Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. DOT image reconstruction is an ill-posed problem due to the highly scattered photons in the medium and the smaller number of measurements compared to the number of unknowns. Limited-angle DOT reduces probe complexity at the cost of increased reconstruction complexity. Reconstructions are thus commonly marred by artifacts and, as a result, it is difficult to obtain an accurate reconstruction of target objects, e.g., malignant lesions. Reconstruction does not always ensure good localization of small lesions. Furthermore, conventional optimization-based reconstruction methods are computationally expensive, rendering them too slow for real-time imaging applications. Our goal is to develop a fast and accurate image reconstruction method using deep learning, where multitask learning ensures accurate lesion localization in addition to improved reconstruction. We apply spatial-wise attention and a distance transform based loss function in a novel multitask learning formulation to improve localization and reconstruction compared to single-task optimized methods. Given the scarcity of real-world sensor-image pairs required for training supervised deep learning models, we leverage physics-based simulation to generate synthetic datasets and use a transfer learning module to align the sensor domain distribution between in silico and real-world data, while taking advantage of cross-domain learning. Applying our method, we find that we can reconstruct and localize lesions faithfully while allowing real-time reconstruction. We also demonstrate that the present algorithm can reconstruct multiple cancer lesions. The results demonstrate that multitask learning provides sharper and more accurate reconstruction.

中文翻译：

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有限角度漫反射光学断层扫描中病灶的多任务深度学习重建和定位


漫射光学断层扫描 (DOT) 利用近红外光在组织中的传播来评估其光学特性并识别异常情况。由于介质中光子高度散射，并且与未知数相比测量数量较少，点图像重建是一个不适定问题。有限角度 DOT 降低了探针的复杂性，但代价是增加了重建的复杂性。因此，重建通常受到伪影的损害，并且因此难以获得目标对象（例如恶性病变）的准确重建。重建并不总能确保小病变的良好定位。此外，传统的基于优化的重建方法的计算成本很高，使得它们对于实时成像应用来说太慢。我们的目标是使用深度学习开发一种快速、准确的图像重建方法，其中多任务学习除了改进重建之外还确保准确的病变定位。与单任务优化方法相比，我们在新颖的多任务学习公式中应用了空间注意力和基于距离变换的损失函数，以改进定位和重建。鉴于训练监督深度学习模型所需的现实世界传感器图像对的稀缺性，我们利用基于物理的模拟来生成合成数据集，并使用迁移学习模块来调整计算机数据和现实世界数据之间的传感器域分布，同时利用跨领域学习。应用我们的方法，我们发现我们可以忠实地重建和定位病变，同时允许实时重建。我们还证明本算法可以重建多个癌症病灶。 结果表明，多任务学习提供了更清晰、更准确的重建。