On the optimization of pleural line scanning through automatic robot assisted lung ultrasound, an experimental study

Lung ultrasound (LUS) is used to assess the state of lung surface, but is strongly operator-dependent, leading to reduced reproducibility of LUS analysis. Indeed, clinicians can guarantee only limited precision in intercepting optimal imaging plane (O-ImP). Hence, in this study, we experimentally assessed the possibility to automatically intercept O-ImP in LUS exams (i.e., ImP perpendicular to pleural plane) by extracting three features, utilized to guide a robotic arm (UR5e) handling an ultrasound probe. We conducted experiments by designing a highly controllable environment, imaged with linear probe connected to ULA-OP platform and held by UR5e, programmed to explore 328 positions having rotational angles (RA) from −20º to 20º (positions with RA = 0º correspond to O-ImP). Normalized log-scale B-Mode images were formed and a rectangular region of interest (ROI) was considered to compute mean intensity (MeIn) at each depth of ROI. MeIn was utilized to extract: 1. max of cross-correlation of MeIn with envelope of transmitted pulse; 2. variance of MeIn; 3. spatial −12-dB-bandwidth of MeIn. These three features were fed to genetic algorithms to solve optimization problems to guide UR5e toward O-ImP. Results showed strong potential of investigated features in automatically intercepting the O-ImP in LUS examinations, with average errors below 1º.