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.

提出了一种基于深度学习的新框架，利用超声引导下的磁共振成像和超声图像注册技术，自动评估 MRI-iUS 注册的质量，并首次展示其在 iUS 引导下的脑肿瘤切除手术中的实际临床数据上的表现。

基于 Swin UNETR 的脑肿瘤手术 MRI - 超声配准的密集误差图估计

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

In brain tumor resection, accurate removal of cancerous tissues while
preserving eloquent regions is crucial to the safety and outcomes of the
treatment. However, intra-operative tissue deformation (called brain shift) can
move the surgical target and render the pre-surgical plan invalid.
Intra-operative ultrasound (iUS) has been adopted to provide real-time images
to track brain shift, and inter-modal (i.e., MRI-iUS) registration is often
required to update the pre-surgical plan. Quality control for the registration
results during surgery is important to avoid adverse outcomes, but manual
verification faces great challenges due to difficult 3D visualization and the
low contrast of iUS. Automatic algorithms are urgently needed to address this
issue, but the problem was rarely attempted. Therefore, we propose a novel deep
learning technique based on 3D focal modulation in conjunction with uncertainty
estimation to accurately assess MRI-iUS registration errors for brain tumor
surgery. Developed and validated with the public RESECT clinical database, the
resulting algorithm can achieve an estimation error of 0.59+-0.57 mm.

通过 3D 聚焦调制技术和不确定性估计，我们提出了一种基于深度学习的新技术，用于准确评估脑肿瘤手术中的 MRI-iUS 配准误差，通过 RESECT 临床数据库的开发和验证，该算法可以达到 0.59+-0.57 毫米的估计误差。