Deep motion tracking from multiview angiographic image sequences for synchronization of cardiac phases.

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.