IEEE Trans Biomed Eng. 2021 Sep;68(9):2752-2763. doi: 10.1109/TBME.2021.3053463. Epub 2021 Aug 19.
This paper presents a new method for selecting a patient specific forward model to compensate for anatomical variations in electrical impedance tomography (EIT) monitoring of neonates. The method uses a combination of shape sensors and absolute reconstruction. It takes advantage of a probabilistic approach which automatically selects the best estimated forward model fit from pre-stored library models. Absolute/static image reconstruction is performed as the core of the posterior probability calculations. The validity and reliability of the algorithm in detecting a suitable model in the presence of measurement noise is studied with simulated and measured data from 11 patients. The paper also demonstrates the potential improvements on the clinical parameters extracted from EIT images by considering a unique case study with a neonate patient undergoing computed tomography imaging as clinical indication prior to EIT monitoring. Two well-known image reconstruction techniques, namely GREIT and tSVD, are implemented to create the final tidal images. The impacts of appropriate model selection on the clinical extracted parameters such as center of ventilation and silent spaces are investigated. The results show significant improvements to the final reconstructed images and more importantly to the clinical EIT parameters extracted from the images that are crucial for decision-making and further interventions.
本文提出了一种新的方法，用于选择特定于患者的正向模型，以补偿新生儿电阻抗断层扫描（EIT）监测中的解剖变异。该方法结合了形状传感器和绝对重建。它利用了一种概率方法，该方法可自动从预存储的库模型中选择最佳估计的正向模型拟合。绝对/静态图像重建作为后验概率计算的核心。使用来自 11 名患者的模拟和测量数据研究了该算法在存在测量噪声的情况下检测合适模型的有效性和可靠性。本文还通过考虑一个具有代表性的临床案例研究，证明了在对接受 EIT 监测的新生儿患者进行 CT 成像作为临床指征之前，考虑独特的案例研究，通过考虑独特的案例研究，证明了该算法在检测合适模型方面的有效性和可靠性。通过考虑一个具有代表性的临床案例研究，证明了该算法在检测合适模型方面的有效性和可靠性。该案例研究涉及一名新生儿患者，在接受 EIT 监测之前进行 CT 成像作为临床指征。实施了两种著名的图像重建技术，即 GREIT 和 tSVD，以创建最终的潮气量图像。研究了适当模型选择对中心通气和无声空间等临床提取参数的影响。结果表明，最终重建图像以及更重要的是从图像中提取的临床 EIT 参数有了显著的改善，这对于决策和进一步干预至关重要。
