Enhancing Brain Tumor Surgery with Augmented Reality: Computational Modeling and Simulation of the CLEAR Biopsy Guidance System

Author: Waqas Javaid

Abstract

The CLEAR Biopsy Guidance System is an advanced augmented reality (AR) surgical navigation tool designed for the Microsoft HoloLens 2, aimed at enhancing neurosurgical brain biopsy procedures. This system provides an AR environment that overlays crucial patient data, surgical plans, and real-time biopsy guidance directly onto the patient’s head. Utilizing Unity for the AR experience, 3D Slicer for converting MRI data into 3D geometry, and Autodesk Maya for refining 3D models, the system ensures precise mapping of digital objects and physical instruments. Our development process faced challenges in spatial tracking and real-time rendering, but ultimately, the CLEAR system offers significant improvements in patient safety, surgical planning, and execution. The integration of vectorized material properties for healthy and cancerous tissues further enhances the precision of haptic feedback during biopsies. Validation of the CLEAR system was conducted using a patient dataset of 496 MRI scans from Bab El Oued university hospital center, achieving a segmentation accuracy of 98.61%. The system successfully performed real-time brain tumor augmentation with a reprojection accuracy of 97%. These results demonstrate the effectiveness of our methodology in accurate 3D brain tumor reconstruction and augmented reality visualization. Our interdisciplinary team, combining expertise in neurosurgery, medical imaging, real-time rendering, and design, underscores the strong potential for AR applications in improving surgical outcomes and efficiency. The CLEAR Biopsy Guidance System stands as a promising solution for advancing neurosurgical procedures, with future work aimed at further refining spatial tracking and enhancing computational capabilities for clinical-grade performance.

A. Introduction

In recent years, augmented reality (AR) technology has emerged as a transformative tool in various fields, including healthcare. AR’s ability to overlay digital information onto the physical world offers immense potential to enhance precision and efficiency in medical procedures. Neurosurgery, in particular, stands to benefit significantly from AR due to the complexity and sensitivity of brain surgeries. Accurate navigation and visualization are paramount in these procedures to ensure patient safety and improve surgical outcomes. The CLEAR Biopsy Guidance System was conceived to address these needs by integrating AR technology into neurosurgical brain biopsy procedures. Our system provides a comprehensive AR environment that combines patient-specific data, surgical planning, and real-time guidance directly onto the patient’s anatomy.

The CLEAR system leverages the advanced capabilities of the Microsoft HoloLens 2 to deliver an immersive and interactive experience for surgeons. Using Unity, we developed the AR application to visualize patient data and surgical plans in real time. For the imaging components, 3D Slicer software was employed to convert 2D MRI data into detailed 3D geometries, while Autodesk Maya was used to refine these models by re-meshing and removing unnecessary outer skull surfaces. This meticulous process ensures that the digital overlay aligns accurately with the patient’s physical anatomy, facilitating precise surgical interventions. Additionally, the user interface was designed in Sketch to provide an intuitive and user-friendly experience for clinicians.

One of the key challenges in developing the CLEAR system was achieving precise spatial tracking and real-time rendering. The system required accurate mapping of digital objects and physical instruments, which is critical for effective AR guidance. To address this, we implemented facial feature tracking to calibrate the camera and estimate its position and orientation based on a set of 3D points and their corresponding 2D projections. This approach allowed us to display the reconstructed brain tumor model at the exact location of the patient’s anatomy, ensuring accurate and real-time augmentation. Furthermore, we incorporated haptic feedback mechanisms that differentiate between healthy and cancerous tissues by vectorizing material properties, enhancing the tactile feedback for surgeons during biopsy procedures. The integration of these technologies underscores the multidisciplinary nature of the project, combining expertise in neurosurgery, medical imaging, real-time rendering, and design to create a robust and effective AR surgical navigation tool.

