Deep Learning–Based Super-Resolution Reconstruction on Undersampled Brain Diffusion-Weighted MRI for Infarction Stroke: A Comparison to Conventional Iterative Reconstruction

作者：Shuo Zhang, Meimeng Zhong, Hanxu Shenliu, Nan Wang, Shuai Hu, Xulun Lu, Liangjie Lin, Haonan Zhang, Yan Zhao, Chao Yang, Hongbo Feng, Qingwei Song · 发表于：American Journal of Neuroradiology · 年份：2024 · DOI：10.3174/ajnr.a8482 · 被引用次数：9 · 研究领域：Advanced MRI Techniques and Applications、Advanced Neuroimaging Techniques and Applications、Advanced Image Processing Techniques

BACKGROUND AND PURPOSE: DWI is crucial for detecting infarction stroke. However, its spatial resolution is often limited, hindering accurate lesion visualization. Our aim was to evaluate the image quality and diagnostic confidence of deep learning (DL)-based super-resolution reconstruction for brain DWI of infarction stroke. MATERIALS AND METHODS: This retrospective study enrolled 114 consecutive participants who underwent brain DWI. The DWI images were reconstructed with 2 schemes: 1) DL-based super-resolution reconstruction (DWI DL ); and 2) conventional compressed sensing reconstruction (DWI CS ). Qualitative image analysis included overall image quality, lesion conspicuity, and diagnostic confidence in infarction stroke of different lesion sizes. Quantitative image quality assessments were performed by measurements of SNR, contrast-to-noise ratio (CNR), ADC, and edge rise distance. Group comparisons were conducted by using a paired t test for normally distributed data and the Wilcoxon test for non-normally distributed data. The overall agreement between readers for qualitative ratings was assessed by using the Cohen κ coefficient. A P value less than .05 was considered statistically significant. RESULTS: A total of 114 DWI examinations constituted the study cohort. For the qualitative assessment, overall image quality, lesion conspicuity, and diagnostic confidence in infarction stroke lesions (lesion size <1.5 cm) improved by DWI DL compared with DWI CS (a...