Robotic-assisted burring in total hip replacement: A new surgical technique to optimise acetabular preparation.

BACKGROUND: In Total Hip replacement (THR) surgery, a critical step is to cut an accurate hemisphere into the acetabulum so that the component can be fitted accurately and obtain early stability. This study aims to determine whether burring rather than reaming the acetabulum can achieve greater accuracy in the creation of this hemisphere. METHODS: A preliminary robotic system was developed to demonstrate the feasibility of burring the acetabulum using the Universal Robot (UR10). The study will describe mechanical design, robot trajectory optimisation, control algorithm development, and results from phantom experiments compared with both robotic reaming and conventional reaming. The system was also tested in a cadaver experiment. RESULTS: The proposed robotic burring system can produce a surface in 2 min with an average error of 0.1 and 0.18 mm, when cutting polyurethane bone block #15 and #30, respectively. The performance was better than robotic reaming and conventional hand reaming. CONCLUSION: The proposed robotic burring system outperformed robotic and conventional reaming methods to produce an accurate acetabular cavity. The findings show the potential usage of a robotic-assisted burring in THR for acetabular preparation.

Complete region of interest reconstruction by fusing multiview deformable three-dimensional transesophageal echocardiography images.

BACKGROUND: While three-dimensional transesophageal echocardiography (3D TEE) has been increasingly used for assessing cardiac anatomy and function, it still suffers from a limited field of view (FoV) of the ultrasound transducer. Therefore, it is difficult to examine a complete region of interest without moving the transducer. Existing methods extend the FoV of 3D TEE images by mosaicing multiview static images, which requires synchronization between 3D TEE images and electrocardiogram (ECG) signal to avoid deformations in the images and can only get the widened image at a specific phase. PURPOSE: This work aims to develop a novel multiview nonrigid registration and fusion method to extend the FoV of 3D TEE images at different cardiac phases, avoiding the bias toward the specifically chosen phase. METHODS: A multiview nonrigid registration and fusion method is proposed to enlarge the FoV of 3D TEE images by fusing dynamic images captured from different viewpoints sequentially. The deformation field for registering images is defined by a collection of affine transformations organized in a graph structure and is estimated by a direct (intensity-based) method. The accuracy of the proposed method is evaluated by comparing it with two B-spline-based methods, two Demons-based methods, and one learning-based method VoxelMorph. Twenty-nine sequences of in vivo 3D TEE images captured from four patients are used for the comparative experiments. Four performance metrics including checkerboard volumes, signed distance, mean absolute distance (MAD), and Dice similarity coefficient (DSC) are used jointly to evaluate the accuracy of the results. Additionally, paired t-tests are performed to examine the significance of the results. RESULTS: The qualitative results show that the proposed method can align images more accurately and obtain the fused images with higher quality than the other five methods. Additionally, in the evaluation of the segmented left atrium (LA) walls for the pairwise registration and sequential fusion experiments, the proposed method achieves the MAD of (0.07 ± 0.03) mm for pairwise registration and (0.19 ± 0.02) mm for sequential fusion. Paired t-tests indicate that the results obtained from the proposed method are more accurate than those obtained by the state-of-the-art VoxelMorph and the diffeomorphic Demons methods at the significance level of 0.05. In the evaluation of left ventricle (LV) segmentations for the sequential fusion experiments, the proposed method achieves a DSC of (0.88 ± 0.08), which is also significantly better than diffeomorphic Demons at the 0.05 level. The FoVs of the final fused 3D TEE images obtained by our method are enlarged around two times compared with the original images. CONCLUSIONS: Without selecting the static (ECG-gated) images from the same cardiac phase, this work addressed the problem of limited FoV of 3D TEE images in the deformable scenario, obtaining the fused images with high accuracy and good quality. The proposed method could provide an alternative to the conventional fusion methods that are biased toward the specifically chosen phase.

Proof of concept study for using UR10 robot to help total hip replacement.

