Fan Fuxin, Kreher Björn, Keil Holger, Maier Andreas, Huang Yixing
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Siemens Healthcare GmbH, Forchheim, Germany.
Med Phys. 2022 May;49(5):2914-2930. doi: 10.1002/mp.15617. Epub 2022 Mar 31.
Fiducial markers are commonly used in navigation-assisted minimally invasive spine surgery and they help transfer image coordinates into real-world coordinates. In practice, these markers might be located outside the field-of-view (FOV) of C-arm cone-beam computed tomography (CBCT) systems used in intraoperative surgeries, due to the limited detector sizes. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation.
In this work, we propose two fiducial marker detection methods: direct detection from distorted markers (direct method) and detection after marker recovery (recovery method). For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed. For marker recovery, a task-specific data preparation strategy is proposed to recover markers from severely truncated data. Afterwards, a conventional marker detection algorithm is applied for position detection. The networks in both methods are trained based on simulated data. For the direct method, 6800 images and 10 000 images are generated, respectively, to train the U-Net and ResNet50. For the recovery method, the training set includes 1360 images for FBPConvNet and Pix2pixGAN. The simulated data set with 166 markers and four cadaver cases with real fiducials are used for evaluation.
The two methods are evaluated on simulated data and real cadaver data. The direct method achieves 100% detection rates within 1 mm detection error on simulated data with normal truncation and simulated data with heavier noise, but only detect 94.6% markers in extremely severe truncation case. The recovery method detects all the markers successfully in three test data sets and around 95% markers are detected within 0.5 mm error. For real cadaver data, both methods achieve 100% marker detection rates with mean registration error below 0.2 mm.
Our experiments demonstrate that the direct method is capable of detecting distorted markers accurately and the recovery method with the task-specific data preparation strategy has high robustness and generalizability on various data sets. The task-specific data preparation is able to reconstruct structures of interest outside the FOV from severely truncated data better than conventional data preparation.
基准标记常用于导航辅助的微创脊柱手术，它们有助于将图像坐标转换为现实世界坐标。在实际操作中，由于探测器尺寸有限，这些标记可能位于术中使用的C形臂锥束计算机断层扫描（CBCT）系统的视野（FOV）之外。因此，CBCT体积中的重建标记会出现伪影且形状扭曲，这给导航带来了障碍。
在这项工作中，我们提出了两种基准标记检测方法：从扭曲标记直接检测（直接法）和标记恢复后检测（恢复法）。对于从重建体积中的扭曲标记直接检测，提出了一种使用两个神经网络和传统圆检测算法的高效自动标记检测方法。对于标记恢复，提出了一种特定任务的数据准备策略，以从严重截断的数据中恢复标记。然后，应用传统的标记检测算法进行位置检测。两种方法中的网络均基于模拟数据进行训练。对于直接法，分别生成6800张图像和10000张图像来训练U-Net和ResNet50。对于恢复法，训练集包括用于FBPConvNet和Pix2pixGAN的1360张图像。使用具有166个标记的模拟数据集和四个带有真实基准的尸体病例进行评估。
在模拟数据和真实尸体数据上对这两种方法进行了评估。直接法在正常截断的模拟数据和噪声较大较重的模拟数据上，在检测误差1毫米内实现了100%的检测率，但在极端严重截断情况下仅检测到94.6%的标记。恢复法在三个测试数据集中成功检测到了所有标记，并且约95%的标记在0.5毫米误差内被检测到。对于真实尸体数据，两种方法均实现了100%的标记检测率，平均配准误差低于0.2毫米。
我们的实验表明，直接法能够准确检测扭曲的标记，而具有特定任务数据准备策略的恢复法在各种数据集上具有很高的鲁棒性和通用性。特定任务的数据准备能够比传统数据准备更好地从严重截断的数据中重建视野外的感兴趣结构。
