Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Signal Processing

Background:

• Confocal Microwave Imaging (CMI) is under development for early breast cancer detection.
• Image reconstruction algorithms are crucial for CMI system efficacy.
• Previous algorithm evaluations used models, not clinical patient data.

Purpose of the Study:

• To evaluate and compare the performance of various CMI image reconstruction algorithms using clinical patient data.
• To assess the ability of algorithms to detect and localize abnormalities in breast cancer patients.
• To determine the imaging quality of different algorithms in a clinical context.

Main Methods:

• Six imaging algorithms, including data-independent and data-adaptive types, were applied to reconstruct 3D images.
• Data was obtained from a small-scale patient study at the University of Calgary.
• Reconstructed images were compared against clinical reports and evaluated using quality metrics.

Main Results:

• The Delay-and-Sum (DAS) and Delay-Multiply-and-Sum (DMAS) algorithms showed results consistent with clinical information.
• DMAS algorithm demonstrated superior image quality compared to all other evaluated algorithms.
• Algorithm performance was assessed for abnormality detection and localization in five clinical patients.

Conclusions:

• The Delay-Multiply-and-Sum (DMAS) algorithm is a promising approach for Confocal Microwave Imaging in breast cancer detection.
• Evaluating algorithms with clinical patient data is essential for assessing real-world performance.
• DMAS offers improved imaging quality, potentially enhancing early breast cancer diagnosis.