Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China.
Phys Med Biol. 2019 Jan 18;64(2):025018. doi: 10.1088/1361-6560/aafa06.
In the diagnosis and interventional treatment of coronary artery disease, the 3D[Formula: see text]time reconstruction of the coronary artery on the basis of x-ray angiographic image sequences can provide dynamic structural information. The synchronization of cardiac phases in the sequences is essential for minimizing the influence of cardiorespiratory motion and realizing precise 3D[Formula: see text]time reconstruction. Key points are initially extracted from the first image of a sequence. Matching grid points between consecutive images in the sequence are extracted by a multi-layer matching strategy. Then deep motion tracking (DMT) of key points is achieved by local deformation based on the neighboring grid points of key points. The local deformation is optimized by the Random sample consensus (RANSAC) algorithm. Then, a simple harmonic motion (SHM) model is utilized to distinguish cardiac motion from other motion sources (e.g. respiratory, patient movement, etc). Next, the signal which is composed of cardiac motions is filtered by a band-pass filter to reconstruct the cardiac phases. Finally, the synchronization of cardiac phases from different imaging angles is realized by a piece-wise linear transformation. The proposed method was evaluated using clinical x-ray angiographic image sequences from 13 patients. [Formula: see text] matching points can be accurately computed by the DMT method. The mean peak temporal distance (MPTD) between the reconstructed cardiac phases and the electrocardiograph signal is [Formula: see text] s. The correlation between the cardiac phases of the same patient is over [Formula: see text]. Compared with three other state-of-the-art methods, the proposed method accurately reconstructs and synchronizes the cardiac phases from different sequences of the same patient. The proposed DMT method is robust and highly effective in synchronizing cardiac phases of angiographic image sequences captured from different imaging angles.
在冠状动脉疾病的诊断和介入治疗中，基于 X 射线血管造影图像序列的冠状动脉 3D[Formula: see text]时间重建可以提供动态结构信息。序列中心脏相位的同步对于最小化心肺运动的影响和实现精确的 3D[Formula: see text]时间重建至关重要。关键点最初从序列的第一幅图像中提取。通过多层匹配策略从序列中的连续图像中提取匹配网格点。然后，通过基于关键点的相邻网格点的局部变形实现关键点的深度运动跟踪（DMT）。局部变形通过随机样本一致性（RANSAC）算法进行优化。然后，利用简谐运动（SHM）模型将心脏运动与其他运动源（如呼吸、患者运动等）区分开来。接下来，通过带通滤波器对包含心脏运动的信号进行滤波，以重建心脏相位。最后，通过分段线性变换实现来自不同成像角度的心脏相位的同步。该方法使用来自 13 名患者的临床 X 射线血管造影图像序列进行了评估。DMT 方法可以准确计算[Formula: see text]个匹配点。重建的心脏相位和心电图信号之间的平均峰值时间距离（MPTD）为[Formula: see text]s。同一患者的心脏相位之间的相关性超过[Formula: see text]。与其他三种最先进的方法相比，该方法能够准确地重建和同步来自同一患者不同序列的心脏相位。所提出的 DMT 方法在从不同成像角度捕获的血管造影图像序列中同步心脏相位方面具有鲁棒性和高效性。
