Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800, Zabrze, Poland.
Sci Rep. 2022 Feb 11;12(1):2347. doi: 10.1038/s41598-022-06076-z.
In this study, we investigate perspectives for thermal tomography based on planar infrared thermal images. Volumetric reconstruction of temperature distribution inside an object is hardly applicable in a way similar to ionizing-radiation-based modalities due to its non-penetrating character. Here, we aim at employing the autoencoder deep neural network to collect knowledge on the single-source heat transfer model. For that purpose, we prepare a series of synthetic 3D models of a cylindrical phantom with assumed thermal properties with various heat source locations, captured at different times. A set of planar thermal images taken around the model is subjected to initial backprojection reconstruction, then passed to the deep model. This paper reports the training and testing results in terms of five metrics assessing spatial similarity between volumetric models, signal-to-noise ratio, or heat source location accuracy. We also evaluate the assumptions of the synthetic model with an experiment involving thermal imaging of a real object (pork) and a single heat source. For validation, we investigate objects with multiple heat sources of a random location and temperature. Our results show the capability of a deep model to reconstruct the temperature distribution inside the object.
在这项研究中，我们研究了基于平面红外热图像的热层析成像的观点。由于其非穿透性，基于容积重建的热层析成像很难像电离辐射模态那样应用。在这里，我们旨在利用自动编码器深度神经网络来收集有关单源热传递模型的知识。为此，我们准备了一系列具有不同热源位置的圆柱形模型的 3D 合成模型，这些模型的热特性是假设的，并且在不同的时间捕获。一组围绕模型拍摄的平面热图像经过初始反向投影重建后，再传递给深度模型。本文报告了五个评估容积模型之间空间相似性、信噪比或热源位置准确性的指标的训练和测试结果。我们还通过对真实物体（猪肉）和单个热源的热成像实验评估了合成模型的假设。为了验证，我们研究了具有随机位置和温度的多个热源的物体。我们的结果表明，深度模型有能力重建物体内部的温度分布。
