Feasibility of A-mode ultrasound based intraoperative registration in computer-aided orthopedic surgery: A simulation and experimental study

Non-contact tracking of shoulder bones using ultrasound and stereophotogrammetry

Purpose

We explored the integration of 3D ultrasound (US) imaging with motion capture technology for non-invasively tracking bones of the shoulder district during normal activity. Our study aimed to demonstrate ex-vivo the proposed 3D US method's feasibility and accuracy of tracking shoulder bones in a controlled cadaveric shoulder positioned in various arm elevations (low, mid and high).

Method

We registered previously acquired full bone shapes to spatially small bony surface patches segmented from 3D US. The bone registration approach used was based on in silico analyses that investigated the effects of different - 1) registration algorithms (Iterative-Closest-Point-ICP, and Coherent Point Drift-CPD) and 2) initial estimate levels of the bone model pose relative to the targeted final bone pose-on the overall registration efficiency and accuracy in a controlled environment.

Results

CPD provided the highest accuracy in the simulation at the cost of 8x longer computation compared to ICP. The RMSE errors were <1 mm for the humerus and scapula at all elevations. Ex-vivo, the CPD registration errors were (Humerus = 2 mm and Scapula = 13.9 mm) (Humerus = 7.2 mm and Scapula = 16.8 mm) and (Humerus = 14.28 mm and Scapula = 27.5 mm), for low, medium and high elevations respectively.

Conclusion

In summary, we demonstrated the feasibility and accuracy of tracking shoulder bones with 3D US in a simulation and a cadaveric experiment. We discovered that CPD may be a more suitable registration method for the task than ICP. We also discussed that 3D US with motion capture technology is very promising for dynamic bone tracking, but the US technology may not be ready for the task yet.

Automatic virtual reduction of unilateral zygomatic fractures based on ICP algorithm: A preliminary study

OBJECTIVE

To establish an automatic reduction method for unilateral zygomatic fractures based on Iterative Closes Point (ICP) algorithm.

MATERIAL AND METHODS

60 patients with unilateral type B zygomatic fractures were included. After acquiring CT images, zygomatic fragments were segmented using self-developed software MICSys. Mid-Sagittal-Plane (MSP) was manually defined using anatomical skull landmarks. Surface of zygoma on the healthy side was then "mirrored" according to MSP. Referring to mirror image, the fragments were reduced by both automatic and manual methods. In automatic group, fragments were registered onto mirror images by ICP algorithm in MICSys. In manual group, an experienced maxillofacial surgeon translated and rotated fragments until coincided with mirror images. Operating time of each group was recorded. RMSE between reduced fragment and mirror image was calculated to evaluate accuracy. Operating time and accuracy between the two groups were compared using T-test.

RESULTS

Virtual bone reduction was conducted for all 60 patients by the two methods. Operating time of automatic group and manual group were 3.06 ± 1.93 s and 65.45 ± 32.19 s, with significant difference (P < 0.0001). RMSE of automatic group and manual group were 1.94 ± 0.59 mm and 2.33 ± 0.57 mm, with significant difference (P < 0.0001).

CONCLUSION

Automatic reduction method based on ICP Algorithm for unilateral zygomatic fractures was initially established and clinically acceptable.

Soft-tissue sound-speed-aware ultrasound-CT registration method for computer-assisted orthopedic surgery

Ultrasound (US) has been introduced to computer-assisted orthopedic surgery for bone registration owing to its advantages of nonionizing radiation, low cost, and noninvasiveness. However, the registration accuracy is limited by US image distortion caused by variations in the acoustic properties of soft tissues. This paper proposes a soft-tissue sound-speed-aware registration method to overcome the above challenge. First, the feature enhancement strategy of multi-channel overlay is proposed for U2-net to improve bone segmentation performance. Secondly, the sound speed of soft tissue is estimated by simulating the bone surface distance map for the update of US-derived points. Finally, an iterative registration strategy is adopted to optimize the registration result. A phantom experiment was conducted using different registration methods for the femur and tibia/fibula. The fiducial registration error (femur, 0.98 ± 0.08 mm (mean ± SD); tibia/fibula, 1.29 ± 0.19 mm) and the target registration error (less than 2.11 mm) showed the high accuracy of the proposed method. The experimental results suggest that the proposed method can be integrated into navigation systems that provide surgeons with accurate 3D navigation information.

