Multimodality brain image registration technology is the key technology to determine the accuracy and speed of brain diagnosis and treatment. In order to achieve high-precision image registration, a fast subpixel registration algorithm based on single-step DFT combined with phase correlation constraint in multimodality brain image was proposed in this paper. Firstly, the coarse positioning at the pixel level was achieved by using the downsampling cross-correlation model, which reduced the Fourier transform dimension of the cross-correlation matrix and the multiplication of the discrete Fourier transform matrix, so as to speed up the coarse registration process. Then, the improved DFT multiplier of the matrix multiplication was used in the neighborhood of the coarse point, and the subpixel fast location was achieved by the bidirectional search strategy. Qualitative and quantitative simulation experiment results show that, compared with comparison registration algorithms, our proposed algorithm could greatly reduce space and time complexity without losing accuracy.

中文翻译：

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基于单步DFT结合相位相关约束的多模态脑图像快速亚像素配准算法。

多模态脑图像配准技术是确定脑部诊断和治疗的准确性和速度的关键技术。为了实现高精度的图像配准，提出了一种基于单步DFT结合相位相关约束的多模态脑图像快速亚像素配准算法。首先，利用降采样互相关模型实现了像素级的粗定位，减小了互相关矩阵的傅立叶变换维数和离散傅立叶变换矩阵的乘积，从而加快了粗配准。处理。然后，在粗点附近使用改进的矩阵乘法DFT乘法器，通过双向搜索策略实现了亚像素快速定位。定性和定量的仿真实验结果表明，与比较配准算法相比，本文提出的算法在不损失精度的前提下，可以大大减少时空复杂度。