用于超快时空编码MRI的Transformer超分辨率重建算法研究

Research on Transformer Super-Resolution Reconstruction Algorithm for Ultrafast Spatiotemporal Encoding Magnetic Resonance Imaging

时空编码(SPEN)磁共振成像(MRI)是一种超快MRI技术,通过该技术采集获得的原始图像空间分辨率较低,需要基于序列物理原理进行超分辨率重建以提高其原始图像的分辨率,而现有的基于深度学习SPEN超分辨率重建算法在提取图像像素长距离依赖关系上的能力有限.为了解决此问题,本文提出了一种基于Transformer的SPEN MRI超分辨率重建算法.该算法采用编码器-解码器结构,并引入Transformer模块以提取特征图的局部上下文信息和长距离依赖关系.实验结果表明,本文所提的重建算法可以在不增加额外采样点的情况下从SPEN低分辨率图像中重建出高空间分辨率、无混叠伪影的超分辨率图像.与现有的超分辨率算法相比,本文提出的算法在临床前以及临床数据集上都取得了更好的重建效果.

Spatio-temporal encoding(SPEN)magnetic resonance imaging(MRI)is an ultrafast MRI technique.However,resolution of the original image acquired with SPEN is relatively low,requiring super-resolution reconstruction based on sequence physics principles to improve spatial resolution.As the existing SPEN super-resolution reconstruction algorithms based on deep learning have confined abilities to capture long-range dependencies,this paper proposes a transformer-based SPEN MRI super-resolution reconstruction algorithm.An encoder-decoder structure is adopted,and a transformer module is introduced to extract local context information and long-range dependencies of feature maps.Experimental results show that the proposed reconstruction method can reconstruct a super-resolution image with high spatial resolution and no aliasing artifacts from the low-resolution SPEN image without adding additional sampling points.Compared to the existing superresolution methods,the proposed method achieves better results on both clinical and preclinical datasets.