Improving Liver Ultrasound Image Quality in Difficult-to-Image Patients

提高难以成像患者的肝脏超声图像质量

基本信息

批准号：
10634660
负责人：
Jeremy Dahl
金额：
$ 55.04万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-15 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10634660
关键词：
Abdomen Acoustics Affect American Architecture Area Attenuated Cardiac Cirrhosis Clinical Clutterings Compensation Computer software Connective Tissue Data Development Diffuse Disease Fatty acid glycerol esters Goals Healthcare Healthcare Systems Heterogeneity Image Imaging technology Lesion Liver Liver diseases Machine Learning Magnetic Resonance Imaging Maps Medical Imaging Methodology Methods Modeling Noise Obesity Output Overweight Patient imaging Patients Performance Phase Physiologic pulse Pilot Projects Population Prevalence Primary carcinoma of the liver cells Resolution Risk Factors Signal Transduction Source Speed Subcutaneous Tissue Surveys System Techniques Technology Testing Thick Thyroid Gland Time Tissues Ultrasonography United States Visualization Weight clinical imaging elastography epidemiology study fetal high body mass index imaging system improved in vivo liver imaging neural network novel obese patients obese person patient population prototype radio frequency simulation sound subcutaneous ultrasound

项目摘要

ABSTRACT The prevalence of obesity in the United States has risen to record levels over the past 40 years, putting strain on the healthcare system and creating difficult challenges for medical imaging. We propose to overcome the challenges that obesity poses to ultrasound imaging by (1) developing novel image-quality improvement techniques, and (2) implementing them on pulse-echo ultrasound imaging systems to yield high-quality images of the liver. Ultrasound imaging is uniquely affected by the presence of additional connective tissue and thick subcutaneous fat layers in overweight and obese patients; these additional subcutaneous layers greatly exacerbate reverberation and phase-aberration of the acoustic wave, leading to high levels of clutter, degraded resolution, and overall poor-quality ultrasound images. Our proposed methods will determine the local speed-of-sound in abdominal tissue layers and use this information to accomplish distributed phase-aberration correction. We also apply machine learning techniques to model and suppress the effects of reverberation clutter and speckle noise. The combination of these techniques is expected to achieve significant improvements in liver image quality. These image-quality improvement methods will be implemented on a real-time ultrasound scanner and will be evaluated in clinical imaging tasks of overweight and obese patients undergoing ultrasound surveillance of hepatocellular carcinoma. Successful development of the proposed technology will not only enable high-quality ultrasound imaging of the liver in otherwise difficult-to-image overweight and obese patients, but also facilitate improved image quality across nearly all ultrasound imaging applications, for all populations.

抽象的过去 40 年来，美国的肥胖率已升至创纪录水平，给医疗保健系统带来压力，并给医学成像带来艰巨的挑战。我们建议通过以下方式克服肥胖对超声成像带来的挑战：（1）开发新颖的图像质量改进技术，以及（2）在脉冲回波上实现它们超声成像系统可产生高质量的肝脏图像。超声成像受到额外结缔组织和厚膜的存在的独特影响。超重和肥胖患者的皮下脂肪层；这些额外的皮下层极大地加剧了声波的混响和相位偏差，导致高混乱程度、分辨率下降和超声图像整体质量差。我们提出的方法将确定腹部组织层的局部声速并使用它信息来完成分布式相位像差校正。我们也应用机器学习建模和抑制混响杂波和散斑噪声影响的技术。这些技术的结合有望实现肝脏的显着改善图像质量。这些图像质量改进方法将实时实施超声扫描仪并将在超重和肥胖的临床成像任务中进行评估接受肝细胞癌超声监测的患者。所提出的技术的成功开发不仅能够实现高质量的超声对难以成像的超重和肥胖患者进行肝脏成像，而且促进几乎所有超声成像应用的图像质量的提高人口。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Distributed Aberration Correction Techniques Based on Tomographic Sound Speed Estimates.

基于断层扫描声速估计的分布式像差校正技术。

DOI：
发表时间：
2022-05
期刊：
影响因子：
0
作者：
Ali, Rehman;Brevett, Thurston;Hyun, Dongwoon;Brickson, Leandra L;Dahl, Jeremy J
通讯作者：
Dahl, Jeremy J

Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography

研究在超声计算机断层扫描中使用走时和反射断层扫描进行基于深度学习的声速估计

DOI：
10.3390/app12073547
发表时间：
2023-11-16
期刊：
Applied Sciences
影响因子：
0
作者：
Gangwon Jeong;Fu Li;Umberto Villa;M. Anastasio
通讯作者：
M. Anastasio

Aberration correction in diagnostic ultrasound: A review of the prior field and current directions.

诊断超声中的像差校正：对先前领域和当前方向的回顾。

DOI：
发表时间：
2023-08
期刊：
影响因子：
0
作者：
Ali, Rehman;Brevett, Thurston;Zhuang, Louise;Bendjador, Hanna;Podkowa, Anthony S;Hsieh, Scott S;Simson, Walter;Sanabria, Sergio J;Herickhoff, Carl D;Dahl, Jeremy J
通讯作者：
Dahl, Jeremy J

Angular spectrum method for curvilinear arrays: Theory and application to Fourier beamforming.

曲线阵列的角谱方法：傅立叶波束形成的理论与应用。