研究目的
To investigate the feasibility and effectiveness of applying compressive sensing (CS) technique to the holographic microwave imaging for small dielectric object detection.
研究成果
The CS-HMRAI via SB method demonstrated the ability to produce high-quality images and detect small inclusions with significantly fewer sensors and less data collection time than traditional HMRAI and CS-HMRAI via OMP approaches. This method shows potential for fast and cost-effective breast tumor detection, though further clinical validation is needed.
研究不足
The study focused on numerical simulations and experimental validations with tissue-like phantoms. Clinical applications would require further validation with human subjects. The method's effectiveness in highly heterogeneous biological tissues and its sensitivity to noise were not extensively explored.
1：Experimental Design and Method Selection:
The study developed a numerical system consisting of various dielectric models and an imaging processing model to evaluate the proposed CS-HMRAI method. The split Bregman (SB) and orthogonal matching pursuit (OMP) algorithms were applied to HMRAI for the evaluation of small inclusions embedded in dielectric objects.
2：Sample Selection and Data Sources:
Four breast models were developed using published dielectric properties of tissues. These models included skin, fat, gland, and tumors of various sizes and shapes.
3：List of Experimental Equipment and Materials:
The experimental setup included a microwave signal generator (vector network analyzer, VNA), an N-element random sensor array, a signal control and data acquisition unit, and a computer with the matched program.
4：Experimental Procedures and Operational Workflow:
The system was used to generate microwave signals to the object through a transmitter, with detectors measuring the scattered electric field from the object. The object image was reconstructed from the recorded data using the HMRAI method.
5：Data Analysis Methods:
The study compared the performance of CS-HMRAI via SB and OMP with HMRAI in terms of image quality, detection accuracy, and computational time.
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