Peressutti Devis, Gomez Alberto, Penney Graeme P, King Andrew P
Division of Imaging Sciences and Biomedical Engineering, King's College London, London, U.K.
Division of Imaging Sciences and Biomedical Engineering, King's College London.
IEEE Trans Biomed Eng. 2017 Feb;64(2):352-361. doi: 10.1109/TBME.2016.2550487.
3-D +t echocardiography (3DtE) is widely employed for the assessment of left ventricular anatomy and function. However, the information derived from 3DtE images can be affected by the poor image quality and the limited field of view. Registration of multiview 3DtE sequences has been proposed to compound images from different acoustic windows, therefore improving both image quality and coverage. We propose a novel subspace error metric for an automatic and robust registration of multiview intrasubject 3DtE sequences.
The proposed metric employs linear dimensionality reduction to exploit the similarity in the temporal variation of multiview 3DtE sequences. The use of a low-dimensional subspace for the computation of the error metric reduces the influence of image artefacts and noise on the registration optimization, resulting in fast and robust registrations that do not require a starting estimate.
The accuracy, robustness, and execution time of the proposed registration were thoroughly validated. Results on 48 pairwise multiview 3DtE registrations show the proposed error metric to outperform a state-of-the-art phase-based error metric, with improvements in median/75th percentile of the target registration error of 21%/31% and an improvement in mean execution time of 45%.
The proposed subspace error metric outperforms sum-of-squared differences and phase-based error metrics for the registration of multiview 3DtE sequences in terms of accuracy, robustness, and execution time.
The use of the proposed subspace error metric has the potential to replace standard image error metrics for a robust and automatic registration of multiview 3DtE sequences.
三维超声心动图（3DtE）被广泛用于评估左心室的解剖结构和功能。然而，从3DtE图像中获取的信息可能会受到图像质量差和视野有限的影响。多视角3DtE序列的配准被提出来将来自不同声学窗口的图像进行合成，从而提高图像质量和覆盖范围。我们提出了一种新颖的子空间误差度量方法，用于多视角受试者内3DtE序列的自动且稳健的配准。
所提出的度量方法采用线性降维来利用多视角3DtE序列时间变化中的相似性。使用低维子空间来计算误差度量可减少图像伪影和噪声对配准优化的影响，从而实现快速且稳健的配准，且无需初始估计。
对所提出配准方法的准确性、稳健性和执行时间进行了全面验证。48对多视角3DtE配准的结果表明，所提出的误差度量方法优于一种基于相位的先进误差度量方法，目标配准误差的中位数/第75百分位数提高了21%/31%，平均执行时间缩短了45%。
在所提出的子空间误差度量方法在准确性、稳健性和执行时间方面优于用于多视角3DtE序列配准的平方和误差度量方法和基于相位的误差度量方法。
使用所提出的子空间误差度量方法有可能取代标准图像误差度量方法，用于多视角3DtE序列的稳健且自动的配准。
