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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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Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
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相关实验视频

Updated: Jan 17, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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使用基于反转的预处理进行MRI注释,用于CT模型适应.

Hartmut Häntze1,2, Lina Xu1, Maximilian Nikolas Rattunde1

  • 1Department of Radiology, Charité-Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.

European radiology experimental
|September 20, 2025
PubMed
概括

计算机断层扫描 (CT) 细分模型可以生成准确的磁共振成像 (MRI) 预细分. 图像逆转预处理提高了CT模型在T1加权MRI上的性能,提高了结构和脏瘤的细分精度.

关键词:
人工智能的人工智能瘤 (细胞) 是一种瘤.图像处理 (计算机辅助)磁共振成像技术 磁共振成像技术断层扫描 (X射线计算) 的使用

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
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相关实验视频

Last Updated: Jan 17, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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科学领域:

  • 医学成像分析分析 医学成像分析
  • 放射学中的人工智能
  • 跨模式图像细分跨模式图像细分

背景情况:

  • 在MRI中手动注释新类是耗时的.
  • CT细分模型可以支持MRI分析,但直接翻译具有挑战性.
  • 经过CT训练的模型可以创建精确的MRI预分割,有或没有图像反转.

研究的目的:

  • 在MRI数据上评估CT训练的细分模型的性能.
  • 评估图像反转预处理对细分精度的影响.
  • 确定CT模型对各种解剖结构和脏瘤的MRI的概括性.

主要方法:

  • 从100名患者的100个T1加权和100个T2加权的脂肪和MRI序列的回顾性分析.
  • 应用一个通用的多类CT模型 (TotalSegmentator) 和一个专门的脏瘤模型.
  • 在原始和强度反转的序列上使用子相似系数 (DSC) 评估细分质量.

主要成果:

  • 分段精度因MRI序列和解剖结构而异.
  • CT模型在T2wfs序列 (DSC 0.60) 中准确地细分了脏,但在血管和肌肉方面遇到了困难.
  • 强度逆转显著改善了T1w序列上的TotalSegmentator性能 (平均DSC为0.04至0.56,p <0.001).
  • 逆转改善了T1w序列中的瘤细分DSC,从0.04到0.42 (p < 0.001).

结论:

  • 经过CT训练的模型可以通过适当的预处理,如图像反转,将其推广为MRI.
  • 逆向预处理使T1wMRI使用CT模型实现了细胞癌的细分.
  • CT模型显示了对MRI的潜在可转移性,加速了用于MRI分析的AI开发.