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相关概念视频

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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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相关实验视频

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探索基于深度学习的大脑MRI-to-CT合成中的对比概括.

Lotte Nijskens1, Cornelis A T van den Berg1, Joost J C Verhoeff2

  • 1Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Science, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584CX, The Netherlands; Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584CX, The Netherlands.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
|July 20, 2023
PubMed
概括

域随机化 (DR) 增强了从MRI生成合成CT的深度学习模型,提高了稳定性并减少了再培训需求. 这种方法提高了放射治疗应用中未见的MRI序列的概括性.

关键词:
人工智能的人工智能是人工智能.计算机断层扫描 (CT) 是一种计算机断层扫描.域名转移 域名转移域名转移概括 概括 是一个概括.机器学习 机器学习磁共振成像技术 磁共振成像技术医学成像医学成像辐射疗法 辐射疗法回归是一种回归.

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科学领域:

  • 医疗成像医学成像
  • 放射治疗 物理 物理
  • 人工智能在医学中的应用

背景情况:

  • 合成计算机断层扫描 (sCT) 对基于MRI的放射治疗至关重要,深度学习 (DL) 模型对其产生有希望.
  • 挑战来自不同中心的MRI协议的变化,导致DL模型的概括性较差和质量较低的sCT.
  • 这就需要方法来提高用于sCT生成的DL模型的稳定性和通用性.

研究的目的:

  • 调查域随机化 (DR) 在增强DL模型对大脑sCT生成的概括能力方面的有效性.
  • 评估DR对从未见过的MRI序列生成的sCT的准确性和稳定性的影响.

主要方法:

  • 一个生成对抗网络 (GAN) 使用CT和各种MRI序列 (T1,T2,FLAIR) 来训练95名接受放射治疗的患者.
  • 一个基线模型在没有DR的情况下被训练,其在未见的FLAIR序列上的性能与DR增强模型进行了比较.
  • 图像相似度指标和基于sCT的放射治疗剂量计划的准确性与基底真相CT进行了评估.

主要成果:

  • 基线模型在未见的FLAIR序列 (106 ± 20.7 HU) 上显示了最高的平均绝对误差 (MAE).
  • 与基线相比,DR模型在FLAIR上表现有所改善 (MAE = 99.0 ± 14.9 HU),尽管仍然低于用FLAIR数据训练的模型 (MAE = 72.6 ± 10.1 HU).
  • 与基线相比,DR还导致了改善的马传递率,这表明剂量计划的准确性更好.

结论:

  • 域随机化有效地提高图像相似性和剂量准确性,用于在看不见的MRI序列上生成sCT.
  • DR提高了DL模型的稳定性,在遇到新的或变化的MRI数据时减轻了重新培训的需要.
  • 这种方法支持基于MRI的放射疗法的更广泛的临床采用,通过提高不同成像协议中合成CT生成的可靠性.