IEEE Trans Biomed Eng. 2022 Feb;69(2):710-717. doi: 10.1109/TBME.2021.3103776. Epub 2022 Jan 20.
This study investigates the feasibility of using a new self-powered sensing and data logging system for postoperative monitoring of spinal fusion progress. The proposed diagnostic technology directly couples a piezoelectric transducer signal into a Fowler-Nordheim (FN) quantum tunneling-based synchronized dynamical system to record the mechanical usage of spinal fixation devices. The operation of the proposed implantable FN sensor-data-logger is completely self-powered by harvesting the energy from the micro-motion of the spine during the course of fusion. Bench-top testing is performed using corpectomy models to evaluate the performance of the proposed monitoring system. In order to simulate the spinal fusion process, different materials with gradually increasing elastic modulus are used to fill the intervertebral space gap. Besides, finite element models are developed to analyze the strains induced on the spinal rods during the applied cyclic loading. Data measured from the benchtop experiment is processed using an FN sensor-data-logger model to obtain time-evolution curves representing each spinal fusion state. This feasibility study shows that the obtained curves are viable tools to differentiate between conditions of osseous union and assess the effective fusion period.
本研究探讨了一种新的自供电传感和数据记录系统在脊柱融合术后进展监测中的可行性。所提出的诊断技术直接将压电换能器信号耦合到基于福勒-诺德海姆(FN)量子隧穿的同步动力学系统中,以记录脊柱固定装置的机械使用情况。所提出的植入式 FN 传感器-数据记录器的操作完全通过从融合过程中脊柱的微运动中收集能量来实现自供电。使用椎体切除术模型进行台式测试,以评估所提出的监测系统的性能。为了模拟脊柱融合过程,使用具有逐渐增加弹性模量的不同材料来填充椎间隙间隙。此外,还开发了有限元模型来分析在施加的循环载荷过程中脊柱杆上产生的应变。使用 FN 传感器-数据记录器模型处理从台式实验中获得的数据,以获得代表每个脊柱融合状态的时变曲线。这项可行性研究表明,所获得的曲线是区分骨性融合和评估有效融合期的可行工具。