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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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LiftReg: Limited Angle 2D/3D Deformable Registration.

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Inverse Consistency by Construction for Multistep Deep Registration.

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相关实验视频

Updated: Jun 26, 2025

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

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运动补偿无监督深度学习用于5DMRI

Joseph Kettelkamp1, Ludovica Romanin2, Davide Piccini2

  • 1University of Iowa, IA.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|May 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种无监督的深度学习方法,用于更快,更准确的5D心脏MRI重建. 这种新的算法在自由呼吸扫描中提高了运动补偿和数据效率.

关键词:
5D磁力共振成像 5D磁力共振成像心脏磁力共振成像 (MRI)免费运行的MRI.

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
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Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

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相关实验视频

Last Updated: Jun 26, 2025

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
06:56

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 心血管成像 - 心血管成像

背景情况:

  • 五维 (5D) 心脏MRI提供了先进的可视化,但面临着长时间的重建时间和运动器件的挑战.
  • 目前用于5DMRI重建的方法是计算密集的,对数据对比准确性敏感.

研究的目的:

  • 开发一种无监督的深度学习算法,用于5D心脏MRI的运动补偿重建.
  • 与现有的重建技术相比,提高数据效率和减少计算时间.

主要方法:

  • 一种无监督的深度学习方法将心脏MRI数据模型为变形3D图像模板的福里埃样本.
  • 卷积神经网络估计由生理阶段信息驱动的变形图.
  • 用1D导航器和自动编码器确定心脏和呼吸系统阶段.

主要成果:

  • 拟议的算法为当前的运动解析重建提供了一个数据效率高的替代方案.
  • 从测量数据中联合估计变形图和图像模板.
  • 在两个受试者的5D bSSFP数据集上进行了验证.

结论:

  • 无监督的深度学习算法有效地执行5D心脏MRI的运动补偿重建.
  • 这种方法提高了自由呼吸心脏MRI采集的效率和患者舒适度.
  • 这种方法有可能比传统的2D心脏MRI检查有更好的临床益处.