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MISPEL:用于多扫描仪神经成像数据的监督深度学习协调方法.

Mahbaneh Eshaghzadeh Torbati1, Davneet S Minhas2, Charles M Laymon3

  • 1Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA.

Medical image analysis
|August 18, 2023
PubMed
概括
此摘要是机器生成的。

大规模的神经成像数据聚合面临来自扫描器变化的挑战. MISPEL (通过结构保护嵌入学习进行多扫描仪图像协调) 有效地协调多扫描仪数据,提高分析可靠性.

关键词:
统一化 统一化 统一化这就是为什么MRI是MRI.规范化 规范化 规范化 规范化扫描仪的效果,扫描仪的效果.技术变异性 技术变异性

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科学领域:

  • 神经成像是一种神经成像.
  • 数据科学数据科学数据科学
  • 医学成像分析 医学成像分析

背景情况:

  • 聚合的多站点神经成像数据集提供了更大的统计能力.
  • 扫描仪规格的差异引入了技术变化,可能导致偏差分析.
  • 有效的数据规范化和协调对于可靠的多站点研究至关重要.

研究的目的:

  • 提出MISPEL (通过结构保护嵌入学习进行多扫描仪图像协调),一种新的监督协调方法.
  • 制定评估扫描仪相关变异性和评估协调技术的标准.
  • 为了解决聚合的神经成像数据中的技术变异性.

主要方法:

  • 开发了MISPEL,一个监督的多扫描仪协调方法.
  • 在四个扫描仪中创建了一个3T T1图像的多扫描仪匹配数据集.
  • 使用FSL和SPM细分框架进行了评估协调.

主要成果:

  • 与白条纹,RAVEL和CALAMITI相比,MISPEL表现出更好的表现.
  • 提出的标准有效地调查了与扫描仪相关的变异性.
  • MISPEL显示出各种神经成像模式的前景.

结论:

  • MISPEL是多扫描器神经成像数据协调的有效和可扩展的解决方案.
  • 开发的评估标准为评估协调技术提供了坚实的框架.
  • 解决扫描器变异性对于最大限度地利用大规模神经成像数据集的好处至关重要.