Deep denoising approach to improve shear wave phase velocity map reconstruction in ultrasound elastography

IF 3.2 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Medical physics Pub Date : 2024-12-23 DOI:10.1002/mp.17581
Phidakordor Sahshong, Akash Chandra, Karla P. Mercado-Shekhar, Manish Bhatt
{"title":"Deep denoising approach to improve shear wave phase velocity map reconstruction in ultrasound elastography","authors":"Phidakordor Sahshong,&nbsp;Akash Chandra,&nbsp;Karla P. Mercado-Shekhar,&nbsp;Manish Bhatt","doi":"10.1002/mp.17581","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Measurement noise often leads to inaccurate shear wave phase velocity estimation in ultrasound shear wave elastography. Filtering techniques are commonly used for denoising the shear wavefields. However, these filters are often not sufficient, especially in fatty tissues where the signal-to-noise ratio (SNR) can be very low.</p>\n </section>\n \n <section>\n \n <h3> Purpose</h3>\n \n <p>The purpose of this study is to develop a deep learning approach for denoising shear wavefields in ultrasound shear wave elastography. This may lead to improved reconstruction of shear wave phase velocity image maps.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>The study addresses noise by transforming particle velocity data into a time-frequency representation. A neural network with encoder and decoder convolutional blocks effectively decomposes the input and extracts the signal of interest, improving the SNR in high-noise scenarios. The network is trained on simulated phantoms with elasticity values ranging from 3  to 60 kPa. A total of 1 85 570 samples with 80%–20<span></span><math>\n <semantics>\n <mo>%</mo>\n <annotation>$\\%$</annotation>\n </semantics></math> split were used for training and validation. The approach is tested on experimental phantom and ex-vivo goat liver tissue data. Performance was compared with the traditional filtering methods such as bandpass, median, and wavelet filtering. Kruskal–Wallis one-way analysis of variance was performed to check statistical significance. Multiple comparisons were performed using the Mann–Whitney U test and Holm–Bonferroni adjustment of <span></span><math>\n <semantics>\n <mrow>\n <mi>p</mi>\n <mo>−</mo>\n <mi>values</mi>\n </mrow>\n <annotation>$p-{\\rm values}$</annotation>\n </semantics></math>.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The results are evaluated using SNR and the percentage of pixels that can be reconstructed in the phase velocity maps. The SNR levels in experimental data improved from –2 to 9.9 dB levels to 15.6 to 30.3 dB levels. Kruskal–Wallis one-way analysis showed statistical significance (<span></span><math>\n <semantics>\n <mrow>\n <mi>p</mi>\n <mo>&lt;</mo>\n <mn>0.05</mn>\n </mrow>\n <annotation>$p&lt;0.05$</annotation>\n </semantics></math>). Multiple comparisons with <i>p</i>-value corrections also showed statistically significant improvement when compared to the bandpass and wavelet filtering scheme (<span></span><math>\n <semantics>\n <mrow>\n <mi>p</mi>\n <mo>&lt;</mo>\n <mn>0.05</mn>\n </mrow>\n <annotation>$p&lt;0.05$</annotation>\n </semantics></math>). Smoother phase velocity maps were reconstructed after denoising. The coefficient of variation is less than <span></span><math>\n <semantics>\n <mrow>\n <mn>5</mn>\n <mo>%</mo>\n </mrow>\n <annotation>$5\\%$</annotation>\n </semantics></math> in CIRS phantom and less than <span></span><math>\n <semantics>\n <mrow>\n <mn>18</mn>\n <mo>%</mo>\n </mrow>\n <annotation>$18\\%$</annotation>\n </semantics></math> in ex-vivo goat liver tissue.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>The proposed approach demonstrates improvement in shear wave phase velocity image map reconstruction and holds promise that deep learning methods can be effectively utilized to extract true shear wave signal from measured noisy data.</p>\n </section>\n </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1481-1499"},"PeriodicalIF":3.2000,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical physics","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/mp.17581","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Background

Measurement noise often leads to inaccurate shear wave phase velocity estimation in ultrasound shear wave elastography. Filtering techniques are commonly used for denoising the shear wavefields. However, these filters are often not sufficient, especially in fatty tissues where the signal-to-noise ratio (SNR) can be very low.

