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适应性局部边界条件,以改善可变形图像的注册

Eloïse Inacio^1,2, Luc Lafitte², Laurent Facq¹

¹University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project team Monc, F-33405 Talence Cedex, France.

Physics in medicine and biology

|July 30, 2024

概括

这项研究引入了一种新的,适应性边界条件,用于可变形图像注册,提高医学成像任务的准确性. 该方法优化了边界条件,以获得更好的对齐,这对于图像引导疗法至关重要.

科学领域:

医疗成像医学成像
计算解剖学的计算解剖学
图像注册图像注册图像注册

背景情况:

在图像导向治疗中,准确评估和纠正运动至关重要.
可变形图像注册 (DIR) 将移动图像与固定图像对齐.
边界条件对于DIR准确性至关重要,特别是在图像边界附近.

研究的目的:

为各种医疗DIR任务定制边界条件.
为了解决DIR全球边界条件的局限性.
开发适应性边界条件的自动化框架.

主要方法:

分析了同质的迪里克莱特和诺曼边界条件.
提出了一个通用的,局部适应的,罗宾型边界条件.
通过能源最小化通过超参数优化自动化框架.

主要成果:

在CT胸部和CT到MRI腹部登记任务上进行测试.
在胸部CT的目标注册错误中实现了高达12%的相对改善.
在腹部CT-to-MRI任务中表现出近乎最佳的结果.

结论:

定制边界条件对于优化图像注册至关重要.
引入了一种新的voxel-by-voxel适应性边界条件方法.
该框架提供了优化的DIR结果,没有事先的图像假设.

关键词:

边界条件边界条件可变形图像的注册变形图像的注册变量方法变量方法

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