Model Selection Based Algorithm in Neonatal Chest EIT
Model Selection Based Algorithm in Neonatal Chest EIT
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DOI：
10.1109/tbme.2021.3053463
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发表时间：
2021-09-01
影响因子：
4.6
通讯作者：
Bayford, Richard H.
中科院分区：
文献类型：
--
作者：
Seifnaraghi, Nima;de Gelidi, Serena;Bayford, Richard H.
本文提出了一种新的方法来选择一个病人特定的前向模型，以补偿解剖变异的电阻抗断层成像（EIT）监测的新生儿。该方法使用形状传感器和绝对重建的组合。它利用了一种概率方法，可以从预存储的库模型中自动选择最佳的估计正演模型拟合。绝对/静态图像重建作为后验概率计算的核心来执行。该算法的有效性和可靠性，在检测一个合适的模型，在测量噪声的存在下进行了研究，从11例患者的模拟和测量数据。本文还通过考虑一个独特的病例研究，证明了从EIT图像中提取的临床参数的潜在改进，该病例研究将接受计算机断层扫描成像的新生儿患者作为EIT监测前的临床适应症。两个著名的图像重建技术，即GREIT和tSVD，实现创建最终的潮汐图像。适当的模型选择的临床提取的参数，如通风中心和安静的空间的影响进行了研究。结果表明，最终重建图像的显着改善，更重要的是，从图像中提取的临床EIT参数，决策和进一步干预是至关重要的。
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.