评估加速肺部 MR 成像用于利用人工智能辅助压缩感知检测和表征肺结节的诊断可行性。在这项前瞻性试验中，2021 年 12 月至 2022 年 12 月期间入院的良性和恶性肺结节患者接受了胸部 CT 和肺部 MRI。肺部 MRI 使用呼吸门控 3D 梯度回波序列，通过并行成像、压缩感知和深度学习图像重建的组合进行加速，并具有三种不同的加速因子（CS-AI-7、CS-AI-10 和 CS-AI） -15）。两名读者在盲法环境中与 CT 相比，评估了所有序列的图像质量（5 点李克特量表）、结节检测和特征（大小和形态）。使用组内相关系数 (ICC) 确定读者一致性。 37 名患者有 64 个肺结节（实性 n = 57 [3-107 mm] 部分实性 n = 6 [磨砂玻璃/实性 8 mm/4-28 mm /16 mm]毛玻璃结节n = 1 [20 mm]）进行了分析。标称扫描时间为 CS-AI-7 3:53 分钟； CS-AI-10 2:34分钟； CS-AI-15 1:50分钟CS-AI-7 显示出更高的图像质量，而 CS-AI-15 的质量仍保持诊断性。 CS-AI 因子 7、10 和 15 的肺结节检出率分别为 100%、98.4% 和 96.8%。结节形态在最低加速度下最佳，只有 5% 的病例低于 CT，而 CS-AI-10 为 10%，CS-AI-15 为 23%。所有序列的结节大小具有可比性，与 CT 大小平均偏差<<1mm。压缩传感与 AI​​ 的结合可以大幅减少肺部 MRI 的扫描时间，同时保持肺结节的高检出率。肺部 MRI 中的传感和人工智能可显着节省时间，且不会影响结节检测或特征。这一进步具有临床前景，可在不牺牲诊断质量的情况下提高肺癌筛查的效率。通过 MRI 进行肺癌筛查是可能的，但将受益于扫描时间优化。在不同的加速因子下，显着减少了扫描时间、提高了检测率并保留了结节特征。将压缩传感和人工智能集成到肺部 MRI 中，可以在不影响诊断质量的情况下进行高效的肺癌筛查。© 2024。作者。

Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15). Two readers evaluated image quality (5-point Likert scale), nodule detection and characterisation (size and morphology) of all sequences compared to CT in a blinded setting. Reader agreement was determined using the intraclass correlation coefficient (ICC).Thirty-seven patients with 64 pulmonary nodules (solid n = 57 [3-107 mm] part-solid n = 6 [ground glass/solid 8 mm/4-28 mm/16 mm] ground glass nodule n = 1 [20 mm]) were analysed. Nominal scan times were CS-AI-7 3:53 min; CS-AI-10 2:34 min; CS-AI-15 1:50 min. CS-AI-7 showed higher image quality, while quality remained diagnostic even for CS-AI-15. Detection rates of pulmonary nodules were 100%, 98.4%, and 96.8% for CS-AI factors 7, 10, and 15, respectively. Nodule morphology was best at the lowest acceleration and was inferior to CT in only 5% of cases, compared to 10% for CS-AI-10 and 23% for CS-AI-15. The nodule size was comparable for all sequences and deviated on average < 1 mm from the CT size.The combination of compressed sensing and AI enables a substantial reduction in the scan time of lung MRI while maintaining a high detection rate of pulmonary nodules.Incorporating compressed sensing and AI in pulmonary MRI achieves significant time savings without compromising nodule detection or characteristics. This advancement holds clinical promise, enhancing efficiency in lung cancer screening without sacrificing diagnostic quality.Lung cancer screening by MRI may be possible but would benefit from scan time optimisation. Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors. Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.© 2024. The Author(s).