Author Affiliations
1 Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China
2 Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
3 Tera Aurora Electro-optics Technology Co., Ltd., Shanghai 200093, China
4 Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China
This work presents a brief review of our recent research on an antiresonant mechanism named core antiresonant reflection (CARR), which leads to a broadband terahertz (THz) spectrum output with periodic dips at resonant frequencies after its transmission along a hollow-core tubular structure (e.g., a paper tube). The CARR theory relies only on parameters of the tube core (e.g., the inner diameter) rather than the cladding, thus being distinct from existing principles such as the traditional antiresonant reflection inside optical waveguides (ARROWs). We demonstrate that diverse tubular structures, including cylindrical, polyhedral, spiral, meshy, and notched hollow tubes with either transparent or opaque cladding materials, as well as a thick-walled hole, could indeed become CARR-type resonators. Based on this CARR effect, we also perform various applications, such as pressure sensing with paper-folded THz cavities, force/magnetism-driven chiral polarization modulations, and single-pulse measurements of the angular dispersion of THz beams. In future studies, the proposed CARR method promises to support breakthroughs in multiple fields by means of being extended to more kinds of tubular entities for enhancing their interactions with light waves in an antiresonance manner.
antiresonance core cladding tubular structure application 
Chinese Optics Letters
2023, 21(11): 110005
上海理工大学 医疗器械与食品学院, 上海 200093
CT图像中肺叶位置的确定对于肺部疾病的准确定位以及定性定量分析具有重要意义。为了提高肺叶自动分割准确率,提出了一种结合气管,血管等传统解剖学特征以及深度学习的肺叶分割算法。对原始图像进行预处理,获取肺实质、气管、血管以及基于深度学习网络的肺裂分割结果; 整合来自多个解剖结构的信息生成分水岭分割所需成本图像; 通过基于深度学习网络的肺叶粗分割结果,获取肺叶标记区域; 执行基于标记的分水岭分割,实现肺叶的自动分割。选取了来自上海市肺科医院的20例含有肺部疾病患者的CT图像对该方法进行验证,最终的Jaccard相似性系数为92.4%。实验结果表明方法具有较高的肺叶分割精度,并且具有较强的鲁棒性。
CT图像 肺叶分割 深度学习 肺部管道 分水岭分割 CT scans lung lobe deep learning lung tubular structure watershed segmentation 
2020, 46(2): 230

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