scholarly journals New Protocol for Skin Landmark Registration in Image-Guided Neurosurgery: Technical Note

2015 ◽  
Vol 11 (3) ◽  
pp. 376-381 ◽  
Author(s):  
Ian J Gerard ◽  
Jeffery A Hall ◽  
Kelvin Mok ◽  
D Louis Collins
Keyword(s):  
Image Registration ◽  
Technical Note ◽  
The Other ◽  
Registration Accuracy ◽  
Registration Error ◽  
Facial Landmarks ◽  
Target Registration Error ◽  
Image Guided ◽  
Fiducial Registration Error

Abstract BACKGROUND Newer versions of the commercial Medtronic StealthStation allow the use of only 8 landmark pairs for patient-to-image registration as opposed to 9 landmarks in older systems. The choice of which landmark pair to drop in these newer systems can have an effect on the quality of the patient-to-image registration. OBJECTIVE To investigate 4 landmark registration protocols based on 8 landmark pairs and compare the resulting registration accuracy with a 9-landmark protocol. METHODS Four different protocols were tested on both phantoms and patients. Two of the protocols involved using 4 ear landmarks and 4 facial landmarks and the other 2 involved using 3 ear landmarks and 5 facial landmarks. Both the fiducial registration error and target registration error were evaluated for each of the different protocols to determine any difference between them and the 9-landmark protocol. RESULTS No difference in fiducial registration error was found between any of the 8-landmark protocols and the 9-landmark protocol. A significant decrease (P < .05) in target registration error was found when using a protocol based on 4 ear landmarks and 4 facial landmarks compared with the other protocols based on 3 ear landmarks. CONCLUSION When using 8 landmarks to perform the patient-to-image registration, the protocol using 4 ear landmarks and 4 facial landmarks greatly outperformed the other 8-landmark protocols and 9-landmark protocol, resulting in the lowest target registration error.

A Novel Surface Registration for Image-Guided Neurosurgery: Effects of Intervals of Points in Patient Space on Registration Accuracy

2020 ◽  
Vol 10 (6) ◽  
pp. 1466-1472
Author(s):  
Hakje Yoo ◽  
Ahnryul Choi ◽  
Hyunggun Kim ◽  
Joung Hwan Mun
Keyword(s):  
Success Rate ◽  
Point Cloud ◽  
Surgical Navigation ◽  
Spline Interpolation ◽  
Surface Registration ◽  
Registration Accuracy ◽  
Cubic Spline Interpolation ◽  
Registration Error ◽  
Target Registration Error ◽  
Image Guided

Surface registration is an important factor in surgical navigation in determining the success rate and stability of surgery by providing the operator with the exact location of a lesion. The problem of surface registration is that point cloud in the patient space is acquired at irregular intervals due to the operator’s tracking speed and method. The purpose of this study is to analyze the effect of irregular intervals of point cloud caused by tracking speed and method on the registration accuracy. For this study, we created the head phantom to obtain a point cloud in the patient space with a similar object to that of a patient and acquired a point cloud in a total of ten times. In order to analyze the accuracy of registration according to the interval, cubic spline interpolation was applied to the existing point cloud. Additionally, irregular intervals of the point cloud were regenerated into regular intervals. As a result of applying the regenerated point cloud to the surface registration, the surface registration error was not statistically different from the existing point cloud. However, the target registration error significantly lower (p < 0.01). These results indicate that the intervals of point cloud affect the accuracy of registration, and that point cloud with regular intervals can improve the surface registration accuracy.

Technical Note. Effect of Geometrical Distortion Correction in MR on Image Registration Accuracy

1996 ◽  
Vol 20 (4) ◽  
pp. 666-679 ◽  
Author(s):  
Calvin R. Maurer ◽  
Georges B. Aboutanos ◽  
Benoit M. Dawant ◽  
Srikanth Gadamsetty ◽  
Richard A. Margolin ◽  
...  
Keyword(s):  
Image Registration ◽  
Technical Note ◽  
Distortion Correction ◽  
Registration Accuracy ◽  
Geometrical Distortion

Automatic Correspondence Methods towards Point-Based Medical Image Registration

2009 ◽  
pp. 407-425 ◽  
Author(s):  
George K. Matsopoulos
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Medical Image Processing ◽  
The Self ◽  
The Other ◽  
Accurate Estimation ◽  
Registration Accuracy ◽  
Self Organizing Maps ◽  
Point Correspondence ◽  
Self Organizing

The accurate estimation of point correspondences is often required in a wide variety of medical image processing applications including image registration. Numerous point correspondence methods have been proposed, each exhibiting its own characteristics, strengths and weaknesses. This chapter presents a comparative study of four automatic point correspondence methods. The four featured methods are the Automatic Extraction of Corresponding Points approach, the Trimmed Iterated Closest Points scheme, the Correspondence by Sensitivity to Movement technique and the Self-Organizing Maps network. All methods are presented, mainly focusing on their distinct characteristics. An extensive set of dental images, subject to unknown transformations, was employed for the qualitative and quantitative evaluation of the four methods, which was performed in terms of registration accuracy. After assessing all methods, it was deduced that the Self-Organizing Maps approach outperformed in most cases the other three methods in comparison.

Distribution Templates of the Fiducial Points in Image-Guided Neurosurgery

2010 ◽  
Vol 66 (suppl_1) ◽  
pp. ons-143-ons-151
Author(s):  
Wang Manning ◽  
Song Zhijian
Keyword(s):  
Target Point ◽  
Easy Method ◽  
The Real ◽  
Point Pair ◽  
Registration Error ◽  
Target Registration Error ◽  
Image Guided ◽  
The Mean

Abstract Background: Point-pair registration is widely used in an image-guided neurosurgery system. Poor distribution of the fiducial points leads to an increase in the target registration error (TRE). Objective: This study aimed to provide templates consisting of optimized positioning of the fiducial points to reduce the TRE in image-guided neurosurgery. Methods: We divided the head into 6 regions and provided distribution templates of the fiducial points for each of them. A variable termed TREM(r) was used to express the approximate expected square of the TRE at the target point with a specified distribution of fiducial points. We randomly selected 85 patients from 5 hospitals who underwent image-guided neurosurgery and compared the TREM(r) of the real fiducial points with that of the templates. Results: We grouped the patients by hospitals and regions. The mean TREM(r)s of the templates were much smaller than those of the real fiducial points. In each group, the range of the TREM(r) values of the templates was much smaller than that of the real fiducial points. Conclusion: This study provides an easy method to implement a good distribution of the fiducial points to help reduce TRE in image-guided neurosurgery. The templates are simple and exact and can be easily integrated into current workflow.

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