Purpose

During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm cone-beam CT (CBCT) provides intraoperative 3D tomographic imaging which would allow for immediate verification and, if needed, revision. However, the reconstruction quality attainable with commercial CBCT devices is insufficient, predominantly due to severe metal artifacts in the presence of pedicle screws. These artifacts arise from a mismatch between the true physics of image formation and an idealized model thereof assumed during reconstruction. Prospectively acquiring views onto anatomy that are least affected by this mismatch can, therefore, improve reconstruction quality.

Methods

We propose to adjust the C-arm CBCT source trajectory during the scan to optimize reconstruction quality with respect to a certain task, i.e., verification of screw placement. Adjustments are performed on-the-fly using a convolutional neural network that regresses a quality index over all possible next views given the current X-ray image. Adjusting the CBCT trajectory to acquire the recommended views results in non-circular source orbits that avoid poor images, and thus, data inconsistencies.

Results

We demonstrate that convolutional neural networks trained on realistically simulated data are capable of predicting quality metrics that enable scene-specific adjustments of the CBCT source trajectory. Using both realistically simulated data as well as real CBCT acquisitions of a semianthropomorphic phantom, we show that tomographic reconstructions of the resulting scene-specific CBCT acquisitions exhibit improved image quality particularly in terms of metal artifacts.

Conclusion

The proposed method is a step toward online patient-specific C-arm CBCT source trajectories that enable high-quality tomographic imaging in the operating room. Since the optimization objective is implicitly encoded in a neural network trained on large amounts of well-annotated projection images, the proposed approach overcomes the need for 3D information at run-time.

中文翻译：

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一种基于学习的方法，用于在线调整 C 形臂锥束 CT 源轨迹以避免伪影。

 目的

在脊柱融合手术期间，螺钉放置在靠近关键神经的位置，这表明需要高度精确的螺钉放置。在高质量断层扫描成像上验证螺钉的位置至关重要。 C 臂锥形束 CT (CBCT) 提供术中 3D 断层扫描成像，可以立即验证，并在需要时进行修正。然而，商业 CBCT 设备可达到的重建质量不足，主要是由于椎弓根螺钉存在严重的金属伪影。这些伪影是由于图像形成的真实物理原理与重建过程中假设的理想化模型之间的不匹配而产生的。因此，前瞻性地获取受这种不匹配影响最小的解剖结构视图可以提高重建质量。

 方法

我们建议在扫描期间调整 C 形臂 CBCT 源轨迹，以优化特定任务（即螺钉放置验证）的重建质量。使用卷积神经网络即时进行调整，该网络在给定当前 X 射线图像的所有可能的下一个视图上回归质量指数。调整 CBCT 轨迹以获得推荐的视图会产生非圆形源轨道，从而避免图像质量差，从而避免数据不一致。

 结果

我们证明，在真实模拟数据上训练的卷积神经网络能够预测质量指标，从而能够对 CBCT 源轨迹进行特定于场景的调整。使用真实模拟数据以及半拟人体模的真实 CBCT 采集，我们表明，所得到的特定场景 CBCT 采集的断层扫描重建表现出改进的图像质量，特别是在金属伪影方面。

 结论

所提出的方法是朝着在线患者特异性 C 臂 CBCT 源轨迹迈出的一步，可在手术室中实现高质量断层扫描成像。由于优化目标隐式编码在经过大量注释良好的投影图像训练的神经网络中，因此所提出的方法克服了运行时对 3D 信息的需求。