Analysis Of The Errors In Neuronavigation And Research On Improving The Accuracy Of Space Registration

Neurosurgical disease such as brain tumor is a big threat to human health, and surgical operation is the most effective treatment for them. Neurosurgical operation is one of the most difficult and risky operations, since neurosurgeons need not only remove the focus but also protect the normal structures in order to minimize the risk of serious impairment, paralysis, or even death. It is difficult to achieve this goal by the traditional way of operation, and according to statistics in the US, cranial procedures have high complication rates of 10 to 15%.Image Guided Neurosurgery System (IGNS) is an effective instrument to mitigate the risk of complications and improve the quality of neurosurgery. IGNS constructs an image space from the medical images of the patient who will undergo the operation and registers it to the patient space where the real patient exists before operation. During the procedures, IGNS tracks the surgical tools in the patient space and transform the coordinates of them into the image space to generate virtual surgical tools and overlay them on the images. Therefore, the relative position between the images and the virtual tools in the image space is used to help the surgeons localize the position of the real surgical tools relative to the anatomical structures of the real patient. IGNS can help neurosurgeons plan the surgical approaches, localize the position and the extent of the focus and avoid the impairment of normal brain tissue.IGNS can not only help neurosurgeons to minimize the risk of complication but also help to reduce the invasiveness and speed up recovery. Accurate localization is the basis of all these functions, and the accuracy of localization is the most important performance index of IGNS. It has been an important research field to improve the localization accuracy. In this thesis, we first analyze and classify all the factors that may contribute to the inaccuracy of IGNS and then propose several new approaches to optimize the space registration process, which is one of the main sources of the errors. The whole thesis includes the following four parts:Part 1 is the analysis and classification of the errors in IGNS. In this part, we classify all the errors into two groups according to the working principle of IGNS. The first group contains the errors caused by the differences between the anatomical structures in the images and that of the real patient, and the second group contains the errors occurring in transforming the position of surgical tools from the patient space to the image space. The space registration process is a major source of the errors in the second group, and the following parts of this thesis are mainly about how to minimize the errors in this group. In this part, we continue to classify the errors in each group into two subgroups, and discuss 16 errors falling in different subgroups.In part 2, we proposed two approaches to automatically localize the fiducial point from artificial markers. Point-based registration is widely used in IGNS to register the patient space to the image space, and the surgeons need identify the fiducial points in the image space manually in all commercially available systems. The accuracy of the manual identification relies heavily on the experience of the surgeons, and the process of identification cost relatively long time. In this part, we proposed two automatic approaches based on clustering of shape index and projection of surface patch, respectively. In the first approach, we segmented the volume data according to the shape index and curvedness of each voxel and identified the coordinates of the fiducial points by clustering the segmentation results. We tested our methods by 21 CT volume of the patients who underwent image guided neurosurgery, and all 113 fiducial points from artificial markers were identified and the average localization error was 0.43 mm. In the second approach, we identified the fiducial points by comparing the projection image of the artificial marker with that of surface patches extracted from volume data. We tested this method with both CT and MRI volume data. Sixty nine fiducial points were accurately localized from 75 artificial markers in CT data, and 47 fiducial points were accurately localized from 52 artificial markers in MRI data. The localization error of all the other fiducial points was 1 or 2^1/2 voxels.Part 3 is about the optimization of the spatial distribution of fiducial points. What the neurosurgeons care most in the application of IGNS is Target Registration Error (TRE), and researches indicates that TRE is not only determined by the registration accuracy of the fiducial points but also influenced by the number and the distribution of them. In this part, we first proposed an interactive approach to optimize the spatial distribution of fiducial points to improve the TRE in the region of interest, and experiments showed that this approach could reduce half of the TRE resulting from improper distribution of fiducial points. On the basis of this interactive approach, we continued to introduce two sets of distribution templates that were optimized with regard to different parts of head and the whole head, respectively. Neurosurgeons can arrange fiducial points on the surface of the patient' s head directly with the guidance of the templates to achieve a distribution optimized with regard to the target region of operation.Part 4 is the analysis of the property of the TRE in surface based registration. Surface based registration is a relatively new approach of space registration in IGNS. Intrinsic features of patients are used in surface based registration, and it need not artificial markers and dedicated image acquisition after attaching the markers. However, surface based registration is not widely used clinically, which is mainly because that its registration accuracy is not stable and the neurosurgeons are not very clear about the properties of its TRE. In this part, we simulated the process of surface based registration in the image space to study the properties of its TRE and gave several practical guidelines for clinical application of it to guarantee the registration accuracy in the region of interest.In conclusion, space registration is one of the key techniques in IGNS, and the accuracy of the space registration to a large extent determines the accuracy of IGNS. How to achieve an accurate and convenient space registration has been an important research area in the field of IGNS. In this paper, we analyzed all the errors in IGNS and proposed several scientific and effective approaches to improve its accuracy. With the research in this thesis as the major contents, four papers were published in and one paper was accepted by SCI indexed journals, and one paper was published in EI indexed journals. Tow other papers were submitted to SCI indexed journals.