Division of Imaging Sciences and Biomedical Engineering, King's College London, UK.
Phys Med Biol. 2011 Sep 21;56(18):6109-28. doi: 10.1088/0031-9155/56/18/020. Epub 2011 Aug 26.
Echocardiography (echo) is a widely available method to obtain images of the heart; however, echo can suffer due to the presence of artefacts, high noise and a restricted field of view. One method to overcome these limitations is to use multiple images, using the 'best' parts from each image to produce a higher quality 'compounded' image. This paper describes our compounding algorithm which specifically aims to reduce the effect of echo artefacts as well as improving the signal-to-noise ratio, contrast and extending the field of view. Our method weights image information based on a local feature coherence/consistency between all the overlapping images. Validation has been carried out using phantom, volunteer and patient datasets consisting of up to ten multi-view 3D images. Multiple sets of phantom images were acquired, some directly from the phantom surface, and others by imaging through hard and soft tissue mimicking material to degrade the image quality. Our compounding method is compared to the original, uncompounded echocardiography images, and to two basic statistical compounding methods (mean and maximum). Results show that our method is able to take a set of ten images, degraded by soft and hard tissue artefacts, and produce a compounded image of equivalent quality to images acquired directly from the phantom. Our method on phantom, volunteer and patient data achieves almost the same signal-to-noise improvement as the mean method, while simultaneously almost achieving the same contrast improvement as the maximum method. We show a statistically significant improvement in image quality by using an increased number of images (ten compared to five), and visual inspection studies by three clinicians showed very strong preference for our compounded volumes in terms of overall high image quality, large field of view, high endocardial border definition and low cavity noise.
超声心动图（echo）是一种广泛应用的获取心脏图像的方法；然而，由于伪影、高噪声和有限的视野，echo 可能会受到影响。克服这些限制的一种方法是使用多个图像，从每个图像中选择“最佳”部分生成更高质量的“复合”图像。本文介绍了我们的复合算法，该算法特别旨在减少 echo 伪影的影响，同时提高信噪比值、对比度并扩展视野。我们的方法基于所有重叠图像之间的局部特征一致性/一致性来加权图像信息。验证是使用包含多达十个多视图 3D 图像的体模、志愿者和患者数据集进行的。采集了多组体模图像，一些直接从体模表面获取，另一些则通过成像穿过硬组织和软组织模拟材料来降低图像质量。我们的复合方法与原始的、未经复合的超声心动图图像以及两种基本的统计复合方法（均值和最大值）进行了比较。结果表明，我们的方法能够处理一组十个受到软组织和硬组织伪影影响的图像，并生成与直接从体模获取的图像质量相当的复合图像。我们的方法在体模、志愿者和患者数据上实现了几乎与均值方法相同的信噪比提高，同时几乎实现了与最大值方法相同的对比度提高。我们通过使用更多的图像（从五个增加到十个）显示出图像质量的显著提高，并且三位临床医生的视觉检查研究表明，我们的复合体积在整体高质量、大视野、高心内膜边界定义和低腔噪声方面具有很强的优势。
