课题基金 / 基金详情

项目摘要

项目成果

Jeremy Dahl的其他基金

相似基金

相关文献

中文摘要
翻译
摘要 在过去的40年里,美国肥胖症的流行率已经上升到创纪录的水平, 给医疗保健系统带来压力,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)开发 新的图像质量改善技术,以及(2)在脉冲回波上实现它们 超声成像系统,以产生高质量的肝脏图像。 超声成像是唯一的影响,存在额外的结缔组织和厚 超重和肥胖患者的皮下脂肪层;这些额外的皮下层 大大加剧了声波的混响和相位畸变,导致高 杂波水平、分辨率降低和总体质量差的超声图像。我们提出的 方法将确定腹部组织层中的局部声速, 信息,以完成分布式相位畸变校正。我们也应用机器 学习技术来建模和抑制混响杂波和斑点噪声的影响。 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善方法将在实时 超声扫描仪,并将在超重和肥胖的临床成像任务进行评估 肝细胞癌超声监测患者。 所提出的技术的成功开发不仅可以实现高质量的超声 在其他方面难以成像的超重和肥胖患者的肝脏成像,而且 在几乎所有的超声成像应用中, 人口数量。
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)
专著(0)
科研奖励(0)
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: 10.1088/1361-6560/ac4562
发表时间: 2022-01-17
期刊: Physics in medicine and biology
影响因子: 3.5
作者: [Telichko AV, Ali R, Brevett T, Wang H, Vilches-Moure JG, Kumar SU, Paulmurugan R, Dahl JJ]
通讯作者: Dahl JJ
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:
海外基金