Multiple-object 2-D-3-D registration for noninvasive pose identification of fracture fragments

An automatic reduction method of 3D bone fragments based on a novel section contour point descriptor

Comminuted fractures are orthopedic traumas with greater surgical difficulty. In clinical treatment, a great challenge is precise reduction of multiple broken bone fragments; Another great challenge is personalized and precise internal fixation after reduction. For these two issues, we designed an automated method framework for precise reduction and internal fixation of comminuted fractures. First, the Gaussian mixture model (GMM) is used to distinguish section points and noise points in a broken bone model; Second, ellipse fitting is carried out to achieve section points matching and a descriptor is proposed to describe the section features; Then, the Convolution Auto-Encoder (CAE) and genetic algorithm are used to extract feature vectors; Finally, after broken bone models registration, internal fixed plate can be reconstructed. Three verification experiments for comminuted bone fracture show this method has high accuracy and good efficiency. It can provide support for minimally invasive treatment for comminuted fractures.

Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients

Traditional intensity-based 2D/3D registration requires near-perfect initialization in order for image similarity metrics to yield meaningful updates of X-ray pose and reduce the likelihood of getting trapped in a local minimum. The conventional approaches strongly depend on image appearance rather than content, and therefore, fail in revealing large pose offsets that substantially alter the appearance of the same structure. We complement traditional similarity metrics with a convolutional neural network-based (CNN-based) registration solution that captures large-range pose relations by extracting both local and contextual information, yielding meaningful X-ray pose updates without the need for accurate initialization. To register a 2D X-ray image and a 3D CT scan, our CNN accepts a target X-ray image and a digitally reconstructed radiograph at the current pose estimate as input and iteratively outputs pose updates in the direction of the pose gradient on the Riemannian Manifold. Our approach integrates seamlessly with conventional image-based registration frameworks, where long-range relations are captured primarily by our CNN-based method while short-range offsets are recovered accurately with an image similarity-based method. On both synthetic and real X-ray images of the human pelvis, we demonstrate that the proposed method can successfully recover large rotational and translational offsets, irrespective of initialization.

Fiducial-Free 2D/3D Registration for Robot-Assisted Femoroplasty

Femoroplasty is a proposed alternative therapeutic method for preventing osteoporotic hip fractures in the elderly. Previously developed navigation system for femoroplasty required the attachment of an external X-ray fiducial to the femur. We propose a fiducial-free 2D/3D registration pipeline using fluoroscopic images for robot-assisted femoroplasty. Intraoperative fluoroscopic images are taken from multiple views to perform registration of the femur and drilling/injection device. The proposed method was tested through comprehensive simulation and cadaveric studies. Performance was evaluated on the registration error of the femur and the drilling/injection device. In simulations, the proposed approach achieved a mean accuracy of 1.26±0.74 mm for the relative planned injection entry point; 0.63±0.21° and 0.17±0.19° for the femur injection path direction and device guide direction, respectively. In the cadaver studies, a mean error of 2.64 ± 1.10 mm was achieved between the planned entry point and the device guide tip. The biomechanical analysis showed that even with a 4 mm translational deviation from the optimal injection path, the yield load prior to fracture increased by 40.7%. This result suggests that the fiducial-less 2D/3D registration is sufficiently accurate to guide robot assisted femoroplasty.

Pose Estimation of Periacetabular Osteotomy Fragments With Intraoperative X-Ray Navigation

OBJECTIVE

State-of-the-art navigation systems for pelvic osteotomies use optical systems with external fiducials. In this paper, we propose the use of X-ray navigation for pose estimation of periacetabular fragments without fiducials.

METHODS

A two-dimensional/three-dimensional (2-D/3-D) registration pipeline was developed to recover fragment pose. This pipeline was tested through an extensive simulation study and six cadaveric surgeries. Using osteotomy boundaries in the fluoroscopic images, the preoperative plan was refined to more accurately match the intraoperative shape.

RESULTS

In simulation, average fragment pose errors were 1.3 ° /1.7 mm when the planned fragment matched the intraoperative fragment, 2.2 ° /2.1 mm when the plan was not updated to match the true shape, and 1.9 ° /2.0 mm when the fragment shape was intraoperatively estimated. In cadaver experiments, the average pose errors were 2.2  ° /2.2 mm, 3.8 ° /2.5 mm, and 3.5  ° /2.2 mm when registering with the actual fragment shape, a preoperative plan, and an intraoperatively refined plan, respectively. Average errors of the lateral center edge angle were less than 2 ° for all fragment shapes in simulation and cadaver experiments.

CONCLUSION

The proposed pipeline is capable of accurately reporting femoral head coverage within a range clinically identified for long-term joint survivability.

SIGNIFICANCE

Human interpretation of fragment pose is challenging and usually restricted to rotation about a single anatomical axis. The proposed pipeline provides an intraoperative estimate of rigid pose with respect to all anatomical axes, is compatible with minimally invasive incisions, and has no dependence on external fiducials.