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相关概念视频

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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相关实验视频

Updated: May 6, 2026

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

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通过深度学习,从降低频率偏移量化加速CEST成像.

Karandeep Cheema1,2, Pei Han1,2, Hsu-Lei Lee1

  • 1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.

Magnetic resonance in medicine
|September 13, 2024
PubMed
概括
此摘要是机器生成的。

使用低样本Z光谱的深度学习显著减少化学交换和转移 (CEST) 扫描时间. 一个U-NET模型从稀疏的数据准确地构建CEST地图,使得MRI采集速度更快.

关键词:
在 APTw 里.在这里,我们可以看到 DS DS DS DS DS.渔民获得信息获取.在这个过程中,MTTMTTM在U-NET中,U-NET是U-NET.化学交换和转移化学交换和转移深度学习是一种深度学习.在诺伊尔.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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相关实验视频

Last Updated: May 6, 2026

Quantifying Intermembrane Distances with Serial Image Dilations
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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
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科学领域:

  • 磁共振成像是一种磁共振成像技术.
  • 生物医学工程 生物医学工程
  • 人工智能在医学中的应用

背景情况:

  • 化学交换和转移 (CEST) MRI对于组织特征有价值.
  • 获取时间是CESTMRI的主要限制.
  • 深度学习为加速MRI采集提供了潜在的解决方案.

研究的目的:

  • 开发和验证一种深度学习方法,用于从低采样的Z光谱构建CEST地图.
  • 为了显著减少CESTMRI采集时间,同时保持定量准确性.
  • 在模拟的病理条件下评估深度学习模型的性能.

主要方法:

  • 费舍尔信息获取分析,以确定多池安装的最佳频率偏移.
  • 开发和培训U-NET架构,使用18名志愿者的低样本脑CEST数据.
  • 采用了回顾性和前性体内低采样策略,减少了Z频谱偏移的数量.
  • 模拟质母细胞瘤病理学,以评估网络性能.

主要成果:

  • 传统的多池模型无法从低样本数据准确量化CEST地图 (SSIM <0.2).
  • 通过U-NET测试,成功地从低采样Z光谱生成了定量CEST图.
  • 预期低样本缩短了扫描时间的3.5倍,与地面真相相比,实现了高精度 (MSE=4.4e-4,r=0.82,SSIM=0.84).
  • 通过U-NET模型,可靠地预测了模拟质母细胞瘤的CEST值.

结论:

  • 在U-NET架构有效量化CEST地图从 undersampled Z-spectra.
  • 这种深度学习方法可以显著减少CESTMRI扫描时间.
  • 该方法显示了在临床环境中加速定量MRI的前景.