Estimating Maximum Target Registration Error Under Uniform Restriction of Fiducial Localization Error in Image Guided System

The accuracy and learning curves of active and passive dynamic navigation-guided dental implant surgery: An in vitro study

OBJECTIVES

Infrared dynamic navigation principles can be categorized into active and passive navigation systems based on whether the surgical instruments can emit or only reflect light, respectively. This in vitro study aimed to compare the accuracy of implant placement and the learning curves of both active and passive dynamic navigation systems using different registration methods.

METHODS

Implants (n=704) were placed in 64 sets of models and divided into active (Yizhime, DCARER, Suzhou, China) and passive (Iris-Clinic, EPED, Kaohsiung, China) dynamic navigation groups. Both marker point-based registration (M-PBR) and feature point-based registration (F-PBR) were employed by two groups mentioned above. Based on preoperative and postoperative cone-beam computed tomography imaging, the coronal, midpoint, apical, and angular deviations were analyzed from 2D and 3D views. The operation time was recorded for each group.

RESULTS

The active dynamic navigation group exhibited significantly greater accuracy than the passive dynamic navigation group for outcome variables (angular deviation, 4.13 ± 2.39° and 4.62 ± 3.32°; coronal global deviation, 1.48 ± 0.60 and 1.86 ± 1.12 mm; apical global deviation, 1.75 ± 0.81 and 2.20 ± 1.68 mm, respectively). Significant interaction effects were observed for both registration methods and four quadrants with different dynamic navigation systems. Learning curves for the two dynamic navigation groups approached each other after 12 procedures, and finally converged after 27 procedures.

CONCLUSIONS

The accuracy of active dynamic navigation system was superior to that of passive dynamic navigation system. Different combinations of dynamic navigation systems, registration methods, and implanted quadrants displayed various interactions.

CLINICAL SIGNIFICANCE

Our findings could provide guidance for surgeons in choosing an appropriate navigation system use in various implant surgeries. Furthermore, the time required by surgeons to master the technique was calculated for reference. Nevertheless, there are certain limitations to this in vitro study, and therefore further research is required.

Target registration error reduction for percutaneous abdominal intervention

A real-time methodology that finds spatio-temporal correspondence between the positions of the target point in the pre-treatment 3DCT image and during the procedure was proposed. It based on minimizing the target registration error in III tier registration circuits. Particle Swarm Optimization and Differential Evaluation were used to find optimal values of Elastic Body Spline parameters in the generation of abdominal deformation field. Different transformation classes have been tested: rigid, affine, Thin Plate Spline, Elastic Body Spline. The lowest TRE was obtained for the swarm optimization algorithm - differential evolution for the rigid and affine version: 3.47 and 3.73 mm, respectively.

General first-order target registration error model considering a coordinate reference frame in an image-guided surgical system

Point-based rigid registration (PBRR) techniques are widely used in many aspects of image-guided surgery (IGS). Accurately estimating target registration error (TRE) statistics is of essential value for medical applications such as optically surgical tool-tip tracking and image registration. For example, knowing the TRE distribution statistics of surgical tool tip can help the surgeon make right decisions during surgery. In the meantime, the pose of a surgical tool is usually reported relative to a second rigid body whose local frame is called coordinate reference frame (CRF). In an n-ocular tracking system, fiducial localization error (FLE) should be considered inhomogeneous, that means FLE is different between fiducials, and anisotropic that indicates FLE is different in all directions. In this paper, we extend the TRE estimation algorithm relative to a CRF from homogeneous and anisotropic to heterogeneous FLE cases. Arbitrary weightings can be assumed in solving the registration problems in the proposed TRE estimation algorithm. Monte Carlo simulation results demonstrate the proposed algorithm's effectiveness for both homogeneous and inhomogeneous FLE distributions. The results are further compared with those using the other two algorithms. When FLE distribution is anisotropic and homogeneous, the proposed TRE estimation algorithm's performance is comparable with that of the first one. When FLE distribution is heterogeneous, proposed TRE estimation algorithm outperforms the other two classical algorithms in all test cases when ideal weighting scheme is adopted in solving two registrations. Possible clinical applications include the online estimation of surgical tool-tip tracking error with respect to a CRF in IGS. Graphical Abstract This paper provides the target registration error model considering a coordinate reference frame in surgical navigation.

