Helmholtz Zentrum München and Technische Universtität München, Institute for Biological and Medical Imaging, 85764 Neuherberg, Germany.
J Biomed Opt. 2012 Jan;17(1):016009. doi: 10.1117/1.JBO.17.1.016009.
Cardiac imaging in small animals is a valuable tool in basic biological research and drug discovery for cardiovascular disease. Multispectral optoacoustic tomography (MSOT) represents an emerging imaging modality capable of visualizing specific tissue chromophores at high resolution and deep in tissues in vivo by separating their spectral signatures. Whereas single-wavelength images can be acquired by multielement ultrasound detection in real-time imaging, using multiple wavelengths at separate times can lead to image blurring due to motion during acquisition. Therefore, MSOT imaging of the heart results in degraded resolution because of the heartbeat. In this work, we applied a clustering algorithm, k-means, to automatically separate a sequence of single-pulse images at multiple excitation wavelengths into clusters corresponding to different stages of the cardiac cycle. We then performed spectral unmixing on each cluster to obtain images of tissue intrinsic chromophores at different cardiac stages, showing reduced sensitivity to motion compared to signal averaging without clustering. We found that myocardium images of improved resolution and contrast can be achieved using MSOT motion clustering correction. The correction method presented could be generally applied to other MSOT imaging applications prone to motion artifacts, for example, by respiration and heartbeat.
小动物心脏成像在心血管疾病的基础生物学研究和药物发现中是一种非常有价值的工具。多谱段光声断层扫描（MSOT）是一种新兴的成像模式，能够通过分离其光谱特征，以高分辨率和在体内深层可视化特定的组织发色团。虽然在实时成像中可以通过多阵元超声检测获得单波长图像，但由于采集过程中的运动，使用多个不同波长会导致图像模糊。因此，由于心跳，MSOT 心脏成像的分辨率会降低。在这项工作中，我们应用了聚类算法 k-均值，将多个激发波长的单脉冲图像序列自动分为与心动周期不同阶段相对应的簇。然后，我们对每个簇进行光谱解混，以获得不同心脏阶段的组织固有发色团的图像，与不聚类的信号平均相比，对运动的敏感性降低。我们发现，使用 MSOT 运动聚类校正可以实现分辨率和对比度提高的心肌图像。所提出的校正方法可以普遍应用于其他容易出现运动伪影的 MSOT 成像应用，例如呼吸和心跳。
