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Received:October 31, 2023 Published Online:August 20, 2024
Received:October 31, 2023 Published Online:August 20, 2024
中文摘要: 目的 探讨深度学习重建算法（DLR）在肩关节MRI中提高图像质量和缩短扫描时间的可行性与临床价值。方法 前瞻性纳入2023年6月至10月期间在南京医科大学第四附属医院的50例疑似患有肩关节病变的患者，采用1.5T MRI行常规序列扫描图像为Fsecon组，使用并行采集加速因子2的扫描图像为Fsefast组，扫描序列包括脂肪抑制质子加权像（PDWI-FS）和T1加权像（T1WI），将Fsefast组传至Subtle MRTM dlr后获得图像Fsedlr组。测量三组图像中的冈上肌、肱二头肌长头肌腱、盂唇软骨、肱骨骨髓的信号噪声比（SNR）及冈上肌/盂唇软骨的对比噪声比（CNR）并进行比较，两名放射科医师双盲采用Likert 4分法分别对Fsedlr组与Fsecon组的图像清晰度和伪影进行主观评价，并对这两组的病理异常结构进行诊断效能对比。结果 相对于Fsecon组，Fsedlr组扫描时间缩短了44%，且图像清晰度评分、伪影评分均增高，差异有统计学意义（P＜0.05），两名医师主观评分组内相关性系数为0.797~0.919。客观评价指标中，Fsedlr组的SNR和CNR均明显高于Fsecon组与Fsefast组，差异均有统计学意义（P＜0.05）。在两位医师对Fsecon组与FSEdlr组病理异常结构的评估中，两组的诊断结果均有较好的一致性（Kappa值：0.675~1.000），在同一名医师的评估中也显示出极好的一致性（Kappa值：0.771~1.000），其中肱骨骨髓、关节滑囊、肱二头肌长头肌腱的Kappa值均高于0.8。结论 将DLR算法应用于肩关节MRI检查中，能够提高图像质量、缩短图像采集时间，并保证诊断效能，提高检查效率，具有较好的临床价值。
Abstract:Objective To investigate the feasibility and clinical value of the deep learning reconstruction (DLR) algorithm in enhancing image quality and reducing scan time in shoulder MRI. Methods Fifty patients suspected of having shoulder joint lesions in the Fourth Affiliated Hospital of Nanjing Medical University from June to October 2023 were prospectively included. Routine sequence scanning images with 1.5T MRI were grouped as Fsecon, while scanning images with parallel acquisition acceleration factor 2 were grouped as Fsefast. The scanning sequences included fat-suppression proton density-weighted imaging (PDWI-FS) and T1 weighted imaging (T1WI). The Fsefast group was then transferred to Subtle MRTM dlr to obtain images in the Fsedlr group. The signal-to-noise ratio (SNR) of supraspinatus muscle, long head tendon of biceps brachii, glenoid labrum cartilage, and humerus marrow, as well as the contrast-to-noise ratio (CNR) of supraspinatus muscle/glenoid labrum cartilage, were measured and compared among the three groups. Two radiologists blindly evaluated the image clarity and artifacts of the Fsedlr group and the Fsecon group using the Likert 4-point scale, and compared the diagnostic efficacy of pathological abnormal structures between the two groups. Results Compared with the Fsecon group, the scan time of the Fsedlr group was shortened by 44%, and the image clarity and artifact scores were higher, with statistically significant differences (P＜0.05). The intraclass correlation coefficients（ICC）for subjective scores within the two radiologists' groups were 0.797 to 0.919. Among the objective evaluation indicators, the SNR and CNR of the Fsedlr group were significantly higher than those of the Fsecon group and the Fsefast group, with statistically significant differences (P＜0.05). In the evaluation of the pathological abnormal structures of the Fsecon group and the Fsedlr group by two radiologists, the diagnostic results of the two groups were consistent (Kappa value: 0.675-1.000), and also showed excellent consistency in the evaluation of the same radiologist (Kappa value: 0.771-1.000), among which the Kappa values of humerus bone marrow, joint bursa and long head tendon of biceps brachii were higher than 0.8. Conclusion The application of the DLR algorithm in shoulder MRI examination can improve image quality, shorten image acquisition time, ensure diagnostic efficacy, and improve examination efficiency, demonstrating good clinical value.
keywords: Deep learning reconstruction Shoulder joint Magnetic resonance image Signal-to-noise ratio Contrast-to-noise ratio Image quality
文章编号： 中图分类号：R445.2 文献标志码：A
基金项目:江苏省“十三五”强卫工程青年医学重点人才（QNRC2016041）；南京医科大学科技发展基金一般项目（NMUB20230037）
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