A Method to Track 3D Knee Kinematics by Multi-Channel 3D-Tracked A-Mode Ultrasound

This paper introduces a method for measuring 3D tibiofemoral kinematics using a multi-channel A-mode ultrasound system under dynamic conditions. The proposed system consists of a multi-channel A-mode ultrasound system integrated with a conventional motion capture system (i.e., optical tracking system). This approach allows for the non-invasive and non-radiative quantification of the tibiofemoral joint's six degrees of freedom (DOF). We demonstrated the feasibility and accuracy of this method in the cadaveric experiment. The knee joint's motions were mimicked by manually manipulating the leg through multiple motion cycles from flexion to extension. To measure it, six custom ultrasound holders, equipped with a total of 30 A-mode ultrasound transducers and 18 optical markers, were mounted on various anatomical regions of the lower extremity of the specimen. During experiments, 3D-tracked intra-cortical bone pins were inserted into the femur and tibia to measure the ground truth of tibiofemoral kinematics. The results were compared with the tibiofemoral kinematics derived from the proposed ultrasound system. The results showed an average rotational error of 1.51 ± 1.13° and a translational error of 3.14 ± 1.72 mm for the ultrasound-derived kinematics, compared to the ground truth. In conclusion, this multi-channel A-mode ultrasound system demonstrated a great potential of effectively measuring tibiofemoral kinematics during dynamic motions. Its improved accuracy, nature of non-invasiveness, and lack of radiation exposure make this method a promising alternative to incorporate into gait analysis and prosthetic kinematic measurements later.

Improving ultrasound-based bone registration using the iterative closest point algorithm paired with a complex optimization solver

The Iterative Close Point (ICP) algorithm is used for bone registrations based on ultrasound measurements. However, the ICP has been shown to suffer from local minima. The Complex optimization, as a more robust routine compared to the commonly used gradient-based algorithms, could be an alternative for solving the ICP problem. In this study, we investigated the effect of the initial estimate and the number of registration points on bone registrations achieved using the ICP and a Complex optimization routine and we compared it against using Quadratic Sequential Programming (SQP). Ultrasound measurements were performed with an A-mode probe on a bovine humerus and an ovine femur embedded into ballistic gel. Simultaneously, the bones and the probe were tracked in 3D space using retroreflective markers. Kinematic, ultrasound and geometrical data obtained from scans of the specimens and the probe served as input to a bone registrations routine. Registrations were performed using two ICP solvers for different initial estimates and number of registration points. On average, 68 % of the Complex optimization registrations had less than 1 mm translation error and less than 1° rotational error for perturbations of the initial estimate from the reference measurements compared to the 35 % of the SQP ones. Similar medians of registration errors were observed between the two methods for variations of the number of the employed registration points. Although the Complex optimization provided accurate bone registrations for all cases, the objective function could not always determine the registrations with the smallest registration error. Future research should explore methodologies to overcome this challenge.

Femur reconstruction in 3D ultrasound for orthopedic surgery planning

PURPOSE

Derotation varisation osteotomy of the proximal femur in pediatric patients usually relies on 2-dimensional X-ray imaging, as CT and MRI still are disadvantageous when applied in small children either due to a high radiation exposure or the need of anesthesia. This work presents a radiation-free non-invasive tool to 3D-reconstruct the femur surface and measure relevant angles for orthopedic diagnosis and surgery planning from 3D ultrasound scans instead.

METHODS

Multiple tracked ultrasound recordings are segmented, registered and reconstructed to a 3D femur model allowing for manual measurements of caput-collum-diaphyseal (CCD) and femoral anteversion (FA) angles. Novel contributions include the design of a dedicated phantom model to mimic the application ex vivo, an iterative registration scheme to overcome movements of a relative tracker only attached to the skin, and a technique to obtain the angle measurements.

RESULTS

We obtained sub-millimetric surface reconstruction accuracy from 3D ultrasound on a custom 3D-printed phantom model. On a pre-clinical pediatric patient cohort, angular measurement errors were [Formula: see text] and eventually [Formula: see text] for CCD and FA angles, respectively, both within the clinically acceptable range. To obtain these results, multiple refinements of the acquisition protocol were necessary, ultimately reaching success rates of up to 67% for achieving sufficient surface coverage and femur reconstructions that allow for geometric measurements.

CONCLUSION

Given sufficient surface coverage of the femur, clinically acceptable characterization of femoral anatomy is feasible from non-invasive 3D ultrasound. The acquisition protocol requires leg repositioning, which can be overcome using the presented algorithm. In the future, improvements of the image processing pipeline and more extensive surface reconstruction error assessments could enable more personalized orthopedic surgery planning using cutting templates.

