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.

翻译：脑肿瘤的早期手术治疗对于降低患者死亡率至关重要。然而，术中脑组织变形（即脑移位）会导致术前影像失效。作为一种成本效益高且便携的成像工具，术中超声（iUS）能够追踪脑移位，而精确的MRI-iUS配准技术可更新术前手术计划并辅助iUS图像解读。这有助于在最大化肿瘤切除的同时避开功能区，从而提升手术安全性和预后。然而，由于数据的三维特性，人工实时评估MRI-iUS配准结果较为困难且易出错。能够量化多模态医学图像配准质量的自动算法具有重要价值。为此，本文提出一种基于Swin UNETR的新型深度学习框架，首次利用真实临床数据，实现iUS引导的脑肿瘤切除术中MRI-iUS配准的三维分块稠密误差图自动评估，并验证其性能。