图像扭曲
人工智能
计算机科学
计算机视觉
图像(数学)
采样(信号处理)
模式识别(心理学)
滤波器(信号处理)
作者
Qingrui Sha,Kaicong Sun,Caiwen Jiang,Mingze Xu,Xue Zhong,Xiaohuan Cao,Dinggang Shen
出处
期刊:Neural Networks
[Elsevier]
日期:2024-06-07
卷期号:178: 106426-106426
被引量:1
标识
DOI:10.1016/j.neunet.2024.106426
摘要
Multi-phase dynamic contrast-enhanced magnetic resonance imaging image registration makes a substantial contribution to medical image analysis. However, existing methods (e.g., VoxelMorph, CycleMorph) often encounter the problem of image information misalignment in deformable registration tasks, posing challenges to the practical application. To address this issue, we propose a novel smooth image sampling method to align full organic information to realize detail-preserving image warping. In this paper, we clarify that the phenomenon about image information mismatch is attributed to imbalanced sampling. Then, a sampling frequency map constructed by sampling frequency estimators is utilized to instruct smooth sampling by reducing the spatial gradient and discrepancy between all-ones matrix and sampling frequency map. In addition, our estimator determines the sampling frequency of a grid voxel in the moving image by aggregating the sum of interpolation weights from warped non-grid sampling points in its vicinity and vectorially constructs sampling frequency map through projection and scatteration. We evaluate the effectiveness of our approach through experiments on two in-house datasets. The results showcase that our method preserves nearly complete details with ideal registration accuracy compared with several state-of-the-art registration methods. Additionally, our method exhibits a statistically significant difference in the regularity of the registration field compared to other methods, at a significance level of p < 0.05. Our code will be released at https://github.com/QingRui-Sha/SFM.
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