人工智能
对比度(视觉)
计算机视觉
特征(语言学)
舞蹈
计算机科学
超分辨率
图像(数学)
模式识别(心理学)
艺术
语言学
文学类
哲学
作者
Wenxuan Chen,Songdi Wu,Shuai Wang,Zhongsen Li,Yang Jia,Huifeng Yao,Qiyuan Tian,Xiaolei Song
标识
DOI:10.1016/j.media.2024.103359
摘要
Multi-contrast magnetic resonance imaging (MRI) reflects information about human tissues from different perspectives and has wide clinical applications. By utilizing the auxiliary information from reference images (Refs) in the easy-to-obtain modality, multi-contrast MRI super-resolution (SR) methods can synthesize high-resolution (HR) images from their low-resolution (LR) counterparts in the hard-to-obtain modality. In this study, we systematically discussed the potential impacts caused by cross-modal misalignments between LRs and Refs and, based on this discussion, proposed a novel deep-learning-based method with Deformable Attention and Neighborhood-based feature aggregation to be Computationally Efficient (DANCE) and insensitive to misalignments. Our method has been evaluated in two public MRI datasets, i.e., IXI and FastMRI, and an in-house MR metabolic imaging dataset with amide proton transfer weighted (APTW) images. Experimental results reveal that our method consistently outperforms baselines in various scenarios, with significant superiority observed in the misaligned group of IXI dataset and the prospective study of the clinical dataset. The robustness study proves that our method is insensitive to misalignments, maintaining an average PSNR of 30.67 dB when faced with a maximum range of ±9°and ±9 pixels of rotation and translation on Refs. Given our method's desirable comprehensive performance, good robustness, and moderate computational complexity, it possesses substantial potential for clinical applications.
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