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心电图门控介入心脏血管重建中的残余运动补偿。

Residual motion compensation in ECG-gated interventional cardiac vasculature reconstruction.

机构信息

Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, D-91058 Erlangen, Germany.

出版信息

Phys Med Biol. 2013 Jun 7;58(11):3717-37. doi: 10.1088/0031-9155/58/11/3717. Epub 2013 May 8.

Abstract

Three-dimensional reconstruction of cardiac vasculature from angiographic C-arm CT (rotational angiography) data is a major challenge. Motion artefacts corrupt image quality, reducing usability for diagnosis and guidance. Many state-of-the-art approaches depend on retrospective ECG-gating of projection data for image reconstruction. A trade-off has to be made regarding the size of the ECG-gating window. A large temporal window is desirable to avoid undersampling. However, residual motion will occur in a large window, causing motion artefacts. We present an algorithm to correct for residual motion. Our approach is based on a deformable 2D-2D registration between the forward projection of an initial, ECG-gated reconstruction, and the original projection data. The approach is fully automatic and does not require any complex segmentation of vasculature, or landmarks. The estimated motion is compensated for during the backprojection step of a subsequent reconstruction. We evaluated the method using the publicly available CAVAREV platform and on six human clinical datasets. We found a better visibility of structure, reduced motion artefacts, and increased sharpness of the vessels in the compensated reconstructions compared to the initial reconstructions. At the time of writing, our algorithm outperforms the leading result of the CAVAREV ranking list. For the clinical datasets, we found an average reduction of motion artefacts by 13 ± 6%. Vessel sharpness was improved by 25 ± 12% on average.

摘要

从血管造影 C 臂 CT(旋转血管造影)数据中重建心脏血管的三维结构是一个主要挑战。运动伪影会降低图像质量,降低其在诊断和指导中的可用性。许多最先进的方法都依赖于投影数据的回顾性 ECG 门控进行图像重建。必须在 ECG 门控窗口的大小方面做出权衡。大的时间窗口是避免欠采样所必需的。然而,在大窗口中仍会发生残余运动,导致运动伪影。我们提出了一种校正残余运动的算法。我们的方法基于初始 ECG 门控重建的正向投影与原始投影数据之间的可变形 2D-2D 配准。该方法完全自动,不需要对血管或标记进行任何复杂的分割。在后续重建的反向投影步骤中,补偿估计的运动。我们使用公开的 CAVAREV 平台和六个人体临床数据集评估了该方法。与初始重建相比,补偿重建中的结构可见度更好,运动伪影更少,血管更清晰。在撰写本文时,我们的算法优于 CAVAREV 排名列表中的领先结果。对于临床数据集,我们发现运动伪影的平均减少了 13±6%。平均而言,血管清晰度提高了 25±12%。

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