残余物
材料科学
人工神经网络
激光器
噪音(视频)
计算机科学
反演(地质)
反变换采样
生物医学工程
反向
人工智能
光学
算法
物理
医学
图像(数学)
地质学
数学
古生物学
电信
几何学
构造盆地
表面波
作者
Hao Zhang,Dong Li,Bin Chen
标识
DOI:10.1016/j.ijthermalsci.2023.108502
摘要
Laser skin dermatology has attracted increasing attention with awareness of the aesthetic sense. Real-time detection of subcutaneous vessel temperature and structure during laser treatment is essential for efficacy and safety of surgery. By using 1D residual neural network to improve the accuracy of feature extraction, an inverse method model with outstanding reconstruction performance and noise insensitivity was proposed to estimate vascular parameters including temperature, depth and diameter of blood vessel. The network established an end-to-end mapping of the time-dependent skin surface temperature after short-pulse laser irradiation to the temperature gradient along the skin depth, allowing vascular parameters to be extracted directly from the observed data measured by the infrared camera. The model trained by data containing noise of multiple intensities could identify and attenuate noise in the original data, which is beneficial to suppress the broadening effect of a significant broadening of reconstructed temperature peaks induced by noise and significantly improve the reconstruction accuracy of less than 5%. In comparison with traditional iterative methods, the new model shows technical superiority in its unparalleled accuracy and low computational cost as well as great potential for real-time monitoring of subcutaneous vascular features.
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