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Toward a real time multi-tissue Adaptive Physics-Based Non-Rigid Registration framework for brain tumor resection
Fotis Drakopoulos1, Panagiotis Foteinos2, Yixun Liu3
1CRTC Lab and Computer Science, Old Dominion University Norfolk, VA, USA.
Frontiers in Neuroinformatics
|March 6, 2014
Summary
This study introduces an adaptive non-rigid registration method to accurately align pre-operative and intra-operative MRI during brain tumor surgery. The novel approach significantly reduces alignment errors and execution time, improving surgical precision.
Area of Science:
- Medical Imaging
- Computational Anatomy
- Neurosurgery
Background:
- Brain shift and tissue deformation during tumor resection complicate surgical navigation.
- Existing Physics-Based Non-Rigid Registration (PBNRR) methods in ITK are not optimized for intra-operative tumor removal.
- Accurate pre-operative to intra-operative MRI alignment is crucial for minimizing surgical errors.
Purpose of the Study:
- To develop and evaluate an adaptive non-rigid registration method for compensating brain deformation during tumor resection.
- To improve the accuracy and efficiency of aligning pre-operative MRI with intra-operative MRI (iMRI).
- To address the limitations of existing registration techniques in handling tissue/tumor removal.
Main Methods:
- An adaptive, heterogeneous, biomechanical Finite Element (FE) model was developed for simulating tissue/tumor removal in iMRI.
- The method extends existing PBNRR techniques, incorporating parallel processing for efficiency.
- Evaluated on 14 clinical cases, including brain shift, partial, and complete tumor resections.
Main Results:
- The adaptive method significantly reduced alignment errors compared to rigid and ITK's PBNRR methods (up to 7x and 5x reduction, respectively).
- Average alignment error reduction was 9.23 mm versus rigid and 5.63 mm versus ITK's PBNRR.
- End-to-end execution time was under 1 minute on a multi-core workstation.
Conclusions:
- The proposed adaptive non-rigid registration method accurately captures deformations from tumor resection.
- This technique offers substantial improvements in alignment accuracy and speed for neurosurgical procedures.
- The method provides a clinically viable solution for real-time image-guided brain tumor surgery.
