Institute of Medical Imaging and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Department of Radiology, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China.
Med Biol Eng Comput. 2020 Sep;58(9):2083-2093. doi: 10.1007/s11517-020-02226-5. Epub 2020 Jul 10.
Population registration has been proposed for normalizing a large group of images into a common space, which is important in many clinical and research studies, such as brain development, aging, and atlas construction. Different from pairwise registration problem that aligns the target image to the reference directly, determining the reference or the hidden common space with the least bias is important in population registration. In order to decrease this bias, a lot of work takes the arithmetic mean image as the reference. However, the arithmetic mean image is usually too smooth to guide the population registration. This work presents an efficient symmetric population registration strategy for brain template construction, which defines the symmetric population center guiding population registration. This is important because the population registration problem can be translated into a series of pairwise registration problem which is easier to optimize and implement. Another prominent merit of proposed population registration algorithm is reference-free, which eliminates the reference dependency-related bias in population registration. Based on this symmetric population registration, the brain template is constructed by approximating both the population's intensity and gradient information. In addition, we also present a new measurement named with average bias for evaluating the unbiasedness of brain template. Experiments were first carried out on four synthetic images created with controllable transforms, which aim at comparing the difference between conventional method and proposed method. Further experiment is designed for reference-free validation. Finally, in real inter-subject brain data, twenty MRI T1 volumes with size 256 × 256 × 176 are used to construct a symmetric brain template with proposed population registration method. The constructed brain template has a small bias and clear brain details comparing with DARTEL.
人群注册被提议用于将大量图像规范化到一个公共空间，这在许多临床和研究研究中很重要，如大脑发育、衰老和图谱构建。与直接将目标图像对齐到参考的对配准问题不同，在人群注册中，确定参考或隐藏的公共空间是非常重要的。为了减少这种偏差，许多工作采用平均图像作为参考。然而，平均图像通常过于平滑，无法指导人群注册。本工作提出了一种用于脑模板构建的高效对称人群注册策略，该策略定义了指导人群注册的对称人群中心。这很重要，因为人群注册问题可以转化为一系列更容易优化和实现的对配准问题。所提出的人群注册算法的另一个突出优点是无参考，它消除了人群注册中与参考相关的偏差。基于这种对称人群注册，通过近似人群的强度和梯度信息来构建脑模板。此外，我们还提出了一种新的测量方法，称为平均偏差，用于评估脑模板的无偏性。实验首先在四个具有可控变换的合成图像上进行，旨在比较传统方法和所提出方法之间的差异。进一步的实验是为了验证无参考。最后，在真实的受试者间脑数据中，使用 256×256×176 的大小的二十个 MRI T1 体积，采用所提出的人群注册方法构建对称脑模板。与 DARTEL 相比，所构建的脑模板具有较小的偏差和清晰的脑细节。
