Abstract
Electrical impedance tomography (EIT) is a noninvasive imaging modality that allows a continuous assessment of changes in regional bioimpedance of different organs. One of its most common biomedical applications is monitoring regional ventilation distribution in critically ill patients treated in intensive care units. In this work, we put forward a proof-of-principle study that demonstrates how one can reconstruct synchronously measured respiratory or circulatory parameters from the EIT image sequence using a deep learning model trained in an end-to-end fashion. For this purpose, we devise an architecture with a convolutional feature extractor whose output is processed by a recurrent neural network. We demonstrate that one can accurately infer absolute volume, absolute flow, normalized airway pressure and within certain limitations even the normalized arterial blood pressure from the EIT signal alone, in a way that generalizes to unseen patients without prior calibration. As an outlook with direct clinical relevance, we furthermore demonstrate the feasibility of reconstructing the absolute transpulmonary pressure from a combination of EIT and absolute airway pressure, as a way to potentially replace the invasive measurement of esophageal pressure. With these results, we hope to stimulate further studies building on the framework put forward in this work.
Highlights
E LECTRICAL impedance tomography (EIT) is an imaging modality with a wide range of applications
We trained a deep neural network composed of a convolutional feature extractor whose output was fed into a recurrent neural network
We demonstrated the same for the transpulmonary pressure using the airway pressure as additional input
Summary
E LECTRICAL impedance tomography (EIT) is an imaging modality with a wide range of applications. In the medical field it has primarily been used for functional lung imaging, Manuscript received October 23, 2020; revised February 1, 2021; accepted February 10, 2021. Small currents are injected between changing pairs of electrodes and the resulting voltages are measured between the remaining electrodes. The final step is image reconstruction, where these voltages are converted into cross sectional images by solving the underlying inverse problem [2], [3]. Recent machine learning-based approaches to this problem seem promising [4], [5]
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