Global optimization point‐set registration based on translation/rotation decoupling for image‐guided surgery applications

Abstract

Purpose In image‐guided surgery systems, image‐to‐patient spatial registration is to get the spatial transformation between the image space and the actual operating space. Although the image‐to‐patient spatial registration methods using paired point or surface matching are used in some image‐guided neurosurgery systems, the key problem is that the global optimization registration result cannot be achieved. Therefore, this paper proposes a new rotation invariant feature for decoupling rotation and translation space, based on which global optimization point set registration method is proposed. Methods The new rotation invariant features, constructed based on the edges and the angles, are the rotation invariant, which has high feature resolution. Some of them are not only the rotation invariant, but also the translation invariant. To obtain the global optimal solution, branch and bound search strategy is used to search the parameter space of the translation and the computational cost is reduced simultaneously. The registration accuracy of the spatial registration method is analyzed by comparing the difference between the estimated transform and the standard transform to calculate the registration error. Results To validate the performance of the spatial registration method proposed, the registration performance was analyzed by comparing the experimental results with the results of the two mainstream registration methods (the iterative closest point [ICP] registration method and the coherent point drift method). In the experiments, the comparison was based on the registration accuracy and the execution time. We show our registration method can obtain higher accuracy in a shorter time in most cases. At the same time, when using ICP to further refine our results, the ICP method can converge in a very short time, which also shows that our method provides a good initial pose for the ICP method and can help the ICP converge to the global optimal solution faster. Our method can achieve an average rotation error of 0.124 degrees and an average translation error of 0.38 mm on 10 clinical data. Conclusions The results reveal that the surface registration method based on translation rotation decoupling can achieve superior performance regarding both the registration accuracy and the time efficiency in the image‐to‐patient spatial registration.

Rigid point cloud registration based on correspondence cloud for image‐to‐patient registration in image‐guided surgery

“…To avoid the problems associated with paired-point registration depending on fiducial markers,some studies have used rigid point cloud registration methods based on the surface of the patient's anatomical structures for image-to-patient registration. 17,[19][20][21] For example, in IGS for neurosurgery, the surface point cloud in the image space can be obtained by segmenting the head out of the background in pre-operative images, and the corresponding surface point cloud in the patient space can be obtained by 3D laser scanning, and then image-to-patient registration can be done by registering these two point clouds. Before registration, a common way to ensure that densities are similar between two point clouds is to perform voxel downsampling.…”

Puncture and Drainage Surgery for Intracerebral Hemorrhage Guided by 3D Printing Puncture Guide Plate

“…However, it has the disadvantage that the skin markers attached to the patient's head have to be placed before CT scanning and kept in their position until spatial registration is finished, and skin displacement or swelling will lead to large position errors. 12,13 The robotassisted localization method offers simple operation and high precision, with the surgical process primarily controlled by the robot. However, these devices tend to be expensive, leading to their concentration in large hospitals and inaccessibility to most grassroots hospitals.…”

“…The neuronavigation localization method allows surgeons to judge the relative position relationship between real surgical instruments and real patients by observing the position of virtual surgical instruments on the image, so as to help surgeons locate the intracerebral hematoma and guide the puncture. However, it has the disadvantage that the skin markers attached to the patient’s head have to be placed before CT scanning and kept in their position until spatial registration is finished, and skin displacement or swelling will lead to large position errors 12,13 . The robot-assisted localization method offers simple operation and high precision, with the surgical process primarily controlled by the robot.…”