Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Ultrasound II: Endoscopic Ultrasound and FibroScan

Ultrasound II: Endoscopic Ultrasound and FibroScan

Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
Endoscopic Ultrasound (EUS):

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

Lagrangian deformation tracking for strain imaging.

Progress in biomedical engineering (Bristol, England)·2026

Same author

Geometry aware neural radiance fields for freehand ultrasound reconstruction.

Biomedical physics & engineering express·2026

Same author

3D Freehand Ultrasound Reconstruction of the Carotid Artery.

Ultrasound in medicine & biology·2026

Same author

Editorial Expression of Concern: Regulation of Dipeptidyl Peptidase IV in the Post-stroke Rat Brain and In Vitro Ischemia: Implications for Chemokine-Mediated Neural Progenitor Cell Migration and Angiogenesis.

Molecular neurobiology·2026

Same author

Introducing ARONG, A 3D Reconstruction Method for Highly Deformed Histology.

Journal of imaging informatics in medicine·2026

Same author

Comparison of 2D and 3D carotid plaque analysis and longitudinal <i>in vivo</i> ultrasound registration using 3D histology.

Journal of medical imaging (Bellingham, Wash.)·2026

Search research articles

相关实验视频

Updated: Feb 22, 2026

Manufacturing Abdominal Aorta Hydrogel Tissue-Mimicking Phantoms for Ultrasound Elastography Validation

Manufacturing Abdominal Aorta Hydrogel Tissue-Mimicking Phantoms for Ultrasound Elastography Validation

Published on: September 19, 2018

自主监督的超声波B模式应变弹性学使用SMURF.

Zhiwei Zhang¹, Maxwell J Kiernan², Carol C Mitchell³

¹Department of Electrical and Computer Engineering, UW-Madison, United States.

|February 20, 2026

概括

这项研究引入了一种新的无监督深度学习方法SMURF,用于使用B模式图像的超声波应变弹性学 (USE). 在临床应用中,SMURF准确地估计了横向位移和应变,为临床应用提供了更快的处理速度.

关键词:

深度学习是一种深度学习.拉格兰治压力成像技术的拉格兰治压力成像光学流的光学流量超声波应变弹性学.

更多相关视频

Monitoring the Wall Mechanics During Stent Deployment in a Vessel

Monitoring the Wall Mechanics During Stent Deployment in a Vessel

Published on: May 8, 2012

High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart

High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart

Published on: July 9, 2010

相关实验视频

Last Updated: Feb 22, 2026

Manufacturing Abdominal Aorta Hydrogel Tissue-Mimicking Phantoms for Ultrasound Elastography Validation

Manufacturing Abdominal Aorta Hydrogel Tissue-Mimicking Phantoms for Ultrasound Elastography Validation

Published on: September 19, 2018

Monitoring the Wall Mechanics During Stent Deployment in a Vessel

Monitoring the Wall Mechanics During Stent Deployment in a Vessel

Published on: May 8, 2012

High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart

High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart

Published on: July 9, 2010

科学领域:

医学成像医学成像
生物医学工程生物医学工程
机器学习机器学习

背景情况:

超声波应变弹性图 (USE) 对组织特征学至关重要,但横向位移估计仍然具有挑战性.
临床系统通常只提供B模式图像,限制了现有的USE方法的应用.
无监督深度学习网络 (DLN) 需要大量的数据集,B模式数据可以解决临床翻译的问题.

研究的目的:

探索使用自学多无监督循环全对场变换 (SMURF) 进行超声波应变弹性学 (USE).
在B模式图像循环上使用无监督深度学习来估计轴向和侧向应变张力元件.
评估SMURF的性能与传统的体内成像方法相比.

主要方法:

重新训练RAFT网络,对模拟和实验B模式数据集进行基线监督.
在实验B模式和体内数据集上应用SMURF无监督培训.
使用四维 (4D) 成本体积来进行位移和应变估计.

主要成果:

微调的RAFT模型与无监督的SMURF在位移和应变估计方面取得了与传统方法相比的准确性和精度,特别是在横向方向.
基于GPU的拉格朗 Carotid Strain Imaging (LCSI) 方法相比,SMURF显示了显著更快的处理时间,改进了78%.
在临床研究中,该方法显示了实时拉格朗日USE的潜力.

结论:

无监督深度学习技术,如SMURF,对B模式数据集的USE有效.
在USE中,SMURF提供了一个有前途的解决方案,用于准确和高效的横向位移和应变估计.
这种方法促进了USE的更广泛的临床应用,可能使实时成像成为可能.