Generation of a local lung respiratory motion model using a weighted sparse algorithm and motion prior-based registration

Respiration-introduced tumor location uncertainty is a challenge in lung percutaneous interventions, especially for the respiratory motion estimation of the tumor and surrounding vessel structures. In this work, a local motion modeling method is proposed based on whole-chest computed tomography (CT) and CT-fluoroscopy (CTF) scans. A weighted sparse statistical modeling (WSSM) method that can accurately capture location errors for each landmark point is proposed for lung motion prediction. By varying the sparse weight coefficients of the WSSM method, newly input motion information is approximately represented by a sparse linear combination of the respiratory motion repository and employed to serve as prior knowledge for the following registration process. We have also proposed an adaptive motion prior-based registration method to improve the motion prediction accuracy of the motion model in the region of interest (ROI). This registration method adopts a B-spline scheme to interactively weight the relative influence of the prior knowledge, model surface and image intensity information by locally controlling the deformation in the CTF image region. The proposed method has been evaluated on 15 image pairs between the end-expiratory (EE) and end-inspiratory (EI) phases and 31 four-dimensional CT (4DCT) datasets. The results reveal that the proposed WSSM method achieved a better motion prediction performance than other existing lung statistical motion modeling methods, and the motion prior-based registration method can generate more accurate local motion information in the ROI.

SPST-CNN: Spatial pyramid based searching and tagging of liver's intraoperative live views via CNN for minimal invasive surgery

Laparoscopic liver surgery is challenging to perform because of compromised ability of the surgeon to localize subsurface anatomy due to minimal invasive visibility. While image guidance has the potential to address this barrier, intraoperative factors, such as insufflations and variable degrees of organ mobilization from supporting ligaments, may generate substantial deformation. The navigation ability in terms of searching and tagging within liver views has not been characterized, and current object detection methods do not account for the mechanics of how these features could be applied to the liver images. In this research, we have proposed spatial pyramid based searching and tagging of liver's intraoperative views using convolution neural network (SPST-CNN). By exploiting a hybrid combination of an image pyramid at input and spatial pyramid pooling layer at deeper stages of SPST-CNN, we reveal the gains of full-image representations for searching and tagging variable scaled liver live views. SPST-CNN provides pinpoint searching and tagging of intraoperative liver views to obtain up-to-date information about the location and shape of the area of interest. Downsampling input using image pyramid enables SPST-CNN framework to deploy input images with a diversity of resolutions for achieving scale-invariance feature. We have compared the proposed approach to the four recent state-of-the-art approaches and our method achieved better mAP up to 85.9%.

A statistical weighted sparse-based local lung motion modelling approach for model-driven lung biopsy

PURPOSE

Lung biopsy is currently the most effective procedure for cancer diagnosis. However, respiration-induced location uncertainty presents a challenge in precise lung biopsy. To reduce the medical image requirements for motion modelling, in this study, local lung motion information in the region of interest (ROI) is extracted from whole chest computed tomography (CT) and CT-fluoroscopy scans to predict the motion of potentially cancerous tissue and important vessels during the model-driven lung biopsy process.

METHODS

The motion prior of the ROI was generated via a sparse linear combination of a subset of motion information from a respiratory motion repository, and a weighted sparse-based statistical model was used to preserve the local respiratory motion details. We also employed a motion prior-based registration method to improve the motion estimation accuracy in the ROI and designed adaptive variable coefficients to interactively weigh the relative influence of the prior knowledge and image intensity information during the registration process.

RESULTS

The proposed method was applied to ten test subjects for the estimation of the respiratory motion field. The quantitative analysis resulted in a mean target registration error of 1.5 (0.8) mm and an average symmetric surface distance of 1.4 (0.6) mm.

CONCLUSIONS

The proposed method shows remarkable advantages over traditional methods in preserving local motion details and reducing the estimation error in the ROI. These results also provide a benchmark for lung respiratory motion modelling in the literature.