Alessandrini M, De Craene M, Bernard O, Giffard-Roisin S, Allain P, Waechter-Stehle I, Weese J, Saloux E, Delingette H, Sermesant M, D'hooge J
IEEE Trans Med Imaging. 2015 Jul;34(7):1436-1451. doi: 10.1109/TMI.2015.2396632. Epub 2015 Jan 27.
Quantification of cardiac deformation and strain with 3D ultrasound takes considerable research efforts. Nevertheless, a widespread use of these techniques in clinical practice is still held back due to the lack of a solid verification process to quantify and compare performance. In this context, the use of fully synthetic sequences has become an established tool for initial in silico evaluation. Nevertheless, the realism of existing simulation techniques is still too limited to represent reliable benchmarking data. Moreover, the fact that different centers typically make use of in-house developed simulation pipelines makes a fair comparison difficult. In this context, this paper introduces a novel pipeline for the generation of synthetic 3D cardiac ultrasound image sequences. State-of-the art solutions in the fields of electromechanical modeling and ultrasound simulation are combined within an original framework that exploits a real ultrasound recording to learn and simulate realistic speckle textures. The simulated images show typical artifacts that make motion tracking in ultrasound challenging. The ground-truth displacement field is available voxelwise and is fully controlled by the electromechanical model. By progressively modifying mechanical and ultrasound parameters, the sensitivity of 3D strain algorithms to pathology and image properties can be evaluated. The proposed pipeline is used to generate an initial library of 8 sequences including healthy and pathological cases, which is made freely accessible to the research community via our project web-page.
利用三维超声对心脏变形和应变进行量化需要大量的研究工作。然而,由于缺乏用于量化和比较性能的可靠验证过程,这些技术在临床实践中的广泛应用仍然受到阻碍。在此背景下,使用完全合成的序列已成为初始计算机模拟评估的既定工具。然而,现有模拟技术的逼真度仍然过于有限,无法提供可靠的基准数据。此外,不同中心通常使用自行开发的模拟流程,这使得公平比较变得困难。在此背景下,本文介绍了一种用于生成合成三维心脏超声图像序列的新型流程。机电建模和超声模拟领域的先进解决方案在一个原始框架内相结合,该框架利用真实的超声记录来学习和模拟逼真的散斑纹理。模拟图像显示出典型的伪像,这些伪像使得超声中的运动跟踪具有挑战性。真实的位移场以体素方式提供,并完全由机电模型控制。通过逐步修改机械和超声参数,可以评估三维应变算法对病理和图像特性的敏感性。所提出的流程用于生成一个包含健康和病理病例的8个序列的初始库,通过我们的项目网页向研究界免费提供。