Fully automatic initialization of two-dimensional-three-dimensional medical image registration using hybrid classifier

Evaluation of automated radiostereometric image registration in total knee arthroplasty utilizing a synthetic-based and a CT-based volumetric model

Radiostereometic analysis (RSA) is an accurate method for rigid body pose (position and orientation) in three-dimensional space. Traditionally, RSA is based on insertion of periprosthetic tantalum markers and manual implant contour selection which limit clinically application. We propose an automated image registration technique utilizing digitally reconstructed radiographs (DRR) of computed tomography (CT) volumetric bone models (autorsa-bone) as a substitute for tantalum markers. Furthermore, an automated synthetic volumetric representation of total knee arthroplasty implant models (autorsa-volume) to improve previous silhouette-projection methods (autorsa-surface). As reference, we investigated the accuracy of implanted tantalum markers (marker) or a conventional manually contour-based method (mbrsa) for the femur and tibia. The data are presented as mean (standard deviation). The autorsa-bone method displayed similar accuracy of -0.013 (0.075) mm compared to the gold standard method (marker) of -0.013 (0.085). The autorsa-volume with 0.034 (0.106) mm did not markedly improve the autorsa-surface with 0.002 (0.129) mm, and none of these reached the mbrsa method of -0.009 (0.094) mm. In conclusion, marker-free RSA is feasible with similar accuracy as gold standard utilizing DRR and CT obtained volumetric bone models. Furthermore, utilizing synthetic generated volumetric implant models could not improve the silhouette-based method. However, with a slight loss of accuracy the autorsa methods provide a feasible automated alternative to the semi-automated method.

Construction of 3D model of knee joint motion based on MRI image registration

There is a growing demand for information and computational technology for surgeons help with surgical planning as well as prosthetics design. The two-dimensional images are registered to the three-dimensional (3D) model for high efficiency. To reconstruct the 3D model of knee joint including bone structure and main soft tissue structure, the evaluation and analysis of sports injury and rehabilitation treatment are detailed in this study. Mimics 10.0 was used to reconstruct the bone structure, ligament, and meniscus according to the pulse diffusion-weighted imaging sequence (PDWI) and stir sequences of magnetic resonance imaging (MRI). Excluding congenital malformations and diseases of the skeletal muscle system, MRI scanning was performed on bilateral knee joints. Proton weighted sequence (PDWI sequence) and stir pulse sequence were selected for MRI. The models were imported into Geomagic Studio 11 software for refinement and modification, and 3D registration of bone structure and main soft tissue structure was performed to construct a digital model of knee joint bone structure and accessory cartilage and ligament structure. The 3D knee joint model including bone, meniscus, and collateral ligament was established. Reconstruction and image registration based on mimics and Geomagic Studio can build a 3D model of knee joint with satisfactory morphology, which can meet the requirements of teaching, motion simulation, and biomechanical analysis.

The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective

Image-based navigation is widely considered the next frontier of minimally invasive surgery. It is believed that image-based navigation will increase the access to reproducible, safe, and high-precision surgery as it may then be performed at acceptable costs and effort. This is because image-based techniques avoid the need of specialized equipment and seamlessly integrate with contemporary workflows. Furthermore, it is expected that image-based navigation techniques will play a major role in enabling mixed reality environments, as well as autonomous and robot-assisted workflows. A critical component of image guidance is 2D/3D registration, a technique to estimate the spatial relationships between 3D structures, e.g., preoperative volumetric imagery or models of surgical instruments, and 2D images thereof, such as intraoperative X-ray fluoroscopy or endoscopy. While image-based 2D/3D registration is a mature technique, its transition from the bench to the bedside has been restrained by well-known challenges, including brittleness with respect to optimization objective, hyperparameter selection, and initialization, difficulties in dealing with inconsistencies or multiple objects, and limited single-view performance. One reason these challenges persist today is that analytical solutions are likely inadequate considering the complexity, variability, and high-dimensionality of generic 2D/3D registration problems. The recent advent of machine learning-based approaches to imaging problems that, rather than specifying the desired functional mapping, approximate it using highly expressive parametric models holds promise for solving some of the notorious challenges in 2D/3D registration. In this manuscript, we review the impact of machine learning on 2D/3D registration to systematically summarize the recent advances made by introduction of this novel technology. Grounded in these insights, we then offer our perspective on the most pressing needs, significant open problems, and possible next steps.

Reconstruction of knee anatomy from single-plane fluoroscopic x-ray based on a nonlinear statistical shape model

Purpose: Reconstruction of patient anatomy is critical to patient-specific instrument (PSI) design in total joint replacement (TJR). Conventionally, computed tomography (CT) and magnetic resonance imaging (MRI) are used to obtain the patient anatomy as they are accurate imaging modalities. However, computing anatomical landmarks from the patient anatomy for PSIs requires either high-resolution CT, increasing time of scan and radiation exposure to the patient, or longer and more expensive MRI scans. As an alternative, reconstruction from single-plane fluoroscopic x-ray provides a cost-efficient tool to obtain patient anatomical structures while allowing capture of the patient’s joint dynamics, important clinical information for TJR.

Approach: We present a three-dimensional (3D) reconstruction scheme that automatically and accurately reconstructs the 3D knee anatomy from single-plane fluoroscopic x-ray based on a nonlinear statistical shape model called kernel principal component analysis. To increase robustness, we designed a hybrid energy function that integrated feature and intensity information as a similarity measure for the 3D reconstruction.

Results: We evaluated the proposed method on five subjects during deep knee bending: the root-mean-square accuracy is 1.19±0.36  mm for reconstructed femur and 1.15±0.17  mm for reconstructed tibia.

Conclusions: The proposed method demonstrates reliable 3D bone model reconstruction accuracy with successful elimination of prior 3D imaging and reduction of manual labor and radiation dose on patient as well as characterizing joints in motion. This method is promising for applications in medical interventions such as patient-specific arthroplasty design, surgical planning, surgical navigation, and understanding anatomical and dynamic characteristics of joints.

Robust x-ray image segmentation by spectral clustering and active shape model

Extraction of bone contours from x-ray radiographs plays an important role in joint space width assessment, preoperative planning, and kinematics analysis. We present a robust segmentation method to accurately extract the distal femur and proximal tibia in knee radiographs of varying image quality. A spectral clustering method based on the eigensolution of an affinity matrix is utilized for x-ray image denoising. An active shape model-based segmentation method is employed for robust and accurate segmentation of the denoised x-ray images. The performance of the proposed method is evaluated with x-ray images from the public-use dataset(s), the osteoarthritis initiative, achieving a root mean square error of [Formula: see text] for femur and [Formula: see text] for tibia. The results demonstrate that this method outperforms previous segmentation methods in capturing anatomical shape variations, accounting for image quality differences and guiding accurate segmentation.