Design and optimization of miniaturized co-planar Vivaldi antennas for enhanced microwave imaging in brain hemorrhage detection

We designed and optimized a miniaturized coplanar Vivaldi antenna specifically for microwave imaging in cerebral hemorrhage detection. The antenna measures 80 mm × 80 mm × 1 mm and features an arc-shaped radiating arm, a 3 mm × 3 mm optimized pad layout, and an improved metallized via structure with nine vias, each 0.5 mm in diameter. These enhancements significantly improve the antenna's directivity, impedance matching, and signal penetration capability. Experimental results demonstrate that the antenna operates stably within the ultra-wide frequency band of 1.6-8 GHz, achieving a reflection coefficient as low as -45 dB at 4 GHz, a voltage standing wave ratio (VSWR) consistently below 1.5, and a peak gain of 9.5 dB at 6.5 GHz. These characteristics fully meet the sensitivity and penetration depth requirements for medical imaging. In addition to presenting a novel antenna design, this study validates its effectiveness under realistic biological conditions. Comparative analysis between 18- and 36-element antenna arrays demonstrates that the 36-element configuration improves image resolution and signal uniformity, while the 18-element array offers faster acquisition and better suitability for emergency or point-of-care screening scenarios. Additionally, in realistic skull model experiments, we employed rotating antenna technology (with a 20° step size) and multi-angle signal acquisition, further optimizing imaging uniformity and detection accuracy in hemorrhagic regions. By integrating real-time differential imaging technology and beamforming algorithms such as Delayed Sum (DAS) and Delayed Multiplication and Sum (DMAS), the experimental results indicate substantial progress in the identification of brain hemorrhage areas. This research provides critical technical support for the development of portable and non-invasive cerebral hemorrhage detection systems. Overall, by integrating miniaturization, performance optimization, and targeted enhancements, this study provides a robust technical basis for the development of early stroke detection systems.