Adaptive Layered Confocal Microwave Imaging Algorithm for Intracranial Hemorrhage Detection

Microwave imaging technology is an imaging method based on the propagation characteristics of electromagnetic waves, widely used in fields such as biomedicine and materials testing. Traditional confocal microwave imaging technology offers advantages such as fast computational speed and low resource consumption. However, when the number of antennas is limited, significant imaging artifacts arise. To address this issue, this study proposes an adaptive layered confocal microwave imaging algorithm. We construct an elliptical brain model with a major axis of 17 cm and a minor axis of 13 cm, embedding a circular hemorrhage region with a diameter ranging from 2 to 4 cm. This model simulates the microwave imaging environment for intracranial hemorrhage, and experiments are conducted under different antenna configurations (8, 12, 16, and 24 antennas). The mean relative pixel absolute difference (MRPAD), after standardization, is used as an evaluation metric to analyze imaging errors. Experimental results show that the proposed algorithm achieves high accuracy and resolution, effectively reduces artifacts, and performs well in intracranial hemorrhage imaging. It significantly outperforms traditional methods in improving image quality and reducing localization errors. This study provides valuable insights into the application of confocal microwave imaging technology in the biomedical field and has significant implications for the real-time implementation of algorithms and hardware miniaturization in future research.