CAREER: Developing a compact wireless multi-modal detector array for remote sensing and imaging

职业：开发用于遥感和成像的紧凑型无线多模态探测器阵列

• 批准号: 2144138
• 负责人: Chunqi Qian
• 金额: $ 50万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144138&HistoricalAwards=false
• 关键词: CAREER; Developing; compact; wireless; multi

Wireless and batteryless sensors are particularly useful for long-term monitoring of physiological parameters inside confined body cavities. Traditionally, a batteryless sensor is implemented as a passive resonator whose resonance frequency is responsive to physiological variation. When the embedded sensor is sufficiently close to the body surface, its resonance frequency can be directly estimated from radio frequency signals backscattered from the sensor. However, when the penetration depth is much larger than the passive sensor’s own dimension, backscattered signals can be too small to detect. In addition, a passive sensor has limited temporal resolution because repetitive averaging of backscattered signals is required to achieve a reasonable measurement accuracy. To overcome these limitations, this CAREER project will develop a generally applicable circuit that can significantly improve the remote detectability and temporal resolution of batteryless sensors. Using a very compact circuit design, this Self-oscillation Encoding Telemeter (SET) can utilize wireless power to self-oscillate and encode input signals over a wide frequency range. When the SET is utilized to simultaneously encode neuronal voltages and magnetic resonance images (MRI), it can provide correlated information between electrophysiology and imaging, revealing novel insights into the neuronal origin of MRI signal dynamics. Besides its immediate impact on neuroscience research, the SET can improve the sensing capability for many other types of input signals including pH, pressure, humidity, temperature, and strain. Owing to its novel concept, concise design, and broad impacts, the SET sensor will be used as an excellent vehicle to inspire scientific interest of K-12 students, and to educate STEM students (including those from underrepresented groups). It will also serve the national interest with potential transformation of this wireless sensing technology into many advanced sensor systems.To significantly improve the remote detectability of batteryless sensors, Self-oscillation Encoding Telemeters (SETs) will be developed to encode various types of input signals over a wide frequency range. This project features three novel methods to retrieve multiplexed input signals. (1) When input signals with distinct frequency characteristics are encoded by the same SET sensor, they can be resolved by their different separations from the carrier wave. (2) When multiple SETs are concatenated into an array, each sensor will be wirelessly activated to have a unique oscillation frequency, so that input signals encoded by individual SETs can be identified by their sideband patterns. (3) When input signals of similar frequency characteristics are encoded by the same SET, they can be consecutively encoded by cyclically switching the pumping frequency at a faster speed, enabling selective activation of each encoding mode. These three novel methods will be implemented towards four research goals. Based on the sideband separation technique described in method #1, aim 1 will decode electrophysiology (slow) and MRI (fast) signals that are simultaneously encoded and broadcasted by the SET sensor. Based on the frequency-division multiplexing concept described in method #2, aim 2 will develop a planar SETs array and signal processing algorithms to retrieve encoded images from individual sensors, enabling high-resolution mapping of micro vessel distribution along the brain surface. Meanwhile, aim 3 will develop an insertion catheter aligned with a linear SETs array, revealing the underlying correlation between MRI and neuronal signals across multiple brain layers. Utilizing the time-division multiplexing concept described in method #3, aim 4 will consecutively encode multiple quasistatic sensing signals while continuously encoding high-frequency imaging signals at the same time. Collectively, advances from these endeavors will make the batteryless sensors useful for a range of applications from biomedicine to structural monitoring and environmental sensing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

无线和无电池传感器对于长期监测封闭体腔内的生理参数特别有用。传统上，无电池传感器被实现为无源谐振器，其谐振频率响应于生理变化。当嵌入式传感器足够接近身体表面时，其谐振频率可以从从传感器反向散射的射频信号直接估计。然而，当穿透深度远大于无源传感器自身的尺寸时，后向散射信号可能太小而无法检测。此外，无源传感器具有有限的时间分辨率，因为需要对反向散射信号进行重复平均以实现合理的测量精度。为了克服这些限制，CAREER项目将开发一种普遍适用的电路，可以显着提高无电池传感器的远程可探测性和时间分辨率。使用一个非常紧凑的电路设计，这种自激振荡编码遥测仪（SET）可以利用无线电源自激振荡和编码输入信号在一个很宽的频率范围。当SET用于同时编码神经元电压和磁共振图像（MRI）时，它可以提供电生理和成像之间的相关信息，揭示MRI信号动力学的神经元起源的新见解。除了对神经科学研究的直接影响外，SET还可以提高对许多其他类型输入信号的传感能力，包括pH值，压力，湿度，温度和应变。由于其新颖的概念，简洁的设计和广泛的影响，SET传感器将被用作激发K-12学生科学兴趣的绝佳工具，并教育STEM学生（包括来自代表性不足的群体的学生）。它还将服务于国家利益，将这种无线传感技术转化为许多先进的传感器系统。为了显着提高无电池传感器的远程探测能力，将开发自振荡编码遥测仪（SET），以在宽频率范围内对各种类型的输入信号进行编码。这个项目的特点是三种新的方法来检索多路复用输入信号。(1)当具有不同频率特性的输入信号由同一SET传感器编码时，它们可以通过与载波的不同分离来分辨。(2)当多个SET串联成一个阵列时，每个传感器将被无线激活以具有唯一的振荡频率，从而可以通过其边带模式识别由各个SET编码的输入信号。(3)当相似频率特性的输入信号由相同SET编码时，它们可以通过以更快的速度循环切换泵浦频率来连续编码，使得能够选择性地激活每个编码模式。这三种新方法将实现四个研究目标。基于方法#1中描述的边带分离技术，目标1将解码SET传感器同时编码和广播的电生理（慢）和MRI（快）信号。基于方法#2中描述的频分复用概念，目标2将开发平面SET阵列和信号处理算法，以从各个传感器检索编码图像，从而实现微血管分布沿着脑表面的高分辨率映射。与此同时，aim 3将开发一种与线性SET阵列对齐的插入导管，揭示MRI与跨多个大脑层的神经元信号之间的潜在相关性。利用方法#3中描述的时分复用概念，目标4将连续地编码多个准静态感测信号，同时连续地编码高频成像信号。总的来说，这些努力的进步将使无电池传感器在从生物医学到结构监测和环境传感的一系列应用中发挥作用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Novel inductively coupled ear-bars (ICEs) to enhance restored fMRI signal from susceptibility compensation in rats.

• DOI: 10.1093/cercor/bhad479
• 发表时间: 2024-01-14
• 期刊: Cerebral cortex (New York, N.Y. : 1991)