基于低秩先验的快速 RPCA,用于非对比超声微血管成像中的杂波过滤和噪声抑制。

IF 3.8 2区 物理与天体物理 Q1 ACOUSTICS
Xiao Su , Yueyuan Wang , Hanbing Chu , Liyuan Jiang , Yadi Yan , Xiaoyang Qiao , Jianjun Yu , Kaitai Guo , Yujin Zong , Mingxi Wan
{"title":"基于低秩先验的快速 RPCA,用于非对比超声微血管成像中的杂波过滤和噪声抑制。","authors":"Xiao Su ,&nbsp;Yueyuan Wang ,&nbsp;Hanbing Chu ,&nbsp;Liyuan Jiang ,&nbsp;Yadi Yan ,&nbsp;Xiaoyang Qiao ,&nbsp;Jianjun Yu ,&nbsp;Kaitai Guo ,&nbsp;Yujin Zong ,&nbsp;Mingxi Wan","doi":"10.1016/j.ultras.2024.107379","DOIUrl":null,"url":null,"abstract":"<div><p>Accurate and real-time separation of blood signal from clutter and noise signals is a critical step in clinical non-contrast ultrasound microvascular imaging. Despite the widespread adoption of singular value decomposition (SVD) and robust principal component analysis (RPCA) for clutter filtering and noise suppression, the SVD’s sensitivity to threshold selection, along with the RPCA’s limitations in undersampling conditions and heavy computational burden often result in suboptimal performance in complex clinical applications. To address those challenges, this study presents a novel low-rank prior-based fast RPCA (LP-fRPCA) approach to enhance the adaptability and robustness of clutter filtering and noise suppression with reduced computational cost. A low-rank prior constraint is integrated into the non-convex RPCA model to achieve a robust and efficient approximation of clutter subspace, while an accelerated alternating projection iterative algorithm is developed to improve convergence speed and computational efficiency. The performance of the LP-fRPCA method was evaluated against SVD with a tissue/blood threshold (SVD1), SVD with both tissue/blood and blood/noise thresholds (SVD2), and the classical RPCA based on the alternating direction method of multipliers algorithm through phantom and <em>in vivo</em> non-contrast experiments on rabbit kidneys. In the slow flow phantom experiment of 0.2 mm/s, LP-fRPCA achieved an average increase in contrast ratio (CR) of 10.68 dB, 9.37 dB, and 8.66 dB compared to SVD1, SVD2, and RPCA, respectively. In the <em>in vivo</em> rabbit kidney experiment, the power Doppler results demonstrate that the LP-fRPCA method achieved a superior balance in the trade-off between insufficient clutter filtering and excessive suppression of blood flow. Additionally, LP-fRPCA significantly reduced the runtime of RPCA by up to 94-fold. Consequently, the LP-fRPCA method promises to be a potential tool for clinical non-contrast ultrasound microvascular imaging.</p></div>","PeriodicalId":23522,"journal":{"name":"Ultrasonics","volume":"142 ","pages":"Article 107379"},"PeriodicalIF":3.8000,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Low-rank prior-based Fast-RPCA for clutter filtering and noise suppression in non-contrast ultrasound microvascular imaging\",\"authors\":\"Xiao Su ,&nbsp;Yueyuan Wang ,&nbsp;Hanbing Chu ,&nbsp;Liyuan Jiang ,&nbsp;Yadi Yan ,&nbsp;Xiaoyang Qiao ,&nbsp;Jianjun Yu ,&nbsp;Kaitai Guo ,&nbsp;Yujin Zong ,&nbsp;Mingxi Wan\",\"doi\":\"10.1016/j.ultras.2024.107379\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Accurate and real-time separation of blood signal from clutter and noise signals is a critical step in clinical non-contrast ultrasound microvascular imaging. Despite the widespread adoption of singular value decomposition (SVD) and robust principal component analysis (RPCA) for clutter filtering and noise suppression, the SVD’s sensitivity to threshold selection, along with the RPCA’s limitations in undersampling conditions and heavy computational burden often result in suboptimal performance in complex clinical applications. To address those challenges, this study presents a novel low-rank prior-based fast RPCA (LP-fRPCA) approach to enhance the adaptability and robustness of clutter filtering and noise suppression with reduced computational cost. A low-rank prior constraint is integrated into the non-convex RPCA model to achieve a robust and efficient approximation of clutter subspace, while an accelerated alternating projection iterative algorithm is developed to improve convergence speed and computational efficiency. The performance of the LP-fRPCA method was evaluated against SVD with a tissue/blood threshold (SVD1), SVD with both tissue/blood and blood/noise thresholds (SVD2), and the classical RPCA based on the alternating direction method of multipliers algorithm through phantom and <em>in vivo</em> non-contrast experiments on rabbit kidneys. In the slow flow phantom experiment of 0.2 mm/s, LP-fRPCA achieved an average increase in contrast ratio (CR) of 10.68 dB, 9.37 dB, and 8.66 dB compared to SVD1, SVD2, and RPCA, respectively. In the <em>in vivo</em> rabbit kidney experiment, the power Doppler results demonstrate that the LP-fRPCA method achieved a superior balance in the trade-off between insufficient clutter filtering and excessive suppression of blood flow. Additionally, LP-fRPCA significantly reduced the runtime of RPCA by up to 94-fold. Consequently, the LP-fRPCA method promises to be a potential tool for clinical non-contrast ultrasound microvascular imaging.</p></div>\",\"PeriodicalId\":23522,\"journal\":{\"name\":\"Ultrasonics\",\"volume\":\"142 \",\"pages\":\"Article 107379\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ultrasonics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0041624X24001410\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ACOUSTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ultrasonics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0041624X24001410","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0

