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自主监督的任意规模超角分辨率扩散MRI重建自主监督的MRI重建

Shuangxing Wang1, Lihui Wang1, Ying Cao1

  • 1Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province, Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China.

Medical physics
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概括
此摘要是机器生成的。

这项研究介绍了SARDI-nn,这是一个新的深度学习网络,用于从有限的数据中重建高角度分辨率扩散MRI图像. 这种方法通过从更少的采集生成详细的扩散加权图像来增强组织微观结构分析.

关键词:
高角扩散成像技术的高角扩散成像技术隐含的神经表现隐含的神经表现当地的自我相似性.在q-space学习中学习.q-空间相似性类似性

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科学领域:

  • 医疗成像医学成像
  • 神经科学是一个神经科学.
  • 人工智能的人工智能

背景情况:

  • 扩散磁共振成像 (dMRI) 对于在体内组织微观结构的非侵入性研究至关重要.
  • 在多个方向上获取扩散权重 (DW) 图像是耗时的,限制了临床应用.
  • 从有限的扩散方向来估计组织微观结构仍然是一个重大挑战.

研究的目的:

  • 提出一种自主监督的网络,SARDI-nn,用于在任意角分辨率下重构扩散权重 (DW) 图像.
  • 为了从使用较少的扩散方向获得的DW图像中进行详细的组织微观结构分析.

主要方法:

  • 开发了SARDI-nn,包括DW图像特征提取 (DWFE) 和物理驱动隐式重建 (IRR) 模块.
  • 用于培训的双向下采样:首先是低角度分辨率 (LAR) 的DW图像,第二是输入/目标构造.
  • 在人类连接组项目和内部数据集上进行测试,与使用PSNR,SSIM,RMSE和微结构指标 (DKI,NODDI) 的现有方法进行比较.

主要成果:

  • 在重建的DW图像中,SARDI-nn实现了卓越的性能,提高了SSIM的10.04% (高级3) 和5.9% (高级15).
  • 超越微结构指标 (DKI,NODDI) 的监督方法,高达6. upscale因子.
  • 在外部数据集上表现出很好的概括性,证实了方法的稳定性.

结论:

  • SARDI-nn是第一个能够重建高角度分辨率的DW图像的方法,不需要重新训练.
  • 促进扩散方向数和升级的灵活变化,使未见的数据集可以轻松扩展.
  • 使用有限的dMRI收购,为先进的组织微观结构分析提供了一个有前途的方法.