Accurate and robust registration method for computer-assisted high tibial osteotomy surgery

PURPOSE

Computer-assisted high tibial osteotomy (HTO) is a frequently used treatment technique for lower extremity orthopedics, and its small incision and low exposure area are major limitations in tibial registration. This work combines skin surface features and gives a suitable registration algorithm based on Iterative Closest Points (ICP) algorithm to improve registration results. Furthermore, the precision, stability and efficiency of the described method is evaluated.

METHODS

After the initialization stage, the bone surface and skin surface data are combined to construct registration features. Then, a steepest perturbation search method is performed after the ICP algorithm (SPS-ICP) to obtain the optimal transformation through several iterations. Finally, the registration result is evaluated by establishing ground-truth through manual landmarks.

RESULTS

Phantom experiments including simulated human tissue show that the proximal fiducial registration error (FRE) of our method can reach 0.80 ± 0.30 mm (mean ± SD) with an overall rotational error < 1° and translational error < 1.5 mm. Furthermore, it remains stable when the point set is sparse. The average registration time is less than 40 s to ensure the high efficiency of surgical operation.

CONCLUSIONS

The approach fully describes a well-defined framework without additional imaging acquisition equipment for Computer-assisted HTO. By the experiment on the basis of a phantom with simulated soft tissue, the proposed method enables the accurate and robust registration of the tibia, and its computation time meets the demands of surgery.

A hybrid feature-based patient-to-image registration method for robot-assisted long bone osteotomy

PURPOSE

The purpose of this study is to provide a simple, feasible and effective patient-to-image registration method for robot-assisted long bone osteotomy, which has rarely been systematically reported. The practical requirement is to meet the accuracy of 1 mm or even higher without bone-implanted markers.

METHODS

A hybrid feature-based registration method termed CR-RAMSICP is proposed. Point-based coarse registration (CR) is accomplished relying on the optical retro-reflective markers attached to the tracked rigid body fixed out of the bone. In surface-based fine registration, an improved iterative closest point (ICP) algorithm based on the range-adaptive matching strategy (termed RAMSICP) is presented to cope with the robust precise matching between the asymmetric patient and image point clouds, which avoids converging to a local minimum.

RESULTS

A series of registration experiments based on the isolated porcine iliums are carried out. The results illustrate that CR-RAMSICP not only significantly outperforms CR and CR-ICP in the accuracy and reproducibility, but also exhibits better robustness to the CR errors and less sensitiveness to the distribution and number of fiducial points located in the patient point cloud than CR-ICP.

CONCLUSION

The proposed registration method CR-RAMSICP can stably satisfy the desired registration accuracy without the use of bone-implanted markers like fiducial screws. Besides, the RAMSICP algorithm used in fine registration is convenient for programming because any complex metrics or models are not involved.

Optimization of virtual and real registration technology based on augmented reality in a surgical navigation system

Background

The traditional navigation interface was intended only for two-dimensional observation by doctors; thus, this interface does not display the total spatial information for the lesion area. Surgical navigation systems have become essential tools that enable for doctors to accurately and safely perform complex operations. The image navigation interface is separated from the operating area, and the doctor needs to switch the field of vision between the screen and the patient’s lesion area. In this paper, augmented reality (AR) technology was applied to spinal surgery to provide more intuitive information to surgeons. The accuracy of virtual and real registration was improved via research on AR technology. During the operation, the doctor could observe the AR image and the true shape of the internal spine through the skin.

Methods

To improve the accuracy of virtual and real registration, a virtual and real registration technique based on an improved identification method and robot-assisted method was proposed. The experimental method was optimized by using the improved identification method. X-ray images were used to verify the effectiveness of the puncture performed by the robot.

Results

The final experimental results show that the average accuracy of the virtual and real registration based on the general identification method was 9.73 ± 0.46 mm (range 8.90–10.23 mm). The average accuracy of the virtual and real registration based on the improved identification method was 3.54 ± 0.13 mm (range 3.36–3.73 mm). Compared with the virtual and real registration based on the general identification method, the accuracy was improved by approximately 65%. The highest accuracy of the virtual and real registration based on the robot-assisted method was 2.39 mm. The accuracy was improved by approximately 28.5% based on the improved identification method.

Conclusion

The experimental results show that the two optimized methods are highly very effective. The proposed AR navigation system has high accuracy and stability. This system may have value in future spinal surgeries.