Two-stage point-based registration method between ultrasound and CT imaging of the liver based on ICP and unscented Kalman filter: a phantom study

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.

A Symmetry-Based Superposition Method for Planning and Surgical Outcome Assessment

Computer-aided surgical planning has been widely used to increase the safety and predictability of surgery. The validation of the target of surgical planning to surgical outcomes on a patient-specific model is an important issue. The aim of this research was to develop a robust superposition method to assess the deviation of planning and outcome by using the symmetrical characteristic of the affected target. The optimal symmetry plane (OSP) of an object is usually used to evaluate the degree of symmetry of an object. We proposed a refined OSP-based contouring method to transfer a complex three-dimensional superposition operation into two dimensions. We compared the typical iterative closest point (ICP) algorithm with the refined OSP-based contouring method and examined the differences between them. The results using the OSP-based method were much better than the traditional method. As for processing time, the OSP-based contouring method was 11 times faster than the ICP method overall. The proposed method was not affected by the metallic artifacts from medical imaging or geometric changes due to surgical intervention. This technique can be applied for post-operative assessment, such as quantifying the differences between surgical targets and outcomes as well as performing long-term medical follow-up.

Performance Analysis of Localization Algorithms for Inspections in 2D and 3D Unstructured Environments Using 3D Laser Sensors and UAVs

One of the most relevant problems related to Unmanned Aerial Vehicle’s (UAV) autonomous navigation for industrial inspection is localization or pose estimation relative to significant elements of the environment. This paper analyzes two different approaches in this regard, focusing on its application to unstructured scenarios where objects of considerable size are present, such as a truck, a wind tower, an airplane, a building, etc. The presented methods require a previously developed Computer-Aided Design (CAD) model of the main object to be inspected. The first approach is based on an occupancy map built from a horizontal projection of this CAD model and the Adaptive Monte Carlo Localization (AMCL) algorithm to reach convergence by considering the likelihood field observation model between the 2D projection of 3D sensor data and the created map. The second approach uses a point cloud prior map of the 3D CAD model and a scan-matching algorithm based on the Iterative Closest Point Algorithm (ICP) and the Unscented Kalman Filter (UKF). The presented approaches have been extensively evaluated using simulated as well as previously recorded real flight data. We focus on aircraft inspection as a test example, but our results and conclusions can be directly extended to other applications. To support this assertion, a truck inspection has been performed. Our tests reflected that creating a 2D or 3D map from a standard CAD model and using a 3D laser scan on the created maps can optimize the processing time, resources and improve robustness. The techniques used to segment unexpected objects in 2D maps improved the performance of AMCL. In addition, we showed that moving around locations with relevant geometry after take-off when running AMCL enabled faster convergence and high accuracy. Hence, it could be used as an initial position estimation method for other localization algorithms. The ICP-NL method works well in environments with elements other than the object to inspect, but it can provide better results if some techniques to segment the new objects are applied. Furthermore, the proposed ICP-NL scan-matching method together with UKF performed faster, in a more robust manner, than NDT. Moreover, it is not affected by flight height. However, ICP-NL error may still be too high for applications requiring increased accuracy.

Dynamic Hepatocellular Carcinoma Model Within a Liver Phantom for Multimodality Imaging

Introduction

Hepatocellular carcinoma (HCC) is one of the most common cancer in the world, and the effectiveness of its treatment lies in its detection in its early stages. The aim of this study is to mimic HCC dynamically through a liver phantom and apply it in multimodality medical imaging techniques including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound.

Methods and materials

The phantom is fabricated with two main parts, liver parenchyma and HCC inserts. The liver parenchyma was fabricated by adding 2.5 wt% of agarose powder combined with 2.6 wt% of wax powder while the basic material for the HCC samples was made from polyurethane solution combined with 5 wt% glycerol. Three HCC samples were inserted into the parenchyma by using three cylinders implanted inside the liver parenchyma. An automatic injector is attached to the input side of the cylinders and a suction device connected to the output side of the cylinders. After the phantom was prepared, the contrast materials were injected into the phantom and imaged using MRI, CT, and ultrasound.

Results

Both HCC samples and liver parenchyma were clearly distinguished using the three imaging modalities: MRI, CT, and ultrasound. Doppler ultrasound was also applied through the HCC samples and the flow pattern was observed through the samples.

Conclusion

A multimodal dynamic liver phantom, with HCC tumor models have been fabricated. This phantom helps to improve and develop different methods for detecting HCC in its early stages.

Intraoperative Correction of Liver Deformation Using Sparse Surface and Vascular Features via Linearized Iterative Boundary Reconstruction

During image guided liver surgery, soft tissue deformation can cause considerable error when attempting to achieve accurate localization of the surgical anatomy through image-to-physical registration. In this paper, a linearized iterative boundary reconstruction technique is proposed to account for these deformations. The approach leverages a superposed formulation of boundary conditions to rapidly and accurately estimate the deformation applied to a preoperative model of the organ given sparse intraoperative data of surface and subsurface features. With this method, tracked intraoperative ultrasound (iUS) is investigated as a potential data source for augmenting registration accuracy beyond the capacity of conventional organ surface registration. In an expansive simulated dataset, features including vessel contours, vessel centerlines, and the posterior liver surface are extracted from iUS planes. Registration accuracy is compared across increasing data density to establish how iUS can be best employed to improve target registration error (TRE). From a baseline average TRE of 11.4 ± 2.2 mm using sparse surface data only, incorporating additional sparse features from three iUS planes improved average TRE to 6.4 ± 1.0 mm. Furthermore, increasing the sparse coverage to 16 tracked iUS planes improved average TRE to 3.9 ± 0.7 mm, exceeding the accuracy of registration based on complete surface data available with more cumbersome intraoperative CT without contrast. Additionally, the approach was applied to three clinical cases where on average error improved 67% over rigid registration and 56% over deformable surface registration when incorporating additional features from one independent tracked iUS plane.

