Intra-operative adjustment of standard planes in C-arm CT image data
Intra-operative adjustment of standard planes in C-arm CT image data
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DOI：
10.1007/s11548-015-1281-3
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发表时间：
2016-03-01
影响因子：
3
通讯作者：
Nabers, Diana
中科院分区：
文献类型：
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作者：
Brehler, Michael;Goerres, Joseph;Nabers, Diana
目的借助术中移动C型臂CT，可以验证和纠正医疗干预措施，避免术后CT和二次干预。为了对感兴趣的解剖区域进行最佳评估，有必要精确调整标准平面位置，但 C 形臂的移动性导致需要耗时的手动调整。在本文中，我们以跟骨骨折为例提出了自动平面调整。方法我们开发了两种基于SURF关键点的特征检测方法（2D和伪3D），并将SURF方法转移到3D。结合基于图谱的配准，我们的算法自动调整跟骨 C 形臂图像的标准平面。使用临床数据集评估算法的稳健性。此外，我们还测试了两种配准方法、两种 C 形臂图像分辨率和两种金属伪影减少方法的算法性能。结果 对于特征提取，新颖的 3D-SURF 方法表现最佳。正如预期的那样，更高分辨率（5123 体素）也会导致更稳健的特征点，因此比 2563 体素图像（设备的标准设置）稍好。我们对两种不同的伪影减少方法和图像中金属的完全去除的比较表明，我们的方法对于伪影以及金属植入物的数量和位置具有高度鲁棒性。结论通过引入我们的快速算法处理流程，我们开发了用于评估 C 形臂 CT 图像的全自动辅助系统的第一步。
Purpose With the help of an intra-operative mobile C-arm CT, medical interventions can be verified and corrected, avoiding the need for a post-operative CT and a second intervention. An exact adjustment of standard plane positions is necessary for the best possible assessment of the anatomical regions of interest but the mobility of the C-arm causes the need for a time-consuming manual adjustment. In this article, we present an automatic plane adjustment at the example of calcaneal fractures.Methods We developed two feature detection methods (2D and pseudo-3D) based on SURF key points and also transferred the SURF approach to 3D. Combined with an atlas-based registration, our algorithm adjusts the standard planes of the calcaneal C-arm images automatically. The robustness of the algorithms is evaluated using a clinical data set. Additionally, we tested the algorithm's performance for two registration approaches, two resolutions of C-arm images and two methods for metal artifact reduction.Results For the feature extraction, the novel 3D-SURF approach performs best. As expected, a higher resolution (5123 voxel) leads also to more robust feature points and is therefore slightly better than the 2563 voxel images (standard setting of device). Our comparison of two different artifact reduction methods and the complete removal of metal in the images shows that our approach is highly robust against artifacts and the number and position of metal implants.Conclusions By introducing our fast algorithmic processing pipeline, we developed the first steps for a fully automatic assistance system for the assessment of C-arm CT images.