Wu Ji, Yang Fan, Zheng Jinchuan, Nguyen Hung T, Chai Rifai
School of Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.
Sichuan Canyearn Medical Equipment Co., Ltd., Chengdu 610000, China.
Sensors (Basel). 2025 Feb 26;25(5):1429. doi: 10.3390/s25051429.
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.
微波断层扫描是一种很有前景的乳腺成像非侵入性技术，但在噪声环境下进行精确重建仍然具有挑战性。我们提出了一种基于自适应子空间的两步迭代收缩/阈值化（S-TwIST）算法，该算法通过两项关键创新提高了重建精度：一种用于提取确定性对比度源的奇异值分解（SVD）方法，以及一种用于最优奇异值选择的自适应策略。与仅依赖二次入射场的传统DBIM方法不同，S-TwIST纳入了确定性感应电流，以实现更精确的总场近似。使用合成的“Austria”剖面和从UWCEM存储库导出的45个数字乳腺模型对该算法的性能进行了验证。结果表明，该算法在不同噪声水平（0-20 dB SNR）下具有强大的重建能力，在乳腺组织重建中无需先验噪声水平知识即可实现0.4847%的平均相对误差。该算法成功地恢复了复杂的组织结构和密度分布，显示出在临床乳腺成像应用中的潜力。