摘要

从杂波和噪声信号中准确、实时地分离血液信号是临床非对比超声微血管成像的关键步骤。尽管奇异值分解(SVD)和鲁棒性主成分分析(RPCA)被广泛应用于杂波过滤和噪声抑制,但 SVD 对阈值选择的敏感性,以及 RPCA 在欠采样条件下的局限性和沉重的计算负担,往往导致其在复杂临床应用中的性能不尽如人意。为了应对这些挑战,本研究提出了一种新颖的基于低阶先验的快速 RPCA(LP-fRPCA)方法,以提高杂波过滤和噪声抑制的适应性和鲁棒性,同时降低计算成本。低阶先验约束被集成到非凸 RPCA 模型中,以实现杂波子空间的稳健高效逼近,同时开发了一种加速交替投影迭代算法,以提高收敛速度和计算效率。通过对兔子肾脏进行幻影实验和活体非对比实验,评估了 LP-fRPCA 方法与组织/血液阈值的 SVD(SVD1)、组织/血液和血液/噪声阈值的 SVD(SVD2)以及基于乘数交替方向法算法的经典 RPCA 的性能。在 0.2 mm/s 的慢流模型实验中,LP-fRPCA 与 SVD1、SVD2 和 RPCA 相比,对比度(CR)分别平均提高了 10.68 dB、9.37 dB 和 8.66 dB。在活体兔肾实验中,功率多普勒结果表明,LP-fRPCA 方法在滤波杂波不足和过度抑制血流之间取得了较好的平衡。此外,LP-fRPCA 将 RPCA 的运行时间大幅缩短了 94 倍。因此,LP-fRPCA 方法有望成为临床非对比超声微血管成像的潜在工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low-rank prior-based Fast-RPCA for clutter filtering and noise suppression in non-contrast ultrasound microvascular imaging

Accurate and real-time separation of blood signal from clutter and noise signals is a critical step in clinical non-contrast ultrasound microvascular imaging. Despite the widespread adoption of singular value decomposition (SVD) and robust principal component analysis (RPCA) for clutter filtering and noise suppression, the SVD’s sensitivity to threshold selection, along with the RPCA’s limitations in undersampling conditions and heavy computational burden often result in suboptimal performance in complex clinical applications. To address those challenges, this study presents a novel low-rank prior-based fast RPCA (LP-fRPCA) approach to enhance the adaptability and robustness of clutter filtering and noise suppression with reduced computational cost. A low-rank prior constraint is integrated into the non-convex RPCA model to achieve a robust and efficient approximation of clutter subspace, while an accelerated alternating projection iterative algorithm is developed to improve convergence speed and computational efficiency. The performance of the LP-fRPCA method was evaluated against SVD with a tissue/blood threshold (SVD1), SVD with both tissue/blood and blood/noise thresholds (SVD2), and the classical RPCA based on the alternating direction method of multipliers algorithm through phantom and in vivo non-contrast experiments on rabbit kidneys. In the slow flow phantom experiment of 0.2 mm/s, LP-fRPCA achieved an average increase in contrast ratio (CR) of 10.68 dB, 9.37 dB, and 8.66 dB compared to SVD1, SVD2, and RPCA, respectively. In the in vivo rabbit kidney experiment, the power Doppler results demonstrate that the LP-fRPCA method achieved a superior balance in the trade-off between insufficient clutter filtering and excessive suppression of blood flow. Additionally, LP-fRPCA significantly reduced the runtime of RPCA by up to 94-fold. Consequently, the LP-fRPCA method promises to be a potential tool for clinical non-contrast ultrasound microvascular imaging.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Ultrasonics
Ultrasonics 医学-核医学
CiteScore
7.60
自引率
19.00%
发文量
186
审稿时长
3.9 months
期刊介绍: Ultrasonics is the only internationally established journal which covers the entire field of ultrasound research and technology and all its many applications. Ultrasonics contains a variety of sections to keep readers fully informed and up-to-date on the whole spectrum of research and development throughout the world. Ultrasonics publishes papers of exceptional quality and of relevance to both academia and industry. Manuscripts in which ultrasonics is a central issue and not simply an incidental tool or minor issue, are welcomed. As well as top quality original research papers and review articles by world renowned experts, Ultrasonics also regularly features short communications, a calendar of forthcoming events and special issues dedicated to topical subjects.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信