry provides the opportunity to examine clinical patients and compare the recontructed knee with the contralateral knee in 6 degrees of freedom.

Automated Registration of 3-D Knee Implant Models to Fluoroscopic Images Using Lipschitzian Optimization

This paper describes an automated method for registering 3-D models of metallic knee implants to single-plane radiographic images. We develop a multistage approach that identifies the correct pose by matching altered dilations of an edge-detected image with the silhouette of an implant model. The location of the similarity function's minimum is found using a novel optimization routine that combines the Dividing Rectangles algorithm with properties of the registration metric. Depending on the implant type (tibial or femoral), this technique reliably converges under maximum displacements of approximately 25 to 55 millimeters for translation components and 25° to 55° for Euler angles. The method proves to be robust to noise from bones and soft tissue. After an initial guess for the first image in the sequence, subsequent frames can be automatically registered from the optimum pose in the previous image.