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IEEE Open Journal of Antennas and Propagation

rigorous peer review | rapid publication | open access

Electromagnetic Medical Imaging and Sensing based on A Priori Knowledge and Machine Learning

Download Call for Papers (PDF)

Submission Deadline: January 31, 2025

Electromagnetic Medical Imaging and Sensing based on A Priori Knowledge and Machine LearningAims & Scope:

In recent years, there has been a significant interest in the development and application of electromagnetic medical imaging and sensing (EMIS) techniques. These technologies offer new opportunities for non-invasive diagnosis, monitoring, and treatment in the field of healthcare. It is challenging to develop accurate and robust EMIS algorithms for both sensitive and selective diagnosis, due to the inherently ill-conditioned inverse scattering problems and the low dielectric contrast between healthy and diseased tissues. As such, using space-time-frequency a priori knowledge (APK) and machine learning to improve the performance of EMIS is crucial for practical implementation and clinical deployment of medical systems.

Potential topics include but are not limited to the following:

  • Microwave and millimetre-wave imaging based on APK and machine learning
  • Electromagnetic sensors for physiological monitoring
  • Electromagnetic-based wearable devices for health monitoring
  • Hybrid imaging techniques combining multiple modalities
  • Image reconstruction algorithms and signal processing
  • Electromagnetic tomography based on APK and machine learning
  • Electric impedance or capacitance tomography
  • Emerging trends and future directions in EMIS

Keywords:

  1. Microwave and Millimetre-wave Imaging and Sensing
  2. A Priori Knowledge
  3. Machine Learning and AI
  4. Inverse Problem
  5. Antennas for Biomedical Applications
  6. Biomedical Image Processing

Lead Guest Editor:

Yifan Chen
University of Electronic Science and Technology of China, China
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Guest Editors:

Zheng Gong
University of Electronic Science and Technology of China, China
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Xiuzhu Ye
Beijing Institute of Technology, China
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