Real-Time Dynamic Data Driven Deformable Registration for Image-Guided Neurosurgery: Computational Aspects

TL;DR

This paper reviews the computational methods behind real-time, data-driven deformable image registration techniques used in neurosurgery to account for brain shift during surgery, highlighting their evolution and future directions.

Contribution

It provides a comprehensive overview of the computational aspects and evolution of D4NRR methods for intra-operative brain MRI registration over 15 years.

Findings

Summarizes the evolution of adaptive numerical approximation methods.

Identifies new directions for computational improvements.

Highlights the importance of real-time registration in neurosurgery.

Abstract

Current neurosurgical procedures utilize medical images of various modalities to enable the precise location of tumors and critical brain structures to plan accurate brain tumor resection. The difficulty of using preoperative images during the surgery is caused by the intra-operative deformation of the brain tissue (brain shift), which introduces discrepancies concerning the preoperative configuration. Intra-operative imaging allows tracking such deformations but cannot fully substitute for the quality of the pre-operative data. Dynamic Data Driven Deformable Non-Rigid Registration (D4NRR) is a complex and time-consuming image processing operation that allows the dynamic adjustment of the pre-operative image data to account for intra-operative brain shift during the surgery. This paper summarizes the computational aspects of a specific adaptive numerical approximation method and its variations for registering brain MRIs. It outlines its evolution over the last 15 years and identifies new directions for the computational aspects of the technique.