Cross contrast multi-channel image registration using image synthesis for MR brain images.
Cross contrast multi-channel image registration using image synthesis for MR brain images.
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DOI：
10.1016/j.media.2016.10.005
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发表时间：
2017-02
影响因子：
10.9
通讯作者：
Prince JL
中科院分区：
文献类型：
--
作者：
Chen M;Carass A;Jog A;Lee J;Roy S;Prince JL
多模态可变形配准对于许多医学图像分析任务（例如图谱对齐、图像融合和畸变校正）非常重要。传统方法会使用独立于模态的特征或互信息等信息论度量来配准不同模态的图像，而本文提出了一种新的框架，该框架使用能够使用单模态相似性度量（例如平方差和或互相关）的双通道配准算法来解决该问题。为了能够使用这些相同模态的测量，图像合成用于创建相反模态的代理图像以及来自两个可用图像中的每一个的强度归一化图像。通过使用多对比磁共振脑成像数据进行受试者内变形恢复、受试者内边界对齐和受试者间标签转移实验来评估新的变形配准框架。对三种不同的多通道配准算法进行了评估，表明该框架对于所使用的多通道变形配准算法具有鲁棒性。除了一个例外，与使用具有互信息的相同算法的单通道注册相比，所有结果都显示出改进。
Multi-modal deformable registration is important for many medical image analysis tasks such as atlas alignment, image fusion, and distortion correction. Whereas a conventional method would register images with different modalities using modality independent features or information theoretic metrics such as mutual information, this paper presents a new framework that addresses the problem using a two-channel registration algorithm capable of using mono-modal similarity measures such as sum of squared differences or cross-correlation. To make it possible to use these same-modality measures, image synthesis is used to create proxy images for the opposite modality as well as intensity-normalized images from each of the two available images. The new deformable registration framework was evaluated by performing intra-subject deformation recovery, intra-subject boundary alignment, and inter-subject label transfer experiments using multi-contrast magnetic resonance brain imaging data. Three different multi-channel registration algorithms were evaluated, revealing that the framework is robust to the multi-channel deformable registration algorithm that is used. With a single exception, all results demonstrated improvements when compared against single channel registrations using the same algorithm with mutual information.