Article(id=1320381126099710475, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320381108315870143, articleNumber=null, orderNo=null, doi=10.11876/mimt202603002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1779379200000, receivedDateStr=2026-05-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792732991957, onlineDateStr=2026-10-23, pubDate=1781020800000, pubDateStr=2026-06-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792732991957, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792732991957, creator=13701087609, updateTime=1792732991957, updator=13701087609, issue=Issue{id=1320381108315870143, tenantId=1146029695717560320, journalId=1319373289681944729, year='2026', volume='32', issue='3', pageStart='1', pageEnd='132', issueExtLink='null', onlineDate='null', pubDate='1781020800000', pubDateStr='2026-06-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792732987716, creator='13701087609', updateTime=1792746098645, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320436099600728986, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320381108315870143, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320436099600728987, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320381108315870143, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=12, endPage=17, ext={EN=ArticleExt(id=1320381126313619980, articleId=1320381126099710475, tenantId=1146029695717560320, journalId=1319373289681944729, language=EN, title=Sparse array microwave stroke imaging method based on Swin Transformer, columnId=null, journalTitle=Modern Instruments & Medical Treatment, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Portable microwave stroke detection devices often suffer from sparse spatial sampling due to a limited number of antennas. This limitation leads to severe ill-posed and ill-conditioned problems in microwave inverse scattering imaging. To address these technical challenges, this paper proposes a cascaded imaging architecture based on a physics-constrained Swin Transformer, termed deep learning-enhanced truncated singular value decomposition (DL-TSVD) algorithm. By leveraging the global self-attention mechanism of the deep learning network, this architecture achieves high-fidelity mapping from a low-dimensional 8×8 sparse scattering matrix to a 16×16 dense matrix. Subsequently, the dense matrix is fed into the truncated singular value decomposition (TSVD) algorithm to accomplish the high-resolution inversion of the brain's permittivity distribution. In terms of S-parameter fitting accuracy, the complex-domain root mean square error of the predicted matrix is significantly reduced by 67.53% compared with traditional interpolation methods. Additionally, phantom experiments verify the noise-resistant generalization ability of the DL-TSVD architecture under real hardware noise and antenna mutual coupling environments. The structural similarity index measure (SSIM) of the reconstructed images reaches 0.541. The results demonstrate that the proposed method effectively mitigates the ill-conditioned nature of the electromagnetic inverse scattering problem under sparse arrays. It breaks through the resolution bottleneck of spatial sampling without increasing the number of antennas, providing a valuable technical reference for portable microwave stroke monitoring systems.

, authors=Fan Li^a, Zekun Zhang^b, Heng Liu^{b, *}, authorsList=Fan Li, Zekun Zhang, Heng Liu, authorCompany=null, correspAuthors=Heng Liu, authorNote=null, correspAuthorsNote=null, copyrightStatement=All copyrights in 2026 belong to the sponsor of the journal.Not content published by the journal may be reproduced or abridged without authorization., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1320381126594638351, articleId=1320381126099710475, tenantId=1146029695717560320, journalId=1319373289681944729, language=CN, title=基于Swin Transformer的稀疏阵列脑卒中微波成像方法, columnId=1320381126389117454, journalTitle=现代仪器与医疗, columnName=智能医疗器械专栏, runingTitle=null, highlight=null, articleAbstract=

针对便携式微波脑卒中检测设备因天线数量受限导致空间采样稀疏，进而使微波逆散射成像面临严重欠定性与病态性、非迭代算法成像质量差的技术难题，提出一种基于物理先验Swin Transformer的级联成像架构即深度学习增强型截断奇异值分解（deep learning-enhanced truncated singular value decomposition，DL-TSVD）算法。该架构利用深度学习网络的全局自注意力机制，将低维的8×8稀疏散射矩阵高保真映射为16×16稠密矩阵，随后代入截断奇异值分解算法（truncated singular value decomposition，TSVD），完成高分辨率的脑部介电常数分布反演。在S参数拟合精度方面，预测散射矩阵的复数域均方根误差较传统插值法显著降低了67.53%。同时，仿体实验证实了DL-TSVD算法在真实硬件噪声与天线互耦环境下的抗噪泛化能力，重构图像的结构相似性指数（structural similarity index measure，SSIM）达到0.541。实测结果表明该方法在不增加射频硬件的前提下有效缓解了稀疏阵列下电磁逆散射问题的病态性，为便携式微波脑卒中检测设备提供了一定的技术参考。

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