深度学习重建在鼻咽癌5.0T MRI的图像质量与诊断效能比较
本前瞻性研究纳入70例鼻咽癌患者行5.0T MRI，对T2WI、T1WI及增强T1WI进行0-5档深度学习重建。结果显示2-5档DLR图像病灶与翼外肌SNR、CNR均显著高于常规图像，其中3档总体图像质量评分最高。DLR的3档图像T分期诊断一致性与常规图像相当。
要点
- 鼻咽癌5.0T MRI中，2-5档DLR可显著提高病灶与翼外肌SNR及CNR。
- DLR第3档总体图像质量评分最高，T分期一致性不劣于常规重建。
- DLR对伪影减少维度未显示显著优势，阅片时仍应关注伪影影响。
核心结论
- DLR技术可提高常规鼻咽5.0T MRI图像质量，且不影响T分期诊断准确性，具有潜在临床价值。
研究设计与样本
- 前瞻性研究，纳入70例鼻咽癌患者，行5.0T MRI检查。
- 扫描序列包括轴位T2WI、轴位T1WI以及轴位和冠状位增强T1WI。
- 使用DLR技术重建0-5档共六个档位图像，由两名放射科医生独立评估。
- 以临床T分期结果作为T分期评估参照。
关键影像表现/征象
- 2-5档DLR图像中病灶与翼外肌SNR较常规图像显著提高，最大改善幅度为94%。
- 轴位T2WI、轴位及冠状位增强T1WI的2-5档DLR图像中，病灶-翼外肌CNR显著提高，最大改善幅度为108%。
- 除伪影减少外，2-5档DLR图像在病灶可见性、边界清晰度及总体图像质量等主观指标上均优于常规图像。
诊断标准与关键数值
- 2-5档DLR图像中病灶与翼外肌SNR显著高于常规图像，P<0.001。
- 轴位T2WI、轴位及冠状位增强T1WI的2-5档DLR图像中，病灶-翼外肌CNR显著高于常规图像，P<0.001。
- 除伪影减少外，2-5档DLR图像主观评分均显著高于常规图像，P<0.05。
- DLR第3档图像总体图像质量评分最高。
- DLR第3档与常规图像的T分期诊断与临床T分期一致性分别为κ=0.771与0.796。
临床应用与阅片要点
- 鼻咽癌5.0T MRI阅片可优先选择DLR第3档图像，以兼顾图像质量与T分期诊断一致性。
- DLR对伪影减少未显示显著获益，判读鼻咽结构时应继续关注伪影对病变评估的干扰。
- T分期评估中DLR第3档与常规图像一致性相当，提示DLR可用于临床T分期评估流程。
局限性
- 原文未报告该研究为单中心还是多中心设计。
- 原文未报告样本量计算依据及DLR档位选择的独立验证队列。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
英文原文（摘要）· 点击展开
AIM: To investigate the effect of deep learning-based reconstruction (DLR) technology on image quality and diagnostic efficacy of 5T magnetic resonance imaging (MRI) in nasopharyngeal carcinoma (NPC).
MATERIALS AND METHODS: This prospective study included 70 NPC patients who underwent 5T MRI examinations. The protocol included axial T2-weighted imaging (T2WI), axial T1-weighted imaging (T1WI), and axial and coronal contrast-enhanced T1WIs. Images of six gear levels (0-5) were reconstructed using the DLR technology. Two radiologists independently evaluated the visibility of lesions, boundary sharpness, artefact presence, and overall image quality using a 5-point Likert scale. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were also calculated for lesions and lateral pterygoid muscles (LPM). T-stage evaluation was performed for both conventional (level = 0) and DLR images, compared to clinical T-stage results.
RESULTS: DLR images (levels 2-5) demonstrated significantly higher SNRs in lesions and LPM compared to conventional images (P < 0.001), with a maximum improvement of 94%. In axial T2WI, and axial and coronal contrast-enhanced T1WI DLR images (levels 2-5), the lesion-to-lateral pterygoid muscle CNR was significantly higher (P < 0.001), with a maximum improvement of 108%. Qualitative analysis revealed that DLR images (levels 2-5) were superior to conventional images (P < 0.05) across all subjective assessment dimensions except artefact reduction. Among the five reconstruction levels of DLR, level 3 yielded the highest overall image quality score. Additionally, the diagnostic performance of T-stage for DLR (level 3) and conventional images exhibited a comparable degree of consistency with the clinical T-stage results (κ = 0.771 and 0.796, respectively).
CONCLUSION: DLR technology improves the quality of conventional nasopharyngeal MRI images without affecting the diagnostic accuracy of T-stage, offering potential clinical value.
引用
- Zhou, Liu, Xu, et al. Deep learning-based reconstruction for 5.0T magnetic resonance imaging (MRI) in nasopharyngeal carcinoma: comparison of image quality and diagnostic efficacy. Clin Radiol. 2026. PMID: 42361579. doi:10.1016/j.crad.2026.107362
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。