微流控芯片流道亚波长分辨力全频域超声成像原理与方法研究

Microfluidic chip technology is applied to separation of micron scale cells, whose performance is directly affected by microchannel morphology. The microchannels with dimensions of sub-millimeter scale are constituted by complex structures, e.g. branching and cavity, leading to the difficulty of detection under working conditions. To solve the problems that ultrasonic detection resolution is restricted by Rayleigh criterion and microchannel morphology is hard to be presented, the principle and method for ultrasonic imaging of microchannels with sub-wavelength resolution are investigated by inversion processing of array signals in frequency domain. Sparse reflectivity inversion and minimum entropy deconvolution will be employed to extract feature signals for compressing pulse width, so as to clarify the inversion mechanism of time domain narrow pulse signals by frequency domain processing. The reconstructed signal matrix is used for imaging with frequency domain total focusing method (TFM) and time reversal multiple signal classification (TR-MUSIC), breaking through to sub-wavelength resolution. The ray paths of high-order mode waves in substrates and microchannels will be analyzed to determine the most sensitive orientation zones of microchannel sidewalls for different modes and mode-converted waves, revealing the anisotropic responding mechanism of mode waves. The distorted signals are corrected according to Doppler effect, so frequency domain TFM with fluid-solid mode will be developed to clarify the ultrasonic imaging principle for microchannels. This investigation proposes a full frequency domain ultrasonic imaging method with sub-wavelength resolution for microchannels, which would provide technical foundation for performance monitoring of microfluidic chips, and also the improvement of preparation technology.

微流控芯片技术能够实现微米级细胞分选，流道形貌特征直接关系芯片性能。流道尺寸处于亚毫米级，且存在分枝、空腔等复杂结构，导致工作状态下表征困难。针对超声检测分辨力受瑞利准则制约，且难以呈现流道形貌的难题，基于阵列信号全频域反演处理，开展微流控芯片流道亚波长分辨力超声成像原理与方法研究。利用稀疏解卷积和最小熵解卷积方法提取特征检测信号，压缩回波脉冲宽度，阐明时域窄脉冲信号频域反演机制。结合频域全聚焦与多信号分类时间反转方法，对重建信号矩阵实施频域成像，突破至亚波长分辨力。分析高阶模式波在微流控芯片基板和流道内传播路径，明确不同模式与波型组合下的流道侧壁取向敏感区间，揭示模式波各向异性响应机制。根据多普勒效应修正畸变信号，开发频域流-固模式TFM算法，澄清流道超声成像原理。研究提出的微流控芯片流道亚波长分辨力全频域超声成像方法，能够为芯片工作性能监测奠定技术基础，并为芯片制备工艺改进创造条件。

微流控芯片流道尺寸处于亚毫米级且结构复杂，超声检测时存在分辨力不足和特征辨识困难等难题，制约芯片性能评价。项目针对不同特征微流控芯片，开展基于阵列信号反演的亚波长级分辨力全频域超声成像研究。利用超声C扫描观测芯片流道，对比分析检测频率等关键参数对表征结果的影响，并定性描述流道宽度变化，识别流道堵塞状态。结合频域自回归谱外推方法重建具有时域窄脉冲特征的阵列信号，分析加权延时叠加与成像分辨力的相关性。引入时域、频域和时频域等多域特征参量，采用机器学习方法实施解卷积反演，突破瑞利准则，实现了亚波长级分辨力超声成像与定量检测。研究了基于波场外推的频域全聚焦成像方法，分析频域插值和层间加权对成像分辨力和定量检测的影响效果。定量描述不同发射阵元时的阵列信号成像贡献程度，发展了自适应权重稀疏方法，提升频域全聚焦成像效率。结合声线示踪理论，明确特征信号在微流控芯片内的传播、反射与接收行为，揭示了模式波各向异性响应机制。考虑声束覆盖范围与成像效率，提出有效模式波筛选及复合成像方法，实现了先验未知取向流道的完整轮廓重建。上述研究工作为微流控芯片的高效、精准检测提供有力支撑。. 项目期间，负责人以第一/通讯作者发表及录用论文16篇，其中SCI收录8篇，EI收录/刊源5篇，《航空学报》(EI刊源)录用文章被选为封面文章。申请中国发明专利11项，美国发明专利1项，其中11项排序第一，并已授权4项；授权软件著作权2项。指导的研究生获“奥林巴斯杯2021超声检测技术论文评选”优秀论文一等奖和大连理工大学优秀毕业生。

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