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适应性扩散先验用于加速MRI重建

Alper Güngör¹, Salman Uh Dar², Şaban Öztürk³

¹Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey; ASELSAN Research Center, Ankara 06200, Turkey.

Medical image analysis

|June 29, 2023

概括

我们介绍AdaDiff,这是磁共振成像 (MRI) 重建之前的自适应扩散. 这种新的方法提高了图像质量和可靠性,超过了现有的技术,特别是当成像条件发生变化时.

科学领域:

医疗成像医学成像
人工智能的人工智能
图像重建图像的重建

背景情况:

对于MRI重建的深度学习模型,由于成像操作员的变化,通常会与一般化作斗争.
无条件模型通过将图像先验与特定运营商的脱提供了更高的可靠性,扩散模型显示了高保真度.
扩散模型中的静态图像先验可以在推断过程中导致低于最佳的性能.

研究的目的:

在MRI重建之前开发了第一个自适应扩散,名为AdaDiff.
为了提高MRI重建对域移动的性能和可靠性.
提高MRI重建中的扩散模型的适应性.

主要方法:

通过对抗映射在延长的反向扩散步骤上进行训练,AdaDiff利用了高效的扩散预先训练.
采用了两阶段的重建过程:最初的快速扩散阶段,然后是适应阶段.
适应阶段通过更新先前的数据来完善重建,以最大限度地减少数据一致性损失.

主要成果:

与有条件和无条件方法相比,当AdaDiff在多对比脑MRI中受到域移位时,其表现优越.
该方法在同一领域内实现了同等或优异的性能.
与可变成像操作员相比,AdaDiff显示出可靠性和稳定性的显著改进.

结论:

AdaDiff代表了MRI重建的重大进步,提供了更好的适应性和性能.
适应性扩散先验有效地解决了静态先验和域移动的局限性.
这种方法有望在各种临床环境中实现更可靠,更高质量的MRI采集.

关键词:

适应性的适应性的扩散扩散是一种扩散.生成性的产生性.之前的图像之前的图像这就是为什么MRI是MRI.重建重建的重建工作

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Magnetic Resonance Imaging

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