Head and neck squamous cell carcinoma (HNSCC) has one of the highest rates of recurrence cases among solid malignancies. Recurrence rates can be reduced by improving positive margins localization. Frozen section analysis (FSA) of resected specimens is the gold standard for intraoperative margin assessment. However, because of the complex 3D anatomy and the significant shrinkage of resected specimens, accurate margin relocation from specimen back onto the resection site based on FSA results remains challenging. We propose a novel deformable registration framework that uses both the pre-resection upper surface and the post-resection site of the specimen to incorporate thickness information into the registration process. The proposed method significantly improves target registration error (TRE), demonstrating enhanced adaptability to thicker specimens. In tongue specimens, the proposed framework improved TRE by up to 33% as compared to prior deformable registration. Notably, tongue specimens exhibit complex 3D anatomies and hold the highest clinical significance compared to other head and neck specimens from the buccal and skin. We analyzed distinct deformation behaviors in different specimens, highlighting the need for tailored deformation strategies. To further aid intraoperative visualization, we also integrated this framework with an augmented reality-based auto-alignment system. The combined system can accurately and automatically overlay the deformed 3D specimen mesh with positive margin annotation onto the resection site. With a pilot study of the AR guided framework involving two surgeons, the integrated system improved the surgeons' average target relocation error from 9.8 cm to 4.8 cm.

翻译：头颈部鳞状细胞癌（HNSCC）是实体恶性肿瘤中复发率最高的类型之一。通过提高切缘阳性区域的定位精度可降低复发率。术中切缘评估的金标准是对切除标本进行冰冻切片分析（FSA）。然而，由于复杂的3D解剖结构及切除标本的显著收缩，基于FSA结果将切缘位置从标本精确映射回切除部位仍具挑战性。本文提出一种新颖的可变形配准框架，利用标本切除前的上表面与切除后的部位信息，将厚度信息纳入配准过程。该方法显著降低了目标配准误差（TRE），增强了对较厚标本的适应性。在舌部标本中，该框架相较于先前的可变形配准方法将TRE提升了33%。值得注意的是，舌部标本具有复杂的3D解剖结构，且与颊部、皮肤等其他头颈部标本相比具有更高的临床意义。我们分析了不同标本的形变行为差异，强调了定制化形变策略的必要性。为提升术中可视化效果，该框架进一步与基于增强现实的自动对准系统集成。该集成系统能准确自动地将带有阳性切缘标注的变形3D标本网格叠加至切除部位。通过两名外科医生参与的增强现实导航框架初步研究，集成系统将外科医生的平均目标重定位误差从9.8厘米降低至4.8厘米。