Robotics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, New South Wales, Australia.
School of Information Science and Engineering, Harbin Institute of Technology, Weihai, China.
Med Phys. 2023 Jan;50(1):61-73. doi: 10.1002/mp.15910. Epub 2022 Aug 17.
While three-dimensional transesophageal echocardiography (3D TEE) has been increasingly used for assessing cardiac anatomy and function, it still suffers from a limited field of view (FoV) of the ultrasound transducer. Therefore, it is difficult to examine a complete region of interest without moving the transducer. Existing methods extend the FoV of 3D TEE images by mosaicing multiview static images, which requires synchronization between 3D TEE images and electrocardiogram (ECG) signal to avoid deformations in the images and can only get the widened image at a specific phase.
This work aims to develop a novel multiview nonrigid registration and fusion method to extend the FoV of 3D TEE images at different cardiac phases, avoiding the bias toward the specifically chosen phase.
A multiview nonrigid registration and fusion method is proposed to enlarge the FoV of 3D TEE images by fusing dynamic images captured from different viewpoints sequentially. The deformation field for registering images is defined by a collection of affine transformations organized in a graph structure and is estimated by a direct (intensity-based) method. The accuracy of the proposed method is evaluated by comparing it with two B-spline-based methods, two Demons-based methods, and one learning-based method VoxelMorph. Twenty-nine sequences of in vivo 3D TEE images captured from four patients are used for the comparative experiments. Four performance metrics including checkerboard volumes, signed distance, mean absolute distance (MAD), and Dice similarity coefficient (DSC) are used jointly to evaluate the accuracy of the results. Additionally, paired t-tests are performed to examine the significance of the results.
The qualitative results show that the proposed method can align images more accurately and obtain the fused images with higher quality than the other five methods. Additionally, in the evaluation of the segmented left atrium (LA) walls for the pairwise registration and sequential fusion experiments, the proposed method achieves the MAD of (0.07 ± 0.03) mm for pairwise registration and (0.19 ± 0.02) mm for sequential fusion. Paired t-tests indicate that the results obtained from the proposed method are more accurate than those obtained by the state-of-the-art VoxelMorph and the diffeomorphic Demons methods at the significance level of 0.05. In the evaluation of left ventricle (LV) segmentations for the sequential fusion experiments, the proposed method achieves a DSC of (0.88 ± 0.08), which is also significantly better than diffeomorphic Demons at the 0.05 level. The FoVs of the final fused 3D TEE images obtained by our method are enlarged around two times compared with the original images.
Without selecting the static (ECG-gated) images from the same cardiac phase, this work addressed the problem of limited FoV of 3D TEE images in the deformable scenario, obtaining the fused images with high accuracy and good quality. The proposed method could provide an alternative to the conventional fusion methods that are biased toward the specifically chosen phase.
尽管三维经食管超声心动图(3D TEE)已越来越多地用于评估心脏解剖结构和功能,但它仍然受到超声换能器有限视野(FoV)的限制。因此,在不移动物体的情况下,很难检查完整的感兴趣区域。现有的方法通过拼接多视图静态图像来扩展 3D TEE 图像的 FoV,这需要 3D TEE 图像和心电图(ECG)信号之间的同步,以避免图像变形,并且只能在特定的相位获得扩展的图像。
本研究旨在开发一种新的多视图非刚性配准和融合方法,以扩展不同心脏相位的 3D TEE 图像的 FoV,避免偏向特定选择的相位。
提出了一种多视图非刚性配准和融合方法,通过顺序融合从不同视角捕获的动态图像来扩大 3D TEE 图像的 FoV。用于配准图像的变形场由一组组织成图形结构的仿射变换定义,并通过直接(基于强度)方法进行估计。通过与两种基于 B 样条的方法、两种基于 Demons 的方法和一种基于学习的方法 VoxelMorph 进行比较,评估了所提出方法的准确性。使用来自四名患者的 29 个体内 3D TEE 图像序列进行了比较实验。使用棋盘体积、签名距离、平均绝对距离(MAD)和骰子相似系数(DSC)四个性能指标联合评估结果的准确性。此外,还进行了配对 t 检验以检查结果的显著性。
定性结果表明,与其他五种方法相比,该方法可以更准确地对齐图像,并获得质量更高的融合图像。此外,在左心房(LA)壁的分割结果评估中,对于成对配准和顺序融合实验,所提出的方法在成对配准中获得 MAD 为(0.07 ± 0.03)mm,在顺序融合中获得 MAD 为(0.19 ± 0.02)mm。配对 t 检验表明,与最先进的 VoxelMorph 和非刚性 Demons 方法相比,所提出方法的结果在 0.05 水平上更准确。在顺序融合实验中,LV 分割的 DSC 为(0.88 ± 0.08),与非刚性 Demons 相比也具有显著优势,达到 0.05 水平。与原始图像相比,我们的方法获得的最终融合 3D TEE 图像的 FoV 扩大了约两倍。
在不选择相同心脏相位的静态(ECG 门控)图像的情况下,本研究解决了 3D TEE 图像在可变形情况下 FoV 有限的问题,获得了具有高精度和高质量的融合图像。所提出的方法可以为偏向特定选择的相位的传统融合方法提供替代方案。