Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:580-583. doi: 10.1109/EMBC48229.2022.9871293.
Incorporated with a structural prior, discrete cosine transformation (DCT) based electrical impedance tomog-raphy (EIT) algorithm can improve the interpretability of EIT images in clinical settings. However, this benefit comes with a risk of the untrue prior which yields a misleading result compromising clinical decision. The redistribution index is able to detect an untrue prior by analysing EIT reconstructions. In addition to structural priors, EIT reconstruction is also affected by the choice of hyperparameter A in DCT-based EIT algorithm. In this research, influence of hyperparameter on untrue prior detection is investigated in terms of simulation experiment. A series of simulation settings consisting of 30 different atelectasis scales was conducted, then reconstructed with 20 different hyperparameters, to investigate the behavior of redistribution index. The result shows, despite the fact that redistribution index is indeed influenced by the choice of the hyperparameter A, the detection of an untrue prior is not significantly affected. The untrue prior detection is rather stable regardless of the optimal hyperparameter. Clinical Relevance - Optimal hyperparameter is not always guaranteed in clinical settings. This research confirms that the untrue prior detection is not strongly influenced by the hyperparameter. An update of untrue priors incorporated into EIT approach will facilitate a better interpretation of EIT results and an accurate clinical decision.
结合结构先验，离散余弦变换（DCT）基电阻抗断层成像（EIT）算法可以提高 EIT 图像在临床环境中的可解释性。然而，这种好处伴随着不真实先验的风险，这会导致误导性的结果，从而影响临床决策。再分布指数能够通过分析 EIT 重建来检测不真实的先验。除了结构先验之外，EIT 重建还受到 DCT 基 EIT 算法中超参数 A 的选择的影响。在这项研究中，通过仿真实验研究了超参数对不真实先验检测的影响。进行了一系列包含 30 种不同肺不张程度的仿真设置，然后使用 20 种不同的超参数进行重建，以研究再分布指数的行为。结果表明，尽管再分布指数确实受到超参数 A 的选择的影响，但不真实先验的检测并没有受到显著影响。无论最优超参数如何，不真实先验的检测都相对稳定。临床相关性-在临床环境中并不总是能保证最优超参数。这项研究证实，不真实先验的检测并不受超参数的强烈影响。将不真实先验更新纳入 EIT 方法将有助于更好地解释 EIT 结果和做出准确的临床决策。
