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.

已经提出了人口登记来将一大堆图像归一化为一个公共空间，这在许多临床和研究中都很重要，例如大脑发育，衰老和图集构建。与将目标图像直接对准参考图像的成对配准问题不同，在总体配准中，确定具有最小偏差的参考或隐藏公共空间非常重要。为了减少这种偏差，很多工作都以算术平均图像作为参考。但是，算术平均图像通常过于平滑，无法指导人口登记。这项工作提出了一种有效的对称人口登记策略，用于脑模板的构建，它定义了指导人口登记的对称人口中心。这很重要，因为人口登记问题可以转化为一系列成对登记问题，更易于优化和实施。提出的人口登记算法的另一个突出优点是无参考，从而消除了人口登记中与参考依赖相关的偏差。基于这种对称的人口登记，通过近似人口的强度和梯度信息来构造大脑模板。此外，我们还提出了一种以平均偏差命名的新测量方法，用于评估大脑模板的无偏差性。首先对通过可控变换创建的四个合成图像进行实验，目的是比较传统方法与建议方法之间的差异。进一步的实验旨在进行无参考的验证。最后，在真实的受试者间脑数据中，使用二十种MRI T1体积为256×256×176的体积，通过提出的人口登记方法构建对称的脑模板。与DARTEL相比，构造的大脑模板偏差小且大脑细节清晰。