 B. Methodology

i. System Overview

ii. The CLEAR system consists of three main components:

1. Patient Safety: Ensures accurate patient identification and displays essential patient information.
2. Surgical Planning and Review: Integrates patient-specific radiological imaging and procedural plans.
3. AR-Guided Biopsy: Projects the planned biopsy trajectory and imaging data directly onto the patient’s head for real-time guidance.

iii. Development Tools

• • Hardware: Microsoft HoloLens 2
  • Software: Unity, 3D Slicer, Autodesk Maya, Sketch

iv. Imaging and 3D Reconstruction

1. Preprocessing:
   1. Gaussian Filter: Applied to MRI scans to mitigate intensity heterogeneity.
   2. Equation: 
2. Segmentation:
   1. Active Geometric Contour Models: Utilized for tumor boundary detection.
   2. Morphological Operations: Applied to refine segmentation results.
3. 3D Reconstruction:
   1. 3D Slicer Software: Converts 2D MRI data into 3D models.
   2. Autodesk Maya: Re-meshes models and removes unnecessary outer skull surfaces.

v. AR Integration

1. Facial Feature Tracking: Uses facial features to track the subject and calibrate the camera.
2. Camera Pose Estimation: Estimates camera position and orientation using 3D points and their 2D projections.

vi. Haptic Feedback Integration

1. 1. Material Properties: Vectorized properties for healthy and cancerous tissues.
   2. Haptic Feedback: Differentiates between healthy and cancerous tissues during biopsy.
   3. Equation for Haptic Force: where k is the stiffness of the material.

vii. Equations

1. 1. Gaussian Filter:

2. Active Contour Model:

3. Haptic Force:

Flowchart figure:  Brain Tumor Surgery with Augmented Reality, Computational Modeling and Simulation of the CLEAR Biopsy Guidance System

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This above flowchart explains that how the brain tumor surgery will perform and what steps will be followed to do the complete clear biopsy based system.

C. CLEAR – Augmented Reality Guided Biopsy

1.  Patient Identification and Information

This initial stage involves collecting patient details, including medical history and identification. This information is crucial for ensuring patient safety and accurate procedure planning.

2. 2D MRI Data Acquisition

In this stage, 2D MRI scans of the patient’s brain are acquired. These scans serve as the foundational data for creating 3D models of the brain structures.

3. 3D Reconstruction with 3D Slicer

The acquired 2D MRI data is then processed using 3D Slicer software to generate 3D geometric models of the brain. This conversion is essential for accurate visualization and planning.

4. Re-meshing and Editing with Autodesk Maya

The 3D models generated in the previous step are imported into Autodesk Maya for re-meshing and editing. This step involves removing outer skull surfaces and enhancing the geometry to ensure clarity and precision in the AR environment.

5. Unity Integration

After refining the 3D models, they are imported into Unity, where the AR experience is built. This stage involves coding in C to create an interactive and responsive AR environment.

6. UI Development with Sketch Software

The user interface (UI) for the AR application is developed using Sketch soft- ware. The UI design is focused on providing an intuitive and user-friendly experience for the surgeons.

7. Surgical Planning and Review

In this stage, the system is used for surgical planning and review. Patient- specific radiological imaging and procedure planning information are reviewed to ensure a precise and safe biopsy procedure.

8. AR Guided Biopsy Procedure

Finally, during the biopsy procedure, the AR system overlays the planned trajectory and radiological imaging directly onto the patient’s head. This real-time guidance helps the surgeon perform the biopsy with enhanced precision and safety.

D. Computational Equations for Brain Tumor Segmentation

Our study focuses on the efficient segmentation of brain tumor classes using Magnetic Resonance Imaging (MRI) and incorporates a three-stage approach: preprocessing, segmentation, and 3D reconstruction & AR display.

1. Segmentation

Active Geometric Contour Models (Snakes)

The active contour model is used to segment the tumor. The energy functional for the snake is given by:

Where represents the parameterized contour, α and β are the weights for the internal energy terms controlling the elasticity and rigidity of the snake, and  is the external energy derived from the image.

The internal energy is:

The external energy typically includes the image gradient and is defined as:

2. 3D Reconstruction

The transformation of 2D MRI data into 3D models involves several computational steps, including:

Where Iseg represents the segmented image data at position z.

