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中文摘要
翻译
摘要 在过去的40年里,美国的肥胖率上升到了创纪录的水平, 给医疗系统带来压力,并给医学成像带来困难的挑战。我们 建议通过以下方式克服肥胖给超声成像带来的挑战:(1) 新的图像质量改善技术,以及(2)在脉冲回波上的实现 超声成像系统,以产生高质量的肝脏图像。 超声成像独特地受到额外结缔组织和厚度的影响 超重和肥胖患者的皮下脂肪层;这些额外的皮下脂肪层 极大地加剧了声波的混响和位相畸变,导致高 杂乱程度、分辨率降低和整体质量较差的超声图像。我们的建议 方法将确定腹部组织层中的局部声速,并使用 用于完成分布式相位像差校正的信息。我们还适用于机器 模拟和抑制混响、杂波和斑点噪声影响的学习技术。 这些技术的结合有望在肝脏方面取得显著改善。 图像质量。这些改善图像质量的方法将实时实施 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使高质量的超声波 超重和肥胖患者的肝脏成像,但也包括 促进提高几乎所有超声成像应用程序的图像质量 人口。
英文摘要
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.
期刊论文(7)
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会议论文
Optimal transmit apodization for the maximization of lag-one coherence with applications to aberration delay estimation.
最佳传输变迹,以最大化滞后一相干性与像差延迟估计的应用。
DOI: 10.1016/j.ultras.2023.107010
发表时间: 2023
期刊: Ultrasonics
影响因子: 4.2
作者: [Ali,Rehman, Duric,Nebojsa, Dahl,JeremyJ]
通讯作者: Dahl,JeremyJ
DOI: 10.1121/10.0010536
发表时间: 2022-05
期刊: JASA express letters
影响因子: 1
作者: []
通讯作者:
DOI: 10.1109/tuffc.2022.3162836
发表时间: 2022-05
期刊: IEEE transactions on ultrasonics, ferroelectrics, and frequency control
影响因子: --
作者: []
通讯作者:
DOI: 10.1016/j.zemedi.2023.01.003
发表时间: 2023-08
期刊: ZEITSCHRIFT FUR MEDIZINISCHE PHYSIK
影响因子: 2
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.
B7-H3 Targeted Ultrasound Molecular Imaging System for Early Breast Cancer and Metastatic Detection
  • 批准号:
    10584161
  • 项目类别:
  • 资助金额:
    $56.17万
  • 财政年份:
    2023
  • 负责人:
    Jeremy Dahl
  • 依托单位:
Serial Ultrasound to Detect Early Response to Immunotherapy in Metastatic RCC
  • 批准号:
    10357118
  • 项目类别:
  • 资助金额:
    $18.09万
  • 财政年份:
    2022
  • 负责人:
    Jeremy Dahl
  • 依托单位:
Serial Ultrasound to Detect Early Response to Immunotherapy in Metastatic RCC
  • 批准号:
    10589070
  • 项目类别:
  • 资助金额:
    $21.22万
  • 财政年份:
    2022
  • 负责人:
    Jeremy Dahl
  • 依托单位:
Improving Liver Ultrasound Image Quality in Difficult-to-Image Patients
  • 批准号:
    10410471
  • 项目类别:
  • 资助金额:
    $55.13万
  • 财政年份:
    2020
  • 负责人:
    Jeremy Dahl
  • 依托单位:
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