  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.