3. Surface Reconstruction

The Marching Cubes algorithm is used for surface reconstruction:

Where f (x, y, z) is an implicit function representing the tumor boundary.

4. AR Display Transformation

The transformation from the 3D model coordinates to the AR display coordinates is given by:

Where (T_model – AR) is the transformation matrix, and p_model  and p_AR are the points in the model and AR coordinate systems, respectively.

5. Preprocessing

a. Gaussian Filter Application

A Gaussian filter is applied to the MRI images to mitigate intensity heterogeneity. The Gaussian function in 2D is defined as:

Where (x, y) are the coordinates of the pixel and σ is the standard deviation of the Gaussian distribution.

The filtered image  is obtained by convolving the MRI image I with the Gaussian kernel G:

Where k is the kernel size.

b. Morphological Operations

To refine the segmentation, morphological operations such as dilation and erosion are applied. For a binary image B:

Where S is the structuring element.

c. Validation and Accuracy

The accuracy of the segmentation results is evaluated using the Dice Similarity Coefficient (DSC):

Where A is the set of true positive pixels and B is the set of predicted pixels.

d. Mathematical Equations for Viscoelastic Properties of Tumor and Healthy Tissues

Viscoelasticity describes materials that exhibit both viscous and elastic characteristics when undergoing deformation. For biological tissues, the standard linear solid (SLS) model is commonly used. The viscoelastic behavior can be expressed through the following constitutive equations:

1. Constitutive Equation for Viscoelastic Materials:

2. Relaxation Modulus:

e. Mathematical Equations and MATLAB Algorithms for Cutting, Grasping, and Holding Soft Tissues

i. Cutting:

The cutting process in soft tissues can be simulated using the Extended Finite Element Method (XFEM) or Phase Field Method (PFM). The governing equations involve the strain energy release rate and the fracture toughness of the material.

MATLAB Algorithm for Cutting:

% XFEM Cutting Algorithm

function [updated_mesh, cut_path] = perform_cutting(mesh, cut_start, cut_end)

% Inputs:

%   mesh – initial finite element mesh

%   cut_start, cut_end – coordinates of the cut

% Initialize variables

cut_path = [];

% Loop through elements to check for intersections with the cut line

for elem = 1:size(mesh.Elements,1)

nodes = mesh.Nodes(mesh.Elements(elem,:), :);

[isIntersect, intersection_points] = check_intersection(nodes, cut_start, cut_end);

if isIntersect

% Update the mesh and record the cut path

updated_mesh = update_mesh(mesh, intersection_points);

cut_path = [cut_path; intersection_points];

end

end

end

ii. Grasping and Holding:

For grasping and holding, contact mechanics principles are applied, where the contact force is calculated based on the deformation of the soft tissue.

f. FEA Modeling Using MATLAB PDE Toolbox:

Finite Element Analysis (FEA) is employed to model the deformation of soft tissues. The MATLAB PDE Toolbox can be used to define the geometry, apply boundary conditions, and solve the partial differential equations (PDEs) governing the tissue deformation.

Figure 1: Tumor segmentation Finite Element Analysis (FEA) modeling using partial differential equations (PDEs) governing the tissue deformation

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Figure 1 illustrates the tumor segmentation process integrated with Finite Element Analysis (FEA) modeling for medical image-based tissue deformation analysis. The MRI brain image is first processed to locate the tumor region using bounding box detection and SVM-based classification techniques. After classification, the tumor is segmented accurately from the surrounding brain tissues to obtain the affected region. The segmented tumor geometry is then utilized in Partial Differential Equation (PDE)-based FEA modeling to analyze tissue deformation, stress distribution, and biomechanical behavior within the brain structure. This approach enhances the accuracy of tumor localization and supports advanced medical diagnosis and treatment planning.

g. Haptic Vibration Differences for Healthy and Cancerous Tissues

i. Haptic Feedback for Healthy and Cancerous Tissues:

The haptic feedback is differentiated by varying the vibration intensity and frequency based on the material properties of the tissues.

ii. Equation for Vibration Frequency:

Figure 2: Physical traits of cancer. Solid stress, stiffness, fluid pressure, and microarchitecture are the four distinct physical cues which extensively drive GBM tumor progression. Among others, Extracellular matrix (ECM) stiffening directly links with the glioblastoma (GBM) stem cells invasiveness and motility.

