Subspace-Based Two-Step Iterative Shrinkage/Thresholding Algorithm for Microwave Tomography Breast Imaging

TL;DR

This paper introduces a new algorithm for improving microwave tomography breast imaging, especially in noisy conditions.

Contribution

The novel S-TwIST algorithm uses adaptive subspace methods and deterministic contrast sources for better reconstruction accuracy.

Findings

The algorithm achieves average relative errors of 0.4847% in breast tissue reconstruction.

It performs well across noise levels from 0 to 20 dB SNR without prior noise knowledge.

It successfully recovers complex tissue structures and density distributions.

Abstract

Microwave tomography serves as a promising non-invasive technique for breast imaging, yet accurate reconstruction in noisy environments remains challenging. We propose an adaptive subspace-based two-step iterative shrinkage/thresholding (S-TwIST) algorithm that enhances reconstruction accuracy through two key innovations: a singular value decomposition (SVD) approach for extracting deterministic contrast sources, and an adaptive strategy for optimal singular value selection. Unlike conventional DBIM methods that rely solely on secondary incident fields, S-TwIST incorporates deterministic induced currents to achieve more accurate total field approximation. The algorithm’s performance is validated using both synthetic “Austria” profiles and 45 digital breast phantoms derived from the UWCEM repository. The results demonstrate robust reconstruction capabilities across varying noise levels (0–20 dB SNR), achieving average relative errors of 0.4847% in breast tissue reconstruction without requiring prior noise level knowledge. The algorithm successfully recovers complex tissue structures and density distributions, showing potential for clinical breast imaging applications.

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