Purpose

The purpose of this study is to develop a deep learning approach for denoising shear wavefields in ultrasound shear wave elastography. This may lead to improved reconstruction of shear wave phase velocity image maps.

Methods

The study addresses noise by transforming particle velocity data into a time-frequency representation. A neural network with encoder and decoder convolutional blocks effectively decomposes the input and extracts the signal of interest, improving the SNR in high-noise scenarios. The network is trained on simulated phantoms with elasticity values ranging from 3  to 60 kPa. A total of 1 85 570 samples with 80%–20 % $\%$ split were used for training and validation. The approach is tested on experimental phantom and ex-vivo goat liver tissue data. Performance was compared with the traditional filtering methods such as bandpass, median, and wavelet filtering. Kruskal–Wallis one-way analysis of variance was performed to check statistical significance. Multiple comparisons were performed using the Mann–Whitney U test and Holm–Bonferroni adjustment of p values $p-{\rm values}$ .

Results

The results are evaluated using SNR and the percentage of pixels that can be reconstructed in the phase velocity maps. The SNR levels in experimental data improved from –2 to 9.9 dB levels to 15.6 to 30.3 dB levels. Kruskal–Wallis one-way analysis showed statistical significance ( p < 0.05 $p<0.05$ ). Multiple comparisons with p-value corrections also showed statistically significant improvement when compared to the bandpass and wavelet filtering scheme ( p < 0.05 $p<0.05$ ). Smoother phase velocity maps were reconstructed after denoising. The coefficient of variation is less than 5 % $5\%$ in CIRS phantom and less than 18 % $18\%$ in ex-vivo goat liver tissue.

Conclusions

The proposed approach demonstrates improvement in shear wave phase velocity image map reconstruction and holds promise that deep learning methods can be effectively utilized to extract true shear wave signal from measured noisy data.

超声弹性成像中改进横波相速度图重建的深度去噪方法。
背景:在超声横波弹性成像中,测量噪声经常导致横波相速度估计不准确。滤波技术通常用于剪切波场的去噪。然而,这些滤波器往往是不够的,特别是在脂肪组织的信噪比(SNR)可能非常低。目的:本研究的目的是发展一种深度学习方法来去除超声剪切波弹性成像中的剪切波场。这可能会改善横波相速度图像图的重建。方法:研究通过将粒子速度数据转换为时频表示来解决噪声问题。编解码器卷积块的神经网络有效地分解输入并提取感兴趣的信号,提高了高噪声场景下的信噪比。该网络在弹性值从3到60 kPa的模拟幻影上进行训练。使用80%- 20% $\%$分割的1 85 570个样本进行训练和验证。该方法在实验性幻体和离体山羊肝组织数据上进行了测试。并与传统滤波方法如带通滤波、中值滤波和小波滤波进行了性能比较。采用Kruskal-Wallis单因素方差分析检验统计学显著性。使用Mann-Whitney U检验和Holm-Bonferroni调整p-值$p-{\rm值}$进行多次比较。结果:使用信噪比和相速度图中可重建像素的百分比对结果进行评估。实验数据的信噪比水平从-2 ~ 9.9 dB水平提高到15.6 ~ 30.3 dB水平。Kruskal-Wallis单因素分析差异有统计学意义(p 0.05 $p)。与带通和小波滤波方案相比,p值校正的多重比较也显示出统计学上显著的改善(p 0.05 $p)。去噪后重建出更平滑的相速度图。变异系数在CIRS模型中小于5%,在离体山羊肝组织中小于18%。结论:本文提出的方法改善了横波相速度图像图重建,并有望有效利用深度学习方法从实测噪声数据中提取真实横波信号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Medical physics
Medical physics 医学-核医学
CiteScore
6.80
自引率
15.80%
发文量
660
审稿时长
1.7 months
期刊介绍: Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.
×
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