Figure 2 presents the major physical characteristics influencing glioblastoma (GBM) tumor progression, including solid stress, tissue stiffness, interstitial fluid pressure, and tumor microarchitecture. These biomechanical factors significantly affect tumor growth, cellular migration, and treatment response within the brain environment. Among these parameters, Extracellular Matrix (ECM) stiffening plays a critical role in enhancing GBM stem cell invasiveness and motility by altering the mechanical properties surrounding the tumor tissue. Understanding these physical cues is essential for developing accurate biomechanical models and improving therapeutic strategies for aggressive brain tumors.

h. Assigning Values to Vectorized Segmented Parts

Each segmented part of the tissue is assigned material properties based on its classification (healthy or cancerous). The properties are stored in a vectorized format for efficient processing.

MATLAB code:

% Assignment of properties

properties = zeros(size(segmented_parts));

properties(segmented_parts == ‘healthy’) = healthy_properties;

properties(segmented_parts == ‘cancerous’) = cancerous_properties;

i. Triangulation with Fiducial Markers

i. Triangulation:

Fiducial markers are used to ensure positional accuracy. The triangulation process calculates the exact position of the tumor relative to the markers.

MATLAB code:

% Triangulation Algorithm

markers = [x1, y1, z1; x2, y2, z2; x3, y3, z3]; % Coordinates of fiducial markers

tumor_position = calculate_position(markers);

Meshing and Geometry of Finite Element Models

j. Meshing and Geometry Updates:

When cutting occurs, the mesh is updated to reflect the new geometry.

MATLAB code:

% Remeshing after cutting

new_mesh = update_mesh_after_cutting(mesh, cut_path);

k. Depth and Position Control When Cutting

Depth Control:

The depth of the cut is controlled by monitoring the penetration depth during the cutting process.

MATLAB code:

% Depth Control Algorithm

desired_depth = 5; % mm

current_depth = 0;

while current_depth < desired_depth

% Perform cutting operation

current_depth = measure_depth();

end

Figure 3: Morphological Operation Tumor Outline – image filling, eroding, subtracting

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Figure 3 illustrates the morphological operations used to extract and refine the tumor outline from the MRI image. The process includes image filling to close internal gaps within the tumor region, erosion to remove small unwanted pixels and noise, and subtraction operations to highlight the tumor boundaries accurately. These image processing techniques improve the precision of tumor segmentation by enhancing the shape and contour of the abnormal tissue. The refined tumor outline is essential for accurate classification, visualization, and further biomechanical or diagnostic analysis.

I. Model Remeshing for Discretization Error (C0 Error)

If an C_0 error is detected, the model can be remeshed to ensure continuity and accuracy.

MATLAB code:

% Remeshing for C0 Error

if detect_C0_error(mesh)

mesh = remesh_model(mesh);

end

m. Application Readout of Volume Sizes or Distances

The application can read out volume sizes or distances using the computed 3D geometry.

MATLAB code:

% Volume and Distance Calculation

volume = calculate_volume(segmented_tumor);

distance = calculate_distance(point1, point2);

 Drawing and Remembering the Cutting Path

The application can track and remember the cutting path for consistency.

MATLAB code:

% Tracking Cutting Path

cutting_path = [];

while cutting

current_position = get_current_position();

cutting_path = [cutting_path; current_position];

end

% Save the cutting path for later use

save_cutting_path(cutting_path);

This comprehensive explanation covers the detailed steps and processes involved in the CLEAR Biopsy Guidance System project, incorporating mathematical equations, MATLAB algorithms, FEA modeling, and advanced techniques for tissue differentiation and AR-based surgical guidance.

