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基于深度学习超分辨率的便携式低场强MRI定量缺血性病变

Quantitative Ischemic Lesions of Portable Low-Field Strength MRI Using Deep Learning-Based Super-Resolution.

作者信息

Bian Yueyan, Wang Long, Li Jin, Yang Xiaoxu, Wang Erling, Li Yingying, Liu Yuehong, Xiang Lei, Yang Qi

机构信息

Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, China (Y.B., J.L., X.Y., E.W., Y. Li, Y. Liu, Q.Y.).

Department of Research and Development, Subtle Medical, Shanghai, China (L.W., L.X.).

出版信息

Stroke. 2025 Jul;56(7):1843-1852. doi: 10.1161/STROKEAHA.124.050540. Epub 2025 Apr 16.

Abstract

BACKGROUND

Deep learning-based synthetic super-resolution magnetic resonance imaging (SynthMRI) may improve the quantitative lesion performance of portable low-field strength magnetic resonance imaging (LF-MRI). The aim of this study is to evaluate whether SynthMRI improves the diagnostic performance of LF-MRI in assessing ischemic lesions.

METHODS

We retrospectively included 178 stroke patients and 104 healthy controls with both LF-MRI and high-field strength magnetic resonance imaging (HF-MRI) examinations. Using HF-MRI as the ground truth, the deep learning-based super-resolution framework (SCUNet [Swin-Conv-UNet]) was pretrained using large-scale open-source data sets to generate SynthMRI images from LF-MRI images. Participants were split into a training set (64.2%) to fine-tune the pretrained SCUNet, and a testing set (35.8%) to evaluate the performance of SynthMRI. Sensitivity and specificity of LF-MRI and SynthMRI were assessed. Agreement with HF-MRI for Alberta Stroke Program Early CT Score in the anterior and posterior circulation (diffusion-weighted imaging-Alberta Stroke Program Early CT Score and diffusion-weighted imaging-posterior circulation Alberta Stroke Program Early CT Score) was evaluated using intraclass correlation coefficients (ICCs). Agreement with HF-MRI for lesion volume and mean apparent diffusion coefficient (ADC) within lesions was assessed using both ICCs and Pearson correlation coefficients.

RESULTS

SynthMRI demonstrated significantly higher sensitivity and specificity than LF-MRI (89.0% [83.3%-94.6%] versus 77.1% [69.5%-84.7%]; <0.001 and 91.3% [84.7%-98.0%] versus 71.0% [60.3%-81.7%]; <0.001, respectively). The ICCs of diffusion-weighted imaging-Alberta Stroke Program Early CT Score between SynthMRI and HF-MRI were also better than that between LF-MRI and HF-MRI (0.952 [0.920-0.972] versus 0.797 [0.678-0.876], <0.001). For lesion volume and mean apparent diffusion coefficient within lesions, SynthMRI showed significantly higher agreement (<0.001) with HF-MRI (ICC>0.85, >0.78) than LF-MRI (ICC>0.45, >0.35). Furthermore, for lesions during various poststroke phases, SynthMRI exhibited significantly higher agreement with HF-MRI than LF-MRI during the early hyperacute and subacute phases.

CONCLUSIONS

SynthMRI demonstrates high agreement with HF-MRI in detecting and quantifying ischemic lesions and is better than LF-MRI, particularly for lesions during the early hyperacute and subacute phases.

摘要

背景

基于深度学习的合成超分辨率磁共振成像(SynthMRI)可能会提高便携式低场强磁共振成像(LF-MRI)对病变的定量评估性能。本研究旨在评估SynthMRI是否能提高LF-MRI在评估缺血性病变中的诊断性能。

方法

我们回顾性纳入了178例中风患者和104例健康对照者,他们均接受了LF-MRI和高场强磁共振成像(HF-MRI)检查。以HF-MRI作为金标准,基于深度学习的超分辨率框架(SCUNet [Swin-Conv-UNet])使用大规模开源数据集进行预训练,以从LF-MRI图像生成SynthMRI图像。参与者被分为训练集(64.2%)用于对预训练的SCUNet进行微调,以及测试集(35.8%)用于评估SynthMRI的性能。评估了LF-MRI和SynthMRI的敏感性和特异性。使用组内相关系数(ICC)评估SynthMRI和LF-MRI与HF-MRI在阿尔伯塔卒中项目早期CT评分(前循环和后循环的扩散加权成像-阿尔伯塔卒中项目早期CT评分以及扩散加权成像-后循环阿尔伯塔卒中项目早期CT评分)方面的一致性。使用ICC和Pearson相关系数评估SynthMRI和LF-MRI与HF-MRI在病变体积和病变内平均表观扩散系数(ADC)方面的一致性。

结果

SynthMRI显示出比LF-MRI显著更高的敏感性和特异性(89.0% [83.3%-94.6%] 对 77.1% [69.5%-84.7%];<0.001以及91.3% [84.7%-98.0%] 对 71.0% [60.3%-81.7%];分别为<0.001)。SynthMRI与HF-MRI之间的扩散加权成像-阿尔伯塔卒中项目早期CT评分的ICC也优于LF-MRI与HF-MRI之间的ICC(0.952 [0.920-0.972] 对 0.797 [0.678-0.876],<0.001)。对于病变体积和病变内平均表观扩散系数,SynthMRI与HF-MRI的一致性显著高于LF-MRI(<0.001)(ICC>0.85,>0.78)(ICC>0.45,>0.35)。此外,对于中风后各阶段的病变,在超急性期和亚急性期早期,SynthMRI与HF-MRI的一致性显著高于LF-MRI。

结论

SynthMRI在检测和量化缺血性病变方面与HF-MRI具有高度一致性,且优于LF-MRI,特别是对于超急性期和亚急性期早期的病变。

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