Dense Error Map Estimation for MRI-Ultrasound Registration in Brain Tumor Surgery Using Swin UNETR

TL;DR

This paper introduces a deep learning framework using Swin UNETR to automatically generate dense error maps for MRI-ultrasound registration in brain tumor surgery, improving real-time assessment of registration quality.

Contribution

It presents the first application of Swin UNETR for dense error map estimation in MRI-ultrasound registration during brain tumor surgeries.

Findings

Effective automatic assessment of registration quality in clinical data

Improved accuracy over manual evaluation methods

Real-time dense error map generation demonstrated

Abstract

Early surgical treatment of brain tumors is crucial in reducing patient mortality rates. However, brain tissue deformation (called brain shift) occurs during the surgery, rendering pre-operative images invalid. As a cost-effective and portable tool, intra-operative ultrasound (iUS) can track brain shift, and accurate MRI-iUS registration techniques can update pre-surgical plans and facilitate the interpretation of iUS. This can boost surgical safety and outcomes by maximizing tumor removal while avoiding eloquent regions. However, manual assessment of MRI-iUS registration results in real-time is difficult and prone to errors due to the 3D nature of the data. Automatic algorithms that can quantify the quality of inter-modal medical image registration outcomes can be highly beneficial. Therefore, we propose a novel deep-learning (DL) based framework with the Swin UNETR to automatically assess 3D-patch-wise dense error maps for MRI-iUS registration in iUS-guided brain tumor resection and show its performance with real clinical data for the first time.