Comparison of real-time contrast-enhanced ultrasonography and standard ultrasonography in liver cancer microwave ablation

Primary liver cancer has a high incidence and high mortality rates, and currently the only viable option is surgery, although there are a number of difficulties related to this method. The aim of the present study was to investigate the potential advantages of the real-time contrast-enhanced ultrasonography (CEUS) for microwave ablation of primary liver cancer. One hundred patients with primary liver cancer were included in the study. The patients were divided into the ordinary ultrasonography and the CEUS groups. For the ordinary ultrasonography group, the ordinary ultrasonography-guided microwave ablation method was used, while microwave ablation under the guidance of CEUS was conducted for the CEUS group. The size of lesions and clearness of the tumor boundary prior to surgery in the two groups were compared. Additionally, postoperative complications and the survival rate were monitored. Lesion boundary areas measured by CEUS were significantly larger than those measured with ordinary ultrasonography. The incidence rate of postoperative pain, fever, intra-abdominal hemorrhage and infection and other complications in the ordinary ultrasonography group were significantly higher than that in the CEUS group. The tumor recurrence rate in the CEUS group was significantly lower than that in the ordinary ultrasonography group. Seventy-two percent of patients in the CEUS group showed no progress, compared to 48% of in the ordinary ultrasonography group. The progress-free survival rate in the CEUS group after 6 months was significantly higher than that in the ordinary ultrasonography group. Disease-free survival time in the CEUS group was considerably longer than the control group. In conclusion, the guidance of real-time CEUS on the primary liver cancer microwave ablation treatment can achieve good intra-operative results. It offers a real-time guidance effect, improves survival time and reduces the incidence of complications.

A Robust and Accurate Two-Step Auto-Labeling Conditional Iterative Closest Points (TACICP) Algorithm for Three-Dimensional Multi-Modal Carotid Image Registration

Atherosclerosis is among the leading causes of death and disability. Combining information from multi-modal vascular images is an effective and efficient way to diagnose and monitor atherosclerosis, in which image registration is a key technique. In this paper a feature-based registration algorithm, Two-step Auto-labeling Conditional Iterative Closed Points (TACICP) algorithm, is proposed to align three-dimensional carotid image datasets from ultrasound (US) and magnetic resonance (MR). Based on 2D segmented contours, a coarse-to-fine strategy is employed with two steps: rigid initialization step and non-rigid refinement step. Conditional Iterative Closest Points (CICP) algorithm is given in rigid initialization step to obtain the robust rigid transformation and label configurations. Then the labels and CICP algorithm with non-rigid thin-plate-spline (TPS) transformation model is introduced to solve non-rigid carotid deformation between different body positions. The results demonstrate that proposed TACICP algorithm has achieved an average registration error of less than 0.2mm with no failure case, which is superior to the state-of-the-art feature-based methods.

Iterative most-likely point registration (IMLP): a robust algorithm for computing optimal shape alignment

We present a probabilistic registration algorithm that robustly solves the problem of rigid-body alignment between two shapes with high accuracy, by aptly modeling measurement noise in each shape, whether isotropic or anisotropic. For point-cloud shapes, the probabilistic framework additionally enables modeling locally-linear surface regions in the vicinity of each point to further improve registration accuracy. The proposed Iterative Most-Likely Point (IMLP) algorithm is formed as a variant of the popular Iterative Closest Point (ICP) algorithm, which iterates between point-correspondence and point-registration steps. IMLP’s probabilistic framework is used to incorporate a generalized noise model into both the correspondence and the registration phases of the algorithm, hence its name as a most-likely point method rather than a closest-point method. To efficiently compute the most-likely correspondences, we devise a novel search strategy based on a principal direction (PD)-tree search. We also propose a new approach to solve the generalized total-least-squares (GTLS) sub-problem of the registration phase, wherein the point correspondences are registered under a generalized noise model. Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares. We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP. The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes.

A projected landmark method for reduction of registration error in image-guided surgery systems

PURPOSE

Image-guided surgery systems are limited by registration error, so practical and effective methods to improve accuracy are necessary. A projection point-based method for reducing the surface registration error in image-guided surgery was developed and tested.

METHODS

Checkerboard patterns are projected on visible surfaces to create projected landmarks over a region of interest. Surface information thus becomes available in the form of point clouds of surface point coordinates with submillimeter resolution. The reconstructed 3D point cloud is registered using iterative closest point (ICP) approximation to a 3D point cloud extracted from preoperative CT images of the same region of interest. The projected landmark surface registration method was compared with two other methods using a facial surface phantom: (a) landmark registration using anatomical features, and (b) surface matching based on an additional 40 surface points.

RESULTS

The mean error for the projected landmark surface registration method was 0.64 mm, which was 47.4 and 35.3 % lower relative to mean errors of the anatomical landmark registration and the surface-matching methods, respectively. After applying the proposed method, using target registration error as a gold standard, the resulting mean error was 1.1 mm or a reduction of 61.2 % compared to the anatomical landmark registration.

CONCLUSION

Optical checkerboard pattern projection onto visible surfaces was used to acquire surface point clouds for image-guided surgery registration. A projected landmark method eliminated the effects of unwanted and overlapping points by acquiring the desired points at specific locations. The results were more accurate than conventional landmark or surface registration.