Pattern Recognition Lab, Computer Science Department, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany.
Graduate School in Advanced Optical Technologies (SAOT), Erlangen, Germany.
Med Phys. 2017 Sep;44(9):e113-e124. doi: 10.1002/mp.12021.
Rotational coronary angiography enables 3D reconstruction but suffers from intra-scan cardiac and respiratory motion. While gating handles cardiac motion, respiratory motion requires compensation. State-of-the-art algorithms rely on 3D-2D registration that depends on initial reconstructions of sufficient quality. We propose a compensation method that is applied directly in projection domain. It overcomes the need for reconstruction and thus complements the state-of-the-art.
Virtual single-frame background subtraction based on vessel segmentation and spectral deconvolution yields non-truncated images of the contrasted lumen. This allows motion compensation based on data consistency conditions. We compensate craniocaudal shifts by optimizing epipolar consistency to (a) devise an image-based surrogate for cardiac motion and (b) compensate for respiratory motion. We validate our approach in two numerical phantom studies and three clinical cases.
Correlation of the image-based surrogate for cardiac motion with the ECG-based ground truth was excellent yielding a Pearson correlation of 0.93 ± 0.04. Considering motion compensation, the target error measure decreased by 98% and 69%, respectively, for the phantom experiments while for the clinical cases the same figure of merit improved by 46 ± 21%.
The proposed method is entirely image-based and accurately estimates craniocaudal shifts due to respiration and cardiac contraction. Future work will investigate experimental trajectories and possibilities for simplification of the single-frame subtraction pipeline.
旋转冠状动脉造影能够进行 3D 重建，但会受到扫描内心脏和呼吸运动的影响。门控处理心脏运动，而呼吸运动需要补偿。最先进的算法依赖于 3D-2D 配准，这依赖于足够质量的初始重建。我们提出了一种直接在投影域中应用的补偿方法。它克服了重建的需要，因此补充了最先进的方法。
基于血管分割和光谱反卷积的虚拟单帧背景减除可获得对比度内腔的非截断图像。这允许基于数据一致性条件进行运动补偿。我们通过优化极线一致性来补偿头尾方向的移动，以 (a) 设计用于心脏运动的基于图像的替代物，以及 (b) 补偿呼吸运动。我们在两个数值体模研究和三个临床病例中验证了我们的方法。
心脏运动的基于图像的替代物与基于心电图的真实值之间的相关性非常好，Pearson 相关系数为 0.93±0.04。考虑到运动补偿，体模实验中目标误差度量分别降低了 98%和 69%，而对于临床病例，相同的质量度量提高了 46±21%。
所提出的方法完全基于图像，可以准确估计由于呼吸和心脏收缩引起的头尾方向的移动。未来的工作将研究实验轨迹和简化单帧减除管道的可能性。
