alexa Precision Robotic-Assisted Implantation for Preclinical Stereotactic Neurosurgery | Open Access Journals
ISSN: 2168-9873
Journal of Applied Mechanical Engineering
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Precision Robotic-Assisted Implantation for Preclinical Stereotactic Neurosurgery

Abbaszadeh S1* and Wu HCH2

1Department of Nuclear, Plasma and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

2Department of Neurosurgery, Stanford University, 213 Quarry Road, Palo Alto, CA, USA

*Corresponding Author:
Abbaszadeh S
Department of Nuclear, Plasma and Radiological Engineering
University of Illinois at Urbana-Champaign, Urbana, IL, USA
Tel: 217-244-9738
E-mail: Sabbasza@illinois.edu

Received date: June 26, 2017; Accepted date: August 03, 2017; Published date: August 07, 2017

Citation: Abbaszadeh S, Wu HCH (2017) Precision Robotic-Assisted Implantation for Preclinical Stereotactic Neurosurgery. J Appl Mech Eng 6:279. doi: 10.4172/2168-9873.1000279

Copyright: © 2017 Abbaszadeh S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

During stereotactic neurosurgery procedures, needle insertion or implantation of electrode/cannula/optic fibre requires quick, precise, and accurate identification of insertion location and proper guidance of the needle to accurately reach the target. In this study, we investigate an automated method to locate the entry point of the region of interest. This method leverages a digital image capture system, pattern recognition, and motorized stages. Template matching of known anatomical identifiable regions is used to find regions of interest (e.g., Bregma) in rodents.

Keywords

Stereotactic neurosurgery; Automation; Template matching; Preclinical

Introduction

Stereotactic neurosurgical procedure is a common practice in both preclinical and clinical settings. In animal studies, researchers use it to intervene specific regions of the brain to carry out various experiments of different aims. One example is the use of optogenetics to investigate functions of different brain regions, which involves stereotactic injection of virus and stereotactic implantation of optic fibre. It is also widely used clinically as a diagnostic tool for brain tumor biopsy, and for treatment of various neurologic disorders, including pain, movement disorders (e.g., Parkinson’s disease), epilepsy and psychiatric disorders.

The current state-of-the-art stereotactic procedure in rodents involves many steps of calculation and human estimation, and is not error-free. For example, one of the key step is the identification of the Bregma, an anatomical point on the skull at which the coronal suture is intersected perpendicularly by the sagittal suture, which is used as the main reference point. In theory, it is a point which researchers can pinpoint with high accuracy, but in practice, that is seldom the case (Figures 1a-1c). The sagittal suture, another important landmark, is also never a straight line.

applied-mechanical-engineering-Schematic-region-interest

Figure 1: (a) Schematic of region of interest. (b) Angled view with the Bregma and injection site visible. (c) An example of an image used for template matching.

Identification of the Bregma and the sagittal suture, for example, is a part that requires repetitive checking, is laborious and timeconsuming, and can benefit substantially from automation. Previous work has investigated injection site accuracy, system automation, and registration [1-4].

In this research, we are investigating a technique to speed up this process in animal studies automatically. We present results of automatically identifying features and injection site for a stereotactic procedure in rodents. We also present the automation strategy for a potential system [5,6].

Methods

Figure 2 illustrates the concept of the method used in this work. Rodents were prepared using the standard techniques for the stereotactic procedure (Figure 3). Photos of numerous rodents were acquired to get a sizeable data set. Images are cropped and a template matching technique. Brunelli [7] used to determine the location of the identified structure of interest. The normalized cross-correlation is used to identify the location of the optimal template match. The peak in the correlation plot corresponds to the best match with the template.

applied-mechanical-engineering-associated-correlation

Figure 2: Example template use on an image and the associated correlation plot for a template translated over a 50 × 50 pixel region.

applied-mechanical-engineering-Preclinical-stereotactic

Figure 3: Preclinical stereotactic fixture: (1) heating pad, (2) body temperature probe, (3) ear pins, (4) mouth piece, (5) stereotactic coordinate monitor, (6) x-, y-, and zknobs, (7) stereotactic arm for injection, implantation, etc.

This location is then used to locate the features of interest, such as the Bregma, and determine the injection site.

Template matching works best if the shape is at the same orientation and angle (example variations to the template are shown in Figure 4).

applied-mechanical-engineering-Variations-template

Figure 4: Variations to the template for better matching include length, rotation, and thickness of the different pieces.

Therefore, care was taken to assure similar image samples. A total of 75 images were analysed and MATLAB (MathWorks, Natick, MA) was used for data processing.

The flow diagram of a potential automated system is shown in Figure 5, which includes both location identification as well as injection. First the rodent is prepared according to standard procedures. An image is acquired of the rodent skull. Template matching is applied to the image to identify the location of interest. A virtual grid in the image is used to determine distances. A calibration step is initially carried out before the procedure to translate pixels to real space in the imaging system. The coordinates of the injection site are then fed to the motor control to move the needle into position. Lastly the injection is made.

applied-mechanical-engineering-Flow-diagram

Figure 5: Flow diagram of potential automated system.

Results and Discussion

From the images acquired, there are 3 potential components to apply the template matching to, either the red, green, or blue channels (Figure 6). For the results presented here, the template matching was applied to the blue channel. The template was translated by 100 pixels in x and y directions. The results for several images are shown in Figure 7. The template is overlaid with the image based on the position in the correlation plot with the highest value. Better correlation is represented as brighter value in the correlation plot.

applied-mechanical-engineering-Example-image-separated

Figure 6: Example image separated into RGB components. Subtraction of Red and Green, and Red and Blue components.

applied-mechanical-engineering-template-overlaid

Figure 7: (Top) Example images with the template overlaid on top of image at position determined using (bottom) correlation plots.

Conclusion

In this paper, we demonstrate a method for identifying anatomical features for the stereotactic procedure. This method has the potential to reduce effort and increase accuracy for a time laborious, and errorprone process. To accomplish this task a template matching method is used. The correlation of the template with the image is used to pinpoint the location of anatomical features. A fully automated procedure is proposed to automate the location detection and injection. Future work involves optimizing the template and minimizing the separation distance between the location found using this method and the location determined by human professionals.

References

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