论文标题
机器学习启用了人类血液氧合的多个照明定量光声成像
Machine learning enabled multiple illumination quantitative optoacoustic imaging of blood oxygenation in humans
论文作者
论文摘要
光声（OA）成像是在各种生物医学应用中量化血氧饱和度（SO $ _2 $）的一种有希望的方式 - 在诊断，监测器官功能甚至肿瘤治疗计划方面。我们提出了一种基于多光谱（MS）和多个照明（MI）OA成像与学识渊博的频谱解粉（LSD）相结合的$ _2 $定量成像的准确且实际上可行的实时方法。为此，我们开发了具有超声（US）成像能力的混合实时MI MS OA成像设置；我们训练了由通用蒙特卡洛模拟产生的MI频谱上的梯度提升机，并使用训练有素的模型估算了实际OA测量值$ _2 $。我们验证了径向动脉的体内和体内图像序列的MI-LSD，并伴随着五名健康人类志愿者的静脉。我们将该方法的性能与先前的LSD工作和常规线性Umbixing进行了比较。 MI-LSD在体内提供了高度准确的结果，并始终如一地在体内结果。这项初步研究表明，使用我们的方法，定量OA的血氧仪成像的可能性很高。
Optoacoustic (OA) imaging is a promising modality for quantifying blood oxygen saturation (sO$_2$) in various biomedical applications - in diagnosis, monitoring of organ function or even tumor treatment planning. We present an accurate and practically feasible real-time capable method for quantitative imaging of sO$_2$ based on combining multispectral (MS) and multiple illumination (MI) OA imaging with learned spectral decoloring (LSD). For this purpose we developed a hybrid real-time MI MS OA imaging setup with ultrasound (US) imaging capability; we trained gradient boosting machines on MI spectrally colored absorbed energy spectra generated by generic Monte Carlo simulations, and used the trained models to estimate sO$_2$ on real OA measurements. We validated MI-LSD in silico and on in vivo image sequences of radial arteries and accompanying veins of five healthy human volunteers. We compared the performance of the method to prior LSD work and conventional linear unmixing. MI-LSD provided highly accurate results in silico and consistently plausible results in vivo. This preliminary study shows a potentially high applicability of quantitative OA oximetry imaging, using our method.
