深度学习增强的膝关节 MRI 并行成像和同时多层加速重建。
Deep Learning-Enhanced Parallel Imaging and Simultaneous Multislice Acceleration Reconstruction in Knee MRI.
机构信息
From the School of Biomedical Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University.
Department of Orthopaedic Surgery, Research Institute for Convergence of Biomedical Science and Technology.
出版信息
Invest Radiol. 2022 Dec 1;57(12):826-833. doi: 10.1097/RLI.0000000000000900. Epub 2022 Jul 1.
OBJECTIVES
This study aimed to examine various combinations of parallel imaging (PI) and simultaneous multislice (SMS) acceleration imaging using deep learning (DL)-enhanced and conventional reconstruction. The study also aimed at comparing the diagnostic performance of the various combinations in internal knee derangement and provided a quantitative evaluation of image sharpness and noise using edge rise distance (ERD) and noise power (NP), respectively.
MATERIALS AND METHODS
The data from adult patients who underwent knee magnetic resonance imaging using various DL-enhanced acquisitions between June 2021 and January 2022 were retrospectively analyzed. The participants underwent conventional 2-fold PI and DL protocols with 4- to 8-fold acceleration imaging (P2S2 [2-fold PI with 2-fold SMS], P3S2, and P4S2). Three readers evaluated the internal knee derangement and the overall image quality. The diagnostic performance was calculated using consensus reading as a standard reference, and we conducted comparative evaluations. We calculated the ERD and NP for quantitative evaluations of image sharpness and noise, respectively. Interreader and intermethod agreements were calculated using Fleiss κ.
RESULTS
A total of 33 patients (mean age, 49 ± 19 years; 20 women) were included in this study. The diagnostic performance for internal knee derangement and the overall image quality were similar among the evaluated protocols. The NP values were significantly lower using the DL protocols than with conventional imaging ( P < 0.001), whereas the ERD values were similar among these methods ( P > 0.12). Interreader and intermethod agreements were moderate-to-excellent (κ = 0.574-0.838) and good-to-excellent (κ = 0.755-1.000), respectively. In addition, the mean acquisition time was reduced by 47% when using DL with P2S2, by 62% with P3S2, and by 71% with P4S2, compared with conventional P2 imaging (2 minutes and 55 seconds).
CONCLUSIONS
The combined use of DL-enhanced 8-fold acceleration imaging (4-fold PI with 2-fold SMS) showed comparable performance with conventional 2-fold PI for the evaluation of internal knee derangement, with a 71% reduction in acquisition time.
目的
本研究旨在使用深度学习（DL）增强和常规重建检查并行成像（PI）和同时多层（SMS）加速成像的各种组合。本研究还旨在比较各种组合在膝关节内部紊乱诊断中的性能，并使用边缘上升距离（ERD）和噪声功率（NP）分别对图像锐度和噪声进行定