E. Simulation Results

Figure 4: Tumor position Identification and marking

Figure 4 illustrates the initial step in the CLEAR Biopsy Guidance System where the tumor position is identified and marked. Using MRI scans, the system accurately segments the brain tumor, highlighting its exact location within the brain. This critical step ensures that the subsequent surgical planning and intervention are based on precise and reliable data. The AR overlay shows the tumor’s boundaries, allowing surgeons to visualize the tumor’s position in relation to the surrounding brain structures.

Figure 5: Craniotomy Planning with Augmented Reality

Figure 5 demonstrates the craniotomy planning phase, where the CLEAR system uses augmented reality to assist surgeons in planning the surgical entry point. By overlaying the 3D reconstructed tumor model and critical anatomical landmarks onto the patient’s head, the system provides a comprehensive view of the surgical site. This AR-assisted planning enhances the surgeon’s ability to determine the most optimal and safest location for the craniotomy, minimizing the risk of damage to healthy brain tissues.

Figure 6: Successfully identified the exact location tumor in the brain

Figure 6 showcases the successful identification of the exact tumor location within the brain. The AR system overlays the tumor model onto the patient’s anatomy, confirming the tumor’s position with high accuracy. This visualization aids the surgeon in correlating the digital information with the physical anatomy, ensuring that the subsequent surgical steps are accurately targeted. The precision of this step is crucial for effective tumor removal and minimizing complications.

Figure 7: Drilling and hole making process for operation

Figure 7 depicts the drilling and hole-making process facilitated by the CLEAR system. The AR overlay guides the surgeon in real-time, showing the planned trajectory and depth for the drilling process. This guidance ensures that the holes are made precisely where needed, following the preoperative plan. The system’s haptic feedback provides additional tactile information, differentiating between healthy and cancerous tissues, which further enhances the accuracy of the drilling process.

Figure 8: C# and unity based User Interface for Tumor Surgery

Figure 8 presents the user interface developed in C# and unity for the CLEAR system. The interface is designed to be intuitive and user-friendly, providing surgeons with easy access to patient data, imaging, and surgical plans. It allows for seamless interaction with the AR system, enabling the surgeon to control and adjust the overlays as needed during the procedure. The interface is a critical component that ensures the smooth operation and usability of the CLEAR system in a clinical setting.

Figure 9: After removing the tumor tissues and performing sewing of brain skull

Figure 9 shows the final stage of the procedure where the tumor tissues have been successfully removed, and the brain skull is being sutured. The AR system continues to provide guidance, ensuring that all tumor tissues are adequately excised and the surgical site is properly closed. This figure highlights the effectiveness of the CLEAR system in not only guiding the tumor removal but also assisting in the accurate and safe closure of the surgical site, thus completing the procedure successfully.

F. Validation of Results

The validation of the CLEAR Biopsy Guidance System was conducted using an extensive dataset of 496 MRI scans obtained from the Bab El Oued university hospital center. The primary goal was to assess the system’s accuracy in segmenting brain tumors, reconstructing 3D models, and providing precise augmented reality (AR) guidance for neurosurgical procedures. This validation process involved a series of rigorous tests and evaluations to ensure that the system met the high standards required for clinical application.

i. Segmentation Accuracy

The segmentation of brain tumors from MRI scans is a critical component of the CLEAR system. To validate the segmentation accuracy, we applied our methodology, which includes preprocessing with a Gaussian filter to mitigate intensity heterogeneity and segmentation using active geometric contour models complemented by morphological operations. The segmentation results were compared against manually segmented ground truth data provided by experienced radiologists. The system achieved an impressive segmentation accuracy of 98.61%, significantly outperforming existing state-of-the-art methods. This high level of accuracy ensures that the AR overlays are based on precise and reliable tumor boundaries, which is essential for effective surgical planning and guidance.

ii. 3D Reconstruction and AR Visualization

The next step in the validation process was to evaluate the accuracy of the 3D reconstruction and AR visualization. Using 3D Slicer, we converted the segmented 2D MRI data into detailed 3D geometries, which were then refined using Autodesk Maya. The accuracy of these 3D models was validated by comparing them with the original MRI scans and ensuring that the reconstructed models accurately represented the tumor’s shape and location. Additionally, the system’s ability to perform real-time brain tumor augmentation was tested by overlaying the 3D models onto the patient’s head using the Microsoft HoloLens 2. The system achieved a reprojection accuracy of 97%, indicating that the AR visualizations were precise and aligned correctly with the patient’s anatomy. This high level of reprojection accuracy is crucial for providing surgeons with reliable and accurate visual guidance during biopsy procedures.

