Surgical navigation space registering method based on probe

Abstract

The invention belongs to the field of medical instruments, and particularly relates to a surgical navigation space registering method based on a probe. A point cloud coplanar four-point registering method is adopted in the method, registration is conducted on patient space point cloud obtained by the probe and image space point cloud, and a primary registration result of the two groups of point cloud is obtained; the point cloud iterative registration method is used according to the primary registration result, and the registration of the two groups of point cloud is completed. By means of themethod, it is shown through the result that the method is easy to implement, high in registration efficiency and stable in precision and can be applied in a surgical navigation system to improve theregistration precision of the surgical navigation system.

Description

technical field [0001] The invention belongs to the field of medical devices, and in particular relates to a probe-based spatial registration method that can be used for surgical navigation. Background technique [0002] The prior art discloses that the surgical navigation system can track and display the position of the surgical instrument during the operation, wherein the patient's medical image data space acquired before the operation needs to be able to correspond to the patient's space during the operation. The current mainstream surgical navigation registration methods include point matching and surface matching; the point matching is to determine the corresponding relationship between the patient space and the image space through the correspondence between a small number of marked points in the two spaces. Point matching is widely used, but there are Some inherent defects include: point matching requires manual placement of markers, and then an additional image scan i...

AI Technical Summary

Problems solved by technology

The current mainstream surgical navigation registration methods include point matching and surface matching; the point matching is to determine the corresponding relationship between the patient space and the image space through the correspondence between a small number of marked points in the two spaces. Point matching is widely used, but there are Some inherent defects include: point matching requires manual placement of markers, and then an additional image scan is performed, which increases the financial burden on patients; artificial markers are easy to fall off and shift, resulting in reduced registration accuracy; artificial markers need to be manually registered during registration. Recognition is prone to errors, and it takes a long time to identify marked points; the surface matching does not require artificial markers, and uses the features of the point cloud itself for registration without additional image scanning, but practice shows that the efficiency of registration is relatively low. Low, easy to fall into local optimal solution

[0003] Studies have shown that there are several feasible methods for calculating the direct correspondence between two sets of point clouds; among them, the registration method of four coplanar points is to randomly select a set of four coplanar points in one point cloud, and then Search for matching congruent coplanar four points in a point cloud to complete the registration. This method is not sensitive to the initial position, but the registration efficiency is low, and the randomness of point cloud selection leads to unstable registration results ; The point cloud iterative registration method is to find all the point pair information of two sets of point clouds, and then calculate the new point cloud correspondence according to the point pair, and iterate repeatedly to obtain the final point cloud transformation matrix. The registration speed of this method is relatively fast. Fast, stable accuracy, but high requirements on the initial position, may fall into local optimum

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