Augmented Reality (AR) has emerged as a significant advancement in surgical
procedures, offering a solution to the challenges posed by traditional
neuronavigation methods. These conventional techniques often necessitate
surgeons to split their focus between the surgical site and a separate monitor
that displays guiding images. Over the years, many systems have been developed
to register and track the hologram at the targeted locations, each employed its
own evaluation technique. On the other hand, hologram displacement measurement
is not a straightforward task because of various factors such as occlusion,
Vengence-Accomodation Conflict, and unstable holograms in space. In this study,
we explore and classify different techniques for assessing an AR-assisted
neurosurgery system and propose a new technique to systematize the assessment
procedure. Moreover, we conduct a deeper investigation to assess surgeon error
in the pre- and intra-operative phases of the surgery based on the respective
feedback given. We found that although the system can undergo registration and
tracking errors, physical feedback can significantly reduce the error caused by
hologram displacement. However, the lack of visual feedback on the hologram
does not have a significant effect on the user 3D perception.

评估 AR 辅助神经外科系统的不同技术并提出了一种新的技术来系统化评估过程。此外，在手术的准备和术中阶段基于相应的反馈进行更深入的调查，发现物理反馈可以显著减少由全息图位移引起的错误，而全息图上的缺乏视觉反馈对用户的三维感知不会产生显著影响。

AR 辅助神经外科系统的端到端评估

End-to-End assessment of AR-assisted neurosurgery systems

We present a novel method for intraoperative patient-to-image registration by
learning Expected Appearances. Our method uses preoperative imaging to
synthesize patient-specific expected views through a surgical microscope for a
predicted range of transformations. Our method estimates the camera pose by
minimizing the dissimilarity between the intraoperative 2D view through the
optical microscope and the synthesized expected texture. In contrast to
conventional methods, our approach transfers the processing tasks to the
preoperative stage, reducing thereby the impact of low-resolution, distorted,
and noisy intraoperative images, that often degrade the registration accuracy.
We applied our method in the context of neuronavigation during brain surgery.
We evaluated our approach on synthetic data and on retrospective data from 6
clinical cases. Our method outperformed state-of-the-art methods and achieved
accuracies that met current clinical standards.

我们提出了一种新颖的方法，通过学习预期的外观来进行术中患者到图像的注册。我们的方法利用术前成像合成手术显微镜下的特定于患者的预期视图，针对一系列预测的变换。我们的方法通过最小化光学显微镜下术中 2D 视图与合成预期纹理之间的差异来估计相机姿态。与传统方法相比，我们的方法将处理任务转移到术前阶段，从而减少低分辨率、扭曲和噪声术中图像的影响，这些因素通常会降低注册精度。我们在脑外科手术中的神经导航背景下应用了该方法。我们在合成数据和 6 个临床病例的回顾性数据上评估了我们的方法。我们的方法优于现有方法，并达到了当前临床标准的准确性。