目的:利用人工智能辅助压缩感知评估加速肺MR成像对肺结节检测和表征的诊断可行性。
方法:在这项前瞻性试验中,2021年12月至2022年12月期间收治的良性和恶性肺结节患者接受了胸部CT和肺部MRI检查.肺MRI使用呼吸门控的3D梯度回波序列,通过并行成像的组合加速,压缩传感,以及具有三种不同加速因子(CS-AI-7、CS-AI-10和CS-AI-15)的深度学习图像重建。两名读者评估了图像质量(5点李克特量表),与盲态CT相比,所有序列的结节检测和表征(大小和形态)。使用组内相关系数(ICC)确定读者一致性。
结果:分析了37例患者的64个肺结节(实性n=57[3-107mm]部分实性n=6[磨砂玻璃/实性8mm/4-28mm/16mm]磨砂玻璃结节n=1[20mm])。标称扫描时间为CS-AI-73:53分钟;CS-AI-102:34分钟;CS-AI-151:50分钟。CS-AI-7显示出更高的图像质量,而即使对于CS-AI-15,质量仍然具有诊断性。肺结节检出率为100%,98.4%,CS-AI因子7、10和15分别为96.8%。结节形态在最低加速度时最好,仅在5%的病例中劣于CT,相比之下,CS-AI-10为10%,CS-AI-15为23%。对于所有序列,结节大小是相当的,并且与CT大小平均偏离<1mm。
结论:压缩感知和AI的组合可以显着减少肺部MRI的扫描时间,同时保持对肺结节的高检出率。
结论:在肺MRI中结合压缩感知和AI可以在不影响结节检测或特征的情况下节省大量时间。这一进展具有临床前景,在不牺牲诊断质量的情况下提高肺癌筛查的效率。
结论:通过MRI筛查肺癌可能是可能的,但将受益于扫描时间优化。显著减少扫描时间,高检测率,并且在不同的加速因子上实现了保留的结节特征。在肺部MRI中集成压缩传感和AI可在不影响诊断质量的情况下提供有效的肺癌筛查。
OBJECTIVE: Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.