[论文解读] A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance

Mareike Thies, Jan-Nico Zäch+5 more|arXiv (Cornell University)|Jan 1, 2020

Spinal Fractures and Fixation Techniques参考文献 33被引用 30

一句话总结

本文提出一种基于深度学习的方法，用于在脊柱手术中实时、在线调整C臂锥束CT（CBCT）源轨迹，以减少螺钉引起的金属伪影。通过使用卷积神经网络从实时 fluoroscopic 投影图像回归视图特定的质量指数，系统能够动态选择最优的下一视图，避免数据不一致，与标准圆形轨迹相比，显著提升了重建质量并减少了伪影。

ABSTRACT

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. 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 for 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. 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 and real CBCT acquisitions of a semi-anthropomorphic phantom, we show that tomographic reconstructions of the resulting scene-specific CBCT acquisitions exhibit improved image quality particularly in terms of metal artifacts. Since the optimization objective is implicitly encoded in a neural network, the proposed approach overcomes the need for 3D information at run-time.

研究动机与目标

• 解决脊柱融合手术中术中CBCT因金属伪影导致螺钉置入验证受阻的关键临床挑战。
• 通过主动选择与理想图像形成模型最一致的X射线视图，最小化数据不一致性，从而提升重建质量。
• 通过仅从2D投影图像学习任务特定的轨迹调整，消除扫描时对3D体素信息的需求。
• 利用在真实模拟数据上训练的深度神经网络，实现实时、患者特异性的轨迹优化。
• 通过模拟和真实体模数据，证明该方法在可行性及相对于标准圆形CBCT扫描的改进效果。

提出的方法

• 使用卷积神经网络（VGG-19）从单张实时荧光透视投影图像预测视图相关的质量指数。
• 网络基于当前投影输出下一组最优C臂源角度（平面内和面外）的建议，实现在线、增量式的轨迹更新。
• 该方法采用受强化学习启发的策略，选择与理想重建模型不一致最小的视图，尤其避免束硬化和光子饥饿效应。
• 训练数据来自包含钛合金螺钉的X射线投影真实蒙特卡洛模拟，捕捉了真实的散射、束硬化和噪声。
• 在半人体体模的模拟和真实CBCT数据上进行回顾性评估，以评估重建质量。
• 轨迹优化旨在避免沿螺钉长轴方向的视图以及螺钉投影重叠的视图，这些视图已知会导致严重伪影。

实验结果

研究问题

• RQ1 深度学习模型能否仅基于CBCT扫描期间单张当前投影图像，准确预测未来X射线视图的质量？
• RQ2 使用神经网络预测结果在线实时调整C臂轨迹，能否相比标准圆形轨迹显著减少CBCT重建中的金属伪影？
• RQ3 在模拟数据上训练的模型在真实世界CBCT采集（体模）中的泛化程度如何？
• RQ4 与传统圆形轨道相比，所生成的非圆形轨迹在伪影减少和解剖细节保留方面表现如何？
• RQ5 该方法能否在不依赖扫描时3D信息的前提下，提升螺钉皮质破裂的可检测性？

主要发现

• 所提方法通过动态调整C臂轨迹以避开导致严重数据不一致的视图，成功减少了CBCT重建中的金属伪影。
• 卷积神经网络在不同噪声水平和初始角度下均实现了鲁棒且准确的视图质量预测，即使从模拟数据泛化到真实数据也表现良好。
• 使用真实模拟数据训练使网络能够泛化至真实体模采集，重建的螺钉形状和螺纹细节相比标准圆形扫描有显著改善。
• 网络预测的轨迹避开了高伪影视图——尤其是沿螺钉长轴方向和螺钉投影重叠的视图——从而实现了更一致、更准确的重建。
• 该方法表明，仅使用2D投影数据即可实现任务感知、场景特定的轨迹规划，显著提升图像质量，且无需在扫描时获取体素数据。
• 回顾性评估证实，即使从标准圆形平面（平行于螺钉轴线）略微倾斜，也能实现显著的伪影减少，表明该方法具有立即的临床可行性。