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基于熵谱路径的相空间正则化的辛同伦配准。

Symplectomorphic registration with phase space regularization by entropy spectrum pathways.

机构信息

Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, California.

Electrical and Computer Engineering Department, University of California at San Diego, La Jolla, California.

出版信息

Magn Reson Med. 2019 Feb;81(2):1335-1352. doi: 10.1002/mrm.27402. Epub 2018 Sep 19.

Abstract

PURPOSE

The ability to register image data to a common coordinate system is a critical feature of virtually all imaging studies. However, in spite of the abundance of literature on the subject and the existence of several variants of registration algorithms, their practical utility remains problematic, as commonly acknowledged even by developers of these methods.

METHODS

A new registration method is presented that utilizes a Hamiltonian formalism and constructs registration as a sequence of symplectomorphic maps in conjunction with a novel phase space regularization. For validation of the framework a panel of deformations expressed in analytical form is developed that includes deformations based on known physical processes in MRI and reproduces various distortions and artifacts typically present in images collected using these different MRI modalities.

RESULTS

The method is demonstrated on the three different magnetic resonance imaging (MRI) modalities by mapping between high resolution anatomical (HRA) volumes, medium resolution diffusion weighted MRI (DW-MRI) and HRA volumes, and low resolution functional MRI (fMRI) and HRA volumes.

CONCLUSIONS

The method has shown an excellent performance and the panel of deformations was instrumental to quantify its repeatability and reproducibility in comparison to several available alternative approaches.

摘要

目的

将图像数据注册到公共坐标系的能力是几乎所有成像研究的关键特征。然而,尽管关于这个主题的文献很多,并且存在几种注册算法的变体,但正如这些方法的开发者甚至也承认的那样,它们的实际应用仍然存在问题。

方法

提出了一种新的注册方法,该方法利用哈密顿形式,并结合一种新的相空间正则化,将注册构建为一系列辛同胚映射。为了验证该框架,开发了一个以解析形式表示的变形面板,该面板包括基于 MRI 中已知物理过程的变形,并再现了使用这些不同 MRI 模式采集的图像中通常存在的各种扭曲和伪影。

结果

该方法在三种不同的磁共振成像(MRI)模式上进行了演示,通过在高分辨率解剖(HRA)体积、中分辨率扩散加权 MRI(DW-MRI)和 HRA 体积以及低分辨率功能 MRI(fMRI)和 HRA 体积之间进行映射。

结论

该方法表现出优异的性能,变形面板对于量化其与几种可用替代方法相比的可重复性和再现性非常重要。

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A Unified Theory of Neuro-MRI Data Shows Scale-Free Nature of Connectivity Modes.
Neural Comput. 2017 Jun;29(6):1441-1467. doi: 10.1162/NECO_a_00955. Epub 2017 Mar 23.
2
Detecting Spatio-Temporal Modes in Multivariate Data by Entropy Field Decomposition.
J Phys A Math Theor. 2016 Sep 30;49(39). doi: 10.1088/1751-8113/49/39/395001. Epub 2016 Sep 6.
3
A survey of medical image registration - under review.
Med Image Anal. 2016 Oct;33:140-144. doi: 10.1016/j.media.2016.06.030. Epub 2016 Jun 21.
4
Dynamic Multiscale Modes of Resting State Brain Activity Detected by Entropy Field Decomposition.
Neural Comput. 2016 Sep;28(9):1769-811. doi: 10.1162/NECO_a_00871. Epub 2016 Jul 8.
5
Hamiltonian Systems and Optimal Control in Computational Anatomy: 100 Years Since D'Arcy Thompson.
Annu Rev Biomed Eng. 2015;17:447-509. doi: 10.1146/annurev-bioeng-071114-040601. Epub 2015 Nov 4.
6
Finite-Dimensional Lie Algebras for Fast Diffeomorphic Image Registration.
Inf Process Med Imaging. 2015;24:249-59. doi: 10.1007/978-3-319-19992-4_19.
7
Simultaneous multi-scale diffusion estimation and tractography guided by entropy spectrum pathways.
IEEE Trans Med Imaging. 2015 May;34(5):1177-93. doi: 10.1109/TMI.2014.2380812. Epub 2014 Dec 18.
8
DR-BUDDI (Diffeomorphic Registration for Blip-Up blip-Down Diffusion Imaging) method for correcting echo planar imaging distortions.
Neuroimage. 2015 Feb 1;106:284-99. doi: 10.1016/j.neuroimage.2014.11.042. Epub 2014 Nov 26.
9
DR-BUDDI: diffeomorphic registration for blip up-down diffusion imaging.
Med Image Comput Comput Assist Interv. 2014;17(Pt 1):218-26. doi: 10.1007/978-3-319-10404-1_28.
10
Bayesian principal geodesic analysis in diffeomorphic image registration.
Med Image Comput Comput Assist Interv. 2014;17(Pt 3):121-8. doi: 10.1007/978-3-319-10443-0_16.

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