心脏应变成像的时空贝叶斯正则化:模拟和体内结果

Rashid Al Mukaddim;Nirvedh H. Meshram;Ashley M. Weichmann;Carol C. Mitchell;Tomy Varghese
{"title":"心脏应变成像的时空贝叶斯正则化:模拟和体内结果","authors":"Rashid Al Mukaddim;Nirvedh H. Meshram;Ashley M. Weichmann;Carol C. Mitchell;Tomy Varghese","doi":"10.1109/OJUFFC.2021.3130021","DOIUrl":null,"url":null,"abstract":"Cardiac strain imaging (CSI) plays a critical role in the detection of myocardial motion abnormalities. Displacement estimation is an important processing step to ensure the accuracy and precision of derived strain tensors. In this paper, we propose and implement Spatiotemporal Bayesian regularization (STBR) algorithms for two-dimensional (2-D) normalized cross-correlation (NCC) based multi-level block matching along with incorporation into a Lagrangian cardiac strain estimation framework. Assuming smooth temporal variation over a short span of time, the proposed STBR algorithm performs displacement estimation using at least four consecutive ultrasound radio-frequency (RF) frames by iteratively regularizing 2-D NCC matrices using information from a local spatiotemporal neighborhood in a Bayesian sense. Two STBR schemes are proposed to construct Bayesian likelihood functions termed as Spatial then Temporal Bayesian (STBR-1) and simultaneous Spatiotemporal Bayesian (STBR-2). Radial and longitudinal strain estimated from a finite-element-analysis (FEA) model of realistic canine myocardial deformation were utilized to quantify strain bias, normalized strain error and total temporal relative error (TTR). Statistical analysis with one-way analysis of variance (ANOVA) showed that all Bayesian regularization methods significantly outperform NCC with lower bias and errors (\n<inline-formula> <tex-math>${p} &lt; $ </tex-math></inline-formula>\n \n<italic>0.001</i>\n). However, there was no significant difference among Bayesian methods. For example, mean longitudinal TTR for NCC, SBR, STBR-1 and STBR-2 were 25.41%, 9.27%, 10.38% and 10.13% respectively An \n<italic>in vivo</i>\n feasibility study using RF data from ten healthy mice hearts were used to compare the elastographic signal-to-noise ratio (\n<inline-formula> <tex-math>${\\mathrm {SNR}}_{\\mathrm {e}}$ </tex-math></inline-formula>\n) calculated using stochastic analysis. STBR-2 had the highest expected SNR\n<sub>e</sub>\n both for radial and longitudinal strain. The mean expected SNR\n<sub>e</sub>\n values for accumulated radial strain for NCC, SBR, STBR-1 and STBR-2 were 5.03, 9.43, 9.42 and 10.58, respectively. Overall results suggest that STBR improves CSI \n<italic>in vivo</i>\n.","PeriodicalId":73301,"journal":{"name":"IEEE open journal of ultrasonics, ferroelectrics, and frequency control","volume":"1 ","pages":"21-36"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9292640/9377491/09623563.pdf","citationCount":"6","resultStr":"{\"title\":\"Spatiotemporal Bayesian Regularization for Cardiac Strain Imaging: Simulation and In Vivo Results\",\"authors\":\"Rashid Al Mukaddim;Nirvedh H. Meshram;Ashley M. Weichmann;Carol C. Mitchell;Tomy Varghese\",\"doi\":\"10.1109/OJUFFC.2021.3130021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cardiac strain imaging (CSI) plays a critical role in the detection of myocardial motion abnormalities. Displacement estimation is an important processing step to ensure the accuracy and precision of derived strain tensors. In this paper, we propose and implement Spatiotemporal Bayesian regularization (STBR) algorithms for two-dimensional (2-D) normalized cross-correlation (NCC) based multi-level block matching along with incorporation into a Lagrangian cardiac strain estimation framework. Assuming smooth temporal variation over a short span of time, the proposed STBR algorithm performs displacement estimation using at least four consecutive ultrasound radio-frequency (RF) frames by iteratively regularizing 2-D NCC matrices using information from a local spatiotemporal neighborhood in a Bayesian sense. Two STBR schemes are proposed to construct Bayesian likelihood functions termed as Spatial then Temporal Bayesian (STBR-1) and simultaneous Spatiotemporal Bayesian (STBR-2). Radial and longitudinal strain estimated from a finite-element-analysis (FEA) model of realistic canine myocardial deformation were utilized to quantify strain bias, normalized strain error and total temporal relative error (TTR). Statistical analysis with one-way analysis of variance (ANOVA) showed that all Bayesian regularization methods significantly outperform NCC with lower bias and errors (\\n<inline-formula> <tex-math>${p} &lt; $ </tex-math></inline-formula>\\n \\n<italic>0.001</i>\\n). However, there was no significant difference among Bayesian methods. For example, mean longitudinal TTR for NCC, SBR, STBR-1 and STBR-2 were 25.41%, 9.27%, 10.38% and 10.13% respectively An \\n<italic>in vivo</i>\\n feasibility study using RF data from ten healthy mice hearts were used to compare the elastographic signal-to-noise ratio (\\n<inline-formula> <tex-math>${\\\\mathrm {SNR}}_{\\\\mathrm {e}}$ </tex-math></inline-formula>\\n) calculated using stochastic analysis. STBR-2 had the highest expected SNR\\n<sub>e</sub>\\n both for radial and longitudinal strain. The mean expected SNR\\n<sub>e</sub>\\n values for accumulated radial strain for NCC, SBR, STBR-1 and STBR-2 were 5.03, 9.43, 9.42 and 10.58, respectively. Overall results suggest that STBR improves CSI \\n<italic>in vivo</i>\\n.\",\"PeriodicalId\":73301,\"journal\":{\"name\":\"IEEE open journal of ultrasonics, ferroelectrics, and frequency control\",\"volume\":\"1 \",\"pages\":\"21-36\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/iel7/9292640/9377491/09623563.pdf\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE open journal of ultrasonics, ferroelectrics, and frequency control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/9623563/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE open journal of ultrasonics, ferroelectrics, and frequency control","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/9623563/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

心脏应变成像(CSI)在检测心肌运动异常中起着至关重要的作用。位移估计是保证应变张量导出精度的重要处理步骤。在本文中,我们提出并实现了时空贝叶斯正则化(STBR)算法,用于二维(2-D)归一化互相关(NCC)的多级块匹配,并将其纳入拉格朗日心脏应变估计框架。假设在短时间内平滑的时间变化,提出的STBR算法使用至少四个连续的超声射频(RF)帧,通过使用贝叶斯意义上的局部时空邻域信息迭代正则化二维NCC矩阵来执行位移估计。提出了两种构造贝叶斯似然函数的STBR方案,即时空贝叶斯(STBR-1)和同步时空贝叶斯(STBR-2)。利用真实犬心肌变形有限元分析(FEA)模型估算径向和纵向应变,量化应变偏差、归一化应变误差和总时间相对误差(TTR)。单因素方差分析(ANOVA)的统计分析表明,所有贝叶斯正则化方法都显著优于NCC,偏差和误差更低(${p} <0.001美元)。然而,贝叶斯方法之间没有显著差异。例如,NCC、SBR、STBR-1和STBR-2的平均纵向TTR分别为25.41%、9.27%、10.38%和10.13%。采用10只健康小鼠心脏的射频数据进行体内可行性研究,比较随机分析计算的弹性图信噪比(${\ mathm {SNR}}_{\ mathm {e}}$)。STBR-2的径向应变和纵向应变的期望信噪比均最高。NCC、SBR、STBR-1和STBR-2的累积径向应变信噪比均值分别为5.03、9.43、9.42和10.58。总体结果表明,STBR改善了体内CSI。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatiotemporal Bayesian Regularization for Cardiac Strain Imaging: Simulation and In Vivo Results
Cardiac strain imaging (CSI) plays a critical role in the detection of myocardial motion abnormalities. Displacement estimation is an important processing step to ensure the accuracy and precision of derived strain tensors. In this paper, we propose and implement Spatiotemporal Bayesian regularization (STBR) algorithms for two-dimensional (2-D) normalized cross-correlation (NCC) based multi-level block matching along with incorporation into a Lagrangian cardiac strain estimation framework. Assuming smooth temporal variation over a short span of time, the proposed STBR algorithm performs displacement estimation using at least four consecutive ultrasound radio-frequency (RF) frames by iteratively regularizing 2-D NCC matrices using information from a local spatiotemporal neighborhood in a Bayesian sense. Two STBR schemes are proposed to construct Bayesian likelihood functions termed as Spatial then Temporal Bayesian (STBR-1) and simultaneous Spatiotemporal Bayesian (STBR-2). Radial and longitudinal strain estimated from a finite-element-analysis (FEA) model of realistic canine myocardial deformation were utilized to quantify strain bias, normalized strain error and total temporal relative error (TTR). Statistical analysis with one-way analysis of variance (ANOVA) showed that all Bayesian regularization methods significantly outperform NCC with lower bias and errors ( ${p} < $ 0.001 ). However, there was no significant difference among Bayesian methods. For example, mean longitudinal TTR for NCC, SBR, STBR-1 and STBR-2 were 25.41%, 9.27%, 10.38% and 10.13% respectively An in vivo feasibility study using RF data from ten healthy mice hearts were used to compare the elastographic signal-to-noise ratio ( ${\mathrm {SNR}}_{\mathrm {e}}$ ) calculated using stochastic analysis. STBR-2 had the highest expected SNR e both for radial and longitudinal strain. The mean expected SNR e values for accumulated radial strain for NCC, SBR, STBR-1 and STBR-2 were 5.03, 9.43, 9.42 and 10.58, respectively. Overall results suggest that STBR improves CSI in vivo .
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
自引率
0.00%
发文量
0
×
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