A deep neural network for a hemiarray EIT system

Abstract

Electrical Impedance Tomography (EIT) can map electrical property distributions within the body using a surface electrode array. EIT systems using a circumferential array applied to the abdomen can be used to monitor acute intra-abdominal hemorrhages in trauma patients. Nevertheless, these patients may also have suffered spinal injuries that might be exacerbated by lifting the patient to place the array. Thus, a half array ('hemiarray') applied only to the anterior abdomen may be more practical. However, severe reconstruction artifacts result in posterior regions using standard EIT reconstruction methods. This study proposes a novel machine learning-based approach for standard full and hemiarray EIT reconstructions, demonstrating superior reconstruction characteristics compared to conventional methods. Notably, our method mitigates the challenges of reconstructing anomalies in posterior regions. This performance advantage was consistently observed across reconstructions from simulated and real tank data. Based on our findings, we conclude that the machine learning-based hemiarray reconstruction method holds significant promise for challenging imaging scenarios, particularly when access to the anterior or posterior abdomen is restricted.