用于基准测试基于超声心动图的生物力学的合成超声图像
Synthetic ultrasound images to benchmark echocardiography-based biomechanics
AI总结：
针对超声心脏运动估计缺乏跨供应商可复现基准的问题，本文提出由有限元生物力学模拟生成合成B-mode超声图像，提供位移、应变基准量以支持算法评估。
摘要：
亮度模式（B-mode）超声是临床评估多种心血管疾病的常用成像方式。基于超声的功能指标，如射血分数（EF）和每搏输出量（SV），其效用在晚期心血管疾病诊断中已有广泛描述。此外，通过分析心脏运动获得的结构性指标，已被发现在肥厚型心肌病和心肌梗死等结构性心脏病的早期评估中具有重要意义。通过B-mode超声成像估计心脏运动的异质性变化是患者护理的关键组成部分。尽管此类成像技术具有优势，但由于缺乏基准运动量，运动估计算法容易受到不同供应商之间差异的影响。相比之下，心脏生物力学的有限元（FE）模拟利用成熟的心肌本构模型来确保可重复性。在本研究中，我们开发了一种从FE模拟创建合成B-mode超声图像的方法。所提出的方法在复杂的小鼠特异性左心室（LV）加载方案下，提供了位移和应变的详细表示。合成图像与FE模拟之间的比较揭示了位移模式在定性上的相似性，从而产生了基准量，以提高运动估计算法的可重复性。因此，本研究提供了一种创建描述复杂运动模式的大型图像库的方法，以促进心脏运动分析可重复性的增强。
英文摘要：
Brightness mode (B-mode) ultrasound is a common imaging modality in the clinical assessment of several cardiovascular diseases. The utility of ultrasound-based functional indices such as the ejection fraction (EF) and stroke volume (SV) is widely described in diagnosing advanced-stage cardiovascular diseases. Additionally, structural indices obtained through the analysis of cardiac motion have been found to be important in the early-stage assessment of structural heart diseases, such as hypertrophic cardiomyopathy and myocardial infarction. Estimating heterogeneous variations in cardiac motion through B-mode ultrasound imaging is a crucial component of patient care. Despite the benefits of such imaging techniques, motion estimation algorithms are susceptible to variability between vendors due to the lack of benchmark motion quantities. In contrast, finite element (FE) simulations of cardiac biomechanics leverage well-established constitutive models of the myocardium to ensure reproducibility. In this study, we developed a methodology to create synthetic B-mode ultrasound images from FE simulations. The proposed methodology provides a detailed representation of displacements and strains under complex mouse-specific loading protocols of the LV. A comparison between the synthetic images and FE simulations revealed qualitative similarity in displacement patterns, thereby yielding benchmark quantities to improve the reproducibility of motion estimation algorithms. Thus, the study provides a methodology to create an extensive repository of images describing complex motion patterns to facilitate the enhanced reproducibility of cardiac motion analysis.
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