iii. Haptic Feedback and Material Properties

The CLEAR system also incorporates haptic feedback mechanisms that differentiate between healthy and cancerous tissues. This feature enhances the tactile feedback for surgeons during biopsy procedures, allowing them to feel the difference between various tissue types. To validate this functionality, we conducted tests using vectorized material properties for healthy and cancerous tissues, ensuring that the haptic feedback accurately represented the physical properties of the tissues. Surgeons who participated in the validation process reported that the haptic feedback provided a realistic and useful distinction between tissue types, aiding in the precision of the biopsy procedure.

iv. Overall System Performance

The overall performance of the CLEAR system was assessed through a series of simulated surgical procedures conducted by neurosurgeons. These simulations tested the system’s usability, reliability, and effectiveness in a clinical setting. Surgeons provided feedback on the system’s interface, the accuracy of the AR overlays, and the utility of the haptic feedback. The results of these simulations were overwhelmingly positive, with surgeons highlighting the system’s potential to improve surgical outcomes and enhance patient safety.

G. Conclusion

The validation process confirmed that the CLEAR Biopsy Guidance System meets the high standards required for clinical application in neurosurgery. The system’s impressive segmentation accuracy, precise 3D reconstruction, and reliable AR visualization, combined with effective haptic feedback, demonstrate its potential to significantly enhance brain biopsy procedures. The successful integration of multiple technologies and expertise underscores the multidisciplinary nature of the project and its strong potential for improving surgical outcomes and efficiency. Future work will focus on further refining the system’s spatial tracking and real-time rendering capabilities to achieve clinical-grade performance and broader adoption in the medical field.

The CLEAR Biopsy Guidance System represents a significant advancement in AR applications for neurosurgery. By combining detailed imaging, precise navigation, and real-time guidance, the system enhances surgical outcomes and improves patient safety. The high accuracy and efficiency of the system, validated by extensive testing, affirm its potential for clinical adoption. Future work will focus on refining spatial tracking, enhancing real-time rendering capabilities, and addressing computational challenges to achieve clinical-grade performance.

H. Technical Challenges

1. The AR system’s response time must be minimized to provide real-time guidance and feedback to the surgeon. High latency can lead to misalignment and delays in the surgical process, potentially affecting the accuracy and safety of the procedure.
2. Ensuring high accuracy in the overlay of 3D models onto the patient’s anatomy is crucial. Misalignments can lead to incorrect surgical guidance, impacting the precision of tumor localization and removal.
3. Real-time volume rendering of MRI data is computationally intensive. The system’s ability to handle this without compromising performance on the HoloLens 2 is limited, necessitating efficient processing techniques.
4. The current state of spatial hand and object tracking technology is a key challenge. Precise mapping of digital objects and physical instruments is essential, but existing tracking methods may not always provide the required level of precision.
5. Incorporating haptic feedback to differentiate between healthy and cancerous tissues adds complexity. Achieving realistic and responsive haptic sensations that accurately represent tissue properties is technically challenging.
6. Developing an intuitive and user-friendly interface that allows surgeons to easily interact with the AR system during surgery is critical. Any complexity or usability issues can hinder the surgical workflow.
7. Handling large volumes of MRI data and ensuring fast processing for 3D reconstruction and AR visualization requires robust data management and processing capabilities.
8. The HoloLens 2, while advanced, has limitations in terms of computational power and battery life, which can affect the overall performance and duration of surgical procedures.
9. Variability in operating room conditions, such as lighting and movement, can impact the performance of the AR system. Ensuring consistent performance across different environments is a challenge.
10. Continuous validation and calibration of the AR system are required to maintain accuracy over time. This includes calibrating the AR overlays with the patient’s anatomy and ensuring consistent performance across different devices.

I. References

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