Kang Yimeng, Li Wenjing, Lv Qingqing, Tao Qiuying, Sun Jieping, Dang Jinghan, Niu Xiaoyu, Liu Zijun, Li Shujian, Zhang Zanxia, Wang Kaiyu, Wen Baohong, Cheng Jingliang, Zhang Yong, Wang Weijian
Department of Magnetic Resonance Imaging, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China.
Department of Radiology, The Third Affiliated , Zhengzhou University, Zhengzhou, 450052, China.
BMC Med Imaging. 2025 Jan 13;25(1):17. doi: 10.1186/s12880-025-01554-y.
Conventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. Leveraging deep learning-based reconstruction (DLR) holds the potential to mitigate scan time without compromising image quality.
We enrolled a cohort of sixty patients who underwent DL-MRI, conventional MRI, and No-DL MRI examinations to evaluate image quality. Key metrics considered in the assessment included scan duration, overall image quality, quantitative assessments of Relative Signal-to-Noise Ratio (rSNR), Relative Contrast-to-Noise Ratio (rCNR), and diagnostic efficacy. Two experienced radiologists independently assessed image quality using a 5-point scale (5 indicating the highest quality). To gauge interobserver agreement for the assessed pathologies across image sets, we employed weighted kappa statistics. Additionally, the Wilcoxon signed rank test was employed to compare image quality and quantitative rSNR and rCNR measurements.
Scan time was significantly reduced with DL-MRI and represented an approximate 66.5% reduction. DL-MRI consistently exhibited superior image quality in both coronal T2WI and axial T2WI when compared to both conventional MRI (p < 0.01) and No-DL-MRI (p < 0.01). Interobserver agreement was robust, with kappa values exceeding 0.735. For rSNR data, coronal fat-saturated(FS) T2WI and axial FS T2WI in DL-MRI consistently outperformed No-DL-MRI, with statistical significance (p < 0.01) observed in all cases. Similarly, rCNR data revealed significant improvements (p < 0.01) in coronal FS T2WI of DL-MRI when compared to No-DL-MRI. Importantly, our findings indicated that DL-MRI demonstrated diagnostic performance comparable to conventional MRI.
Integrating deep learning-based reconstruction methods into standard clinical workflows has the potential to the promise of accelerating image acquisition, enhancing image clarity, and increasing patient throughput, thereby optimizing diagnostic efficiency.
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传统的髋关节MRI扫描需要较长的扫描时间，这给患者舒适度和临床效率带来了挑战。以前，加速成像技术受到噪声和分辨率之间权衡的限制。利用基于深度学习的重建（DLR）有潜力在不影响图像质量的情况下减少扫描时间。
我们招募了一组60名接受DL-MRI、传统MRI和非DL-MRI检查的患者，以评估图像质量。评估中考虑的关键指标包括扫描时间、整体图像质量、相对信噪比（rSNR）、相对对比噪声比（rCNR）的定量评估以及诊断效能。两位经验丰富的放射科医生使用5分制（5表示最高质量）独立评估图像质量。为了衡量不同图像集间观察者对所评估病变的一致性，我们采用加权kappa统计量。此外，采用Wilcoxon符号秩检验来比较图像质量以及定量的rSNR和rCNR测量值。
DL-MRI显著缩短了扫描时间，减少了约66.5%。与传统MRI（p < 0.01）和非DL-MRI（p < 0.01）相比，DL-MRI在冠状位T2WI和轴位T2WI上始终表现出更高的图像质量。观察者间一致性很强，kappa值超过0.735。对于rSNR数据，DL-MRI的冠状位脂肪饱和（FS）T2WI和轴位FS T2WI始终优于非DL-MRI，所有情况下均具有统计学意义（p < 0.01）。同样，rCNR数据显示，与非DL-MRI相比，DL-MRI的冠状位FS T2WI有显著改善（p < 0.01）。重要的是，我们的研究结果表明DL-MRI的诊断性能与传统MRI相当。
将基于深度学习的重建方法整合到标准临床工作流程中，有可能加快图像采集速度、提高图像清晰度并增加患者通量，从而优化诊断效率。
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