Operational Learning-based Boundary Estimation in Electromagnetic Medical Medical Imaging

Incorporating boundaries of the imaging object as a priori information to imaging algorithms can significantly improve the performance of electromagnetic medical imaging systems. To avoid overly complicating the system by using different sensors and the adverse effect of the subject’s movement, a learning-based method is presented in this paper proposed proposed to estimate the boundary of the imaged object using the same electromagnetic imaging data. Numerous design alternatives were evaluated and juxtaposed with the overarching goal of reaching a reliable operational model in practical settings. The proposed While imaging techniques generally discard the reflection coefficients for being dominant and uninformative for imaging, these parameters are made use of for boundary detection. The learned While imaging techniques generally discard the reflection coefficients for being dominant and uninformative for imaging, these parameters are made use of for boundary detection. The learned model is verified through independent clinical human trials by using a head imaging system with a 16-element antenna array that works across the band 0.5-2 0.7-1.6 0.7-1.6 GHz. The evaluation demonstrated that the model achieves average dissimilarity of 0.012 in Hu-moment while detecting head boundary. The model enables fast scan and image creation while eliminating the need for additional devices for accurate boundary estimation.