Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation.
Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation.
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DOI：
10.1109/tmi.2020.3001750
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发表时间：
2020-11
影响因子：
10.6
通讯作者：
Ntziachristos V
中科院分区：
文献类型：
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作者：
Olefir I;Tzoumas S;Restivo C;Mohajerani P;Xing L;Ntziachristos V
组织氧合分布的无标记成像在许多生物医学应用中是非常理想的，但仍然是难以捉摸的，特别是在皮下测量中。本征谱多光谱光声断层成像(EMSOT)及其基于贝叶斯的实现已被引入以提供组织中准确的无标记血氧饱和度(SO2)图。该方法使用组织中光通量的本征谱模型来解释由于光的波长依赖于组织深度的衰减而引起的光谱变化。EMSOT依赖于反问题的解，该逆问题受许多特别手工设计的约束的约束。尽管eMSOT提供了数量优势，但优化问题的非凸性和约束可能的次优性都可能导致精度降低。我们在这里提出了一种神经网络结构，它能够通过直接从一组输入光谱回归到期望的注量值来学习如何解决eMSOT的逆问题。该体系结构由递归层和卷积层的组合组成，并使用频谱和空间特征进行推理。我们使用单独的模拟数据来训练这样的网络集合，并展示了这种方法如何提高SO2计算的精度，不仅在模拟中，而且在从血液模体和体内小动物(老鼠)获得的实验数据集中。在此首次证实了在光声SO2成像中使用深度学习方法，该方法基于在体内和体外实验获得的地面真实SO2值。
Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular in sub-epidermal measurements. Eigenspectra multispectral optoacoustic tomography (eMSOT) and its Bayesian-based implementation have been introduced to offer accurate label-free blood oxygen saturation (sO2) maps in tissues. The method uses the eigenspectra model of light fluence in tissue to account for the spectral changes due to the wavelength dependent attenuation of light with tissue depth. eMSOT relies on the solution of an inverse problem bounded by a number of ad hoc hand-engineered constraints. Despite the quantitative advantage offered by eMSOT, both the non-convex nature of the optimization problem and the possible suboptimality of the constraints may lead to reduced accuracy. We present herein a neural network architecture that is able to learn how to solve the inverse problem of eMSOT by directly regressing from a set of input spectra to the desired fluence values. The architecture is composed of a combination of recurrent and convolutional layers and uses both spectral and spatial features for inference. We train an ensemble of such networks using solely simulated data and demonstrate how this approach can improve the accuracy of sO2 computation over the original eMSOT, not only in simulations but also in experimental datasets obtained from blood phantoms and small animals (mice) in vivo. The use of a deep-learning approach in optoacoustic sO2 imaging is confirmed herein for the first time on ground truth sO2 values experimentally obtained in vivo and ex vivo.