Advancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery

TL;DR

This paper presents a dynamic data-driven non-rigid registration approach that improves intra-operative brain image alignment during neurosurgery by enhancing accuracy and speed through distributed computing and machine learning, addressing clinical time constraints.

Contribution

It introduces a novel NRR method optimized for real-time neurosurgical applications, integrating distributed computing and machine learning for better accuracy and efficiency.

Findings

NRR results delivered within clinical time constraints

Distributed computing and machine learning improve registration accuracy

Challenges in intra-operative application are identified

Abstract

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.