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：
10410471
负责人：
Jeremy Dahl
金额：
$ 55.13万
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/10410471
关键词：
Abdomen Acoustics Affect American Architecture Area Attenuated Cardiac Cirrhosis Clinical 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 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

Abdomen Acoustics Affect American Architecture Area Attenuated Cardiac Cirrhosis Clinical 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 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

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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.

抽象的在过去的40年中给医疗保健系统带来压力，并为医学成像带来困难的挑战。我们提议克服肥胖对超声成像的挑战（1）发展新颖的图像质量改进技术，（2）在Pulse-echo上实现它们超声成像系统可产生肝脏的高质量图像。超声成像受额外结缔组织和厚的结缔组织的唯一影响超重和肥胖患者的皮下脂肪层；这些额外的皮下层大大加剧了声波的回响和相位滥用，导致高混乱，分辨率下降的水平和整体质量不佳的超声图像。我们提出的方法将确定腹部组织层中的局部听觉速度并使用信息以完成分布式相位放弃校正。我们还申请机器学习技术来建模和抑制混响混乱和斑点噪声的影响。这些技术的结合有望在肝脏方面取得重大改善图像质量。这些图像质量改进方法将实时实现超声扫描仪，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。拟议技术的成功开发不仅将实现高质量的超声波在其他难以形象的超重和肥胖患者中对肝脏进行成像，但也促进几乎所有超声成像应用程序的图像质量，以提高所有人的图像质量人群。