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arXiv · 2308.10784

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

Soorena Salari·Amirhossein Rasoulian·Hassan Rivaz·Yiming Xiao
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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.

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Soorena Salari, Amirhossein Rasoulian, Hassan Rivaz, Yiming Xiao. 2023-08-21. Dense Error Map Estimation for MRI-Ultrasound Registration in Brain Tumor Surgery Using Swin UNETR. https://arxiv.org/abs/2308.10784

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