2 research outputs found
Advancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery
During neurosurgery, medical images of the brain are used to locate tumors
and critical structures, but brain tissue shifts make pre-operative images
unreliable for accurate removal of tumors. Intra-operative imaging can track
these deformations but is not a substitute for pre-operative data. To address
this, we use Dynamic Data-Driven Non-Rigid Registration (NRR), a complex and
time-consuming image processing operation that adjusts the pre-operative image
data to account for intra-operative brain shift. Our review explores a specific
NRR method for registering brain MRI during image-guided neurosurgery and
examines various strategies for improving the accuracy and speed of the NRR
method. We demonstrate that our implementation enables NRR results to be
delivered within clinical time constraints while leveraging Distributed
Computing and Machine Learning to enhance registration accuracy by identifying
optimal parameters for the NRR method. Additionally, we highlight challenges
associated with its use in the operating room
Comparison of Physics-Based Deformable Registration Methods for Image-Guided Neurosurgery
This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete data. It concludes with preliminary results on leveraging Quantum Computing, a promising new technology for computationally intensive problems like Feature Detection and Block Matching in addition to finite element solver; all three account for 75% of computing time in deformable registration