| Popis: |
Prior information, both morphological and functional, can improve reconstruction of the electrical impedance tomography (EIT). In this contribution, two different priors from a personalized morphological image were introduced into an EIT algorithm using discrete cosine transformation constraining subset. A retrospective patient research was conducted to demonstrate the effect that the different levels of priors have on the results. If the accuracy of the priors can be ensured, this algorithm can mean a step out for the possibility of a more interpretive EIT reconstruction facilitating the diagnostic, monitoring and automated adjustment of ventilator setting procedure in a clinical setting. |
