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相关概念视频

Super-resolution Fluorescence Microscopy01:37

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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提高超声波定位显微镜中的子像素精度,使用监督和自我监督的深度学习.

Zeng Zhang1, Misun Hwang2,3, Todd J Kilbaugh4

  • 1Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.

Measurement science & technology
|January 11, 2024
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概括
此摘要是机器生成的。

新的深度学习网络改进了超声波定位显微镜 (ULM) 以更清晰地描绘微血管结构和血液流动. 这些方法提高了距离很近的船只的区分和速度测量的准确性.

关键词:
深度学习是一种深度学习.自主监督学习学习超高分辨率超声波成像技术超声波定位显微镜学 超声波定位显微镜学

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科学领域:

  • 医疗成像医学成像
  • 生物医学工程 生物医学工程
  • 计算成像技术的成像

背景情况:

  • 超声波局部化显微镜 (ULM) 重建了微血管结构,并使用对比增强超声波 (CEUS) 图像中的微气泡测量了血液流动.
  • 精确的微气泡定位对于ULM忠实性至关重要,因为与微气泡相比,CEUS气泡痕迹的大小很大.
  • 现有的方法在噪音数据和准确定位微气泡中心的高分辨率成像方面扎.

研究的目的:

  • 开发先进的深度学习方法,以改进在CEUS数据中的微泡检测和定位.
  • 提高ULM的空间分辨率,以更好地可视化微血管网络.
  • 为了提高微型和巨型船舶的速度谱测量的准确性.

主要方法:

  • 引入基于剩余学习的监督超分辨率盲解卷网络 (SupBD-net).
  • 开发一种新的损失函数,用于自主监督的盲解卷网络 (SelfBD-net),以处理未知的泡痕迹形态.
  • 使用合成数据对比SupBD-net和SelfBD-net与现有的深度学习和盲目的解卷技术.

主要成果:

  • SupBD-net在气泡中心位置上的误差最低 (<0.1 λ),优于其他方法.
  • 在监督方法失败的情况下,SelfBD-net对未知的泡痕迹形态保持了准确性 (<0.15 λ).
  • 无论是SupBD-net还是SelfBD-net,在分离靠近的气泡和微容器以及精确的速度配置测量方面都表现出卓越的性能.

结论:

  • 提出的基于残留学习的方法显著提高了ULM的空间分辨率和准确性.
  • 通过SupBD-net和SelfBD-net,可以区分隔0.15λ的邻近微容器,超越了以前的技术.
  • 这些进步有望在临床和研究应用中实现更详细,更准确的微血管成像.