Dense Error Map Estimation for MRI-Ultrasound Registration in Brain
  Tumor Surgery Using Swin UNETR
Soorena Salari Amirhossein Rasoulian Hassan Rivaz Yiming Xiao
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.