BACKGROUND: The demand for total hip replacement (THR) for treating osteoarthritis has grown substantially worldwide. The existing robotic systems used in THR are invasive and costly. This study aims to develop a less-invasive and low-cost robotic system to assist THR surgery. METHODS: A preliminary robotic reaming system was developed based on a UR10 robot equipped with a reamer to cut acetabulum. A novel approach was proposed to cut through a 5 mm hole in femur such that the operation is less invasive to the patients. RESULTS: The average error of the cutting hemisphere by the robotic reaming system is 0.1182 mm which is smaller than the average result reaming by hand (0.1301 mm). CONCLUSION: The robotic reaming can help make THR procedures less invasive and more accurate. Moreover, the system is expected to be significantly less expensive than the robotic systems available in the market at present.

Direct 3D ultrasound fusion for transesophageal echocardiography.

BACKGROUND: Real-time three-dimensional transesophageal echocardiography (3D TEE) has been increasingly used in clinic for fast 3D analysis of cardiac anatomy and function. However, 3D TEE still suffers from the limited field of view (FoV). It is challenging to adopt conventional multi-view fusion methods to 3D TEE images because feature-based registration methods tend to fail in the ultrasound scenario, and conventional intensity-based methods have poor convergence properties and require an iterative coarse-to-fine strategy. METHODS: A novel multi-view registration and fusion method is proposed to enlarge the FoV of 3D TEE images efficiently. A direct method is proposed to solve the registration problem in the Lie algebra space. Fast implementation is realized by searching voxels on three orthogonal planes between two volumes. Besides, a weighted-average 3D fusion method is proposed to fuse the aligned images seamlessly. For a sequence of 3D TEE images, they are fused incrementally. RESULTS: Qualitative and quantitative results of in-vivo experiments indicate that the proposed registration algorithm outperforms a state-of-the-art PCA-based registration method in terms of accuracy and efficiency. Image registration and fusion performed on 76 in-vivo 3D TEE volumes from nine patients show apparent enlargement of FoV (enlarged around two times) in the obtained fused images. CONCLUSIONS: The proposed methods can fuse 3D TEE images efficiently and accurately so that the whole Region of Interest (ROI) can be seen in a single frame. This research shows good potential to assist clinical diagnosis, preoperative planning, and future intraoperative guidance with 3D TEE.

Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction.

Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs) are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM) approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE) is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), as well as the well-known radio tomographic imaging (RTI) DFL approach.

Optimizing heart rate regulation for safe exercise.

Safe exercise protocols are critical for effective rehabilitation programs. This paper aims to develop a novel control strategy for an automated treadmill system to reduce the danger of injury during cardiac rehabilitation. We have developed a control-oriented nonparametric Hammerstein model for the control of heart rate during exercises by using support vector regression and correlation analysis. Based on this nonparametric model, a model predictive controller has been built. In order to guarantee the safety of treadmill exercise during rehabilitation, this new automated treadmill system is capable of optimizing system performance over predefined ranges of speed and acceleration. The effectiveness of the proposed approach was demonstrated with six subjects by having their heart rate track successfully a predetermined heart rate.

Nonparametric Hammerstein model based model predictive control for heart rate regulation.

This paper proposed a novel nonparametric model based model predictive control approach for the regulation of heart rate during treadmill exercise. As the model structure of human cardiovascular system is often hard to determine, nonparametric modelling is a more realistic manner to describe complex behaviours of cardiovascular system. This paper presents a new nonparametric Hammerstein model identification approach for heart rate response modelling. Based on the pseudo-random binary sequence experiment data, we decouple the identification of linear dynamic part and input nonlinearity of the Hammerstein system. Correlation analysis is applied to acquire step response of linear dynamic component. Support Vector Regression is adopted to obtain a nonparametric description of the inverse of input static nonlinearity that is utilized to form an approximate linear model of the Hammerstein system. Based on the established model, a model predictive controller under predefined speed and acceleration constraints is designed to achieve safer treadmill exercise. Simulation results show that the proposed control algorithm can achieve optimal heart rate tracking performance under predefined constraints.