Evaluating the Performance of Algorithms in Axillary Microwave Imaging towards Improved Breast Cancer Staging

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Oncology

Background:

• Breast cancer is a leading cause of cancer death globally.
• Metastasis to Axillary Lymph Nodes (ALN) is common in advanced breast cancer.
• Current preoperative diagnostics for ALN metastasis lack satisfactory accuracy.

Purpose of the Study:

• To evaluate radar Microwave Imaging (MWI) for detecting breast cancer metastasis in ALNs.
• To assess the performance of various artifact removal and beamformer algorithms in distinct anatomical scenarios.
• To introduce and validate a novel beamformer algorithm, CR-DMAS, for improved ALN detection.

Main Methods:

• Development and analysis of distinct axillary region models.
• Assessment of artifact removal and beamformer algorithm performance.
• Introduction of the Channel-Ranked Delay-Multiply-And-Sum (CR-DMAS) beamformer algorithm.

Main Results:

• CR-DMAS achieved improved Signal-to-Clutter Ratio (up to 3.07 dB) and Signal-to-Mean Ratio (up to 20.78 dB).
• The new algorithm demonstrated a low Location Error of 1.58 mm for single ALN detection.
• CR-DMAS outperformed established beamformers in multiple target detection scenarios.

Conclusions:

• Radar MWI shows promise for detecting ALN metastasis.
• The novel CR-DMAS algorithm offers enhanced performance for ALN detection in MWI.
• This research provides valuable insights into algorithmic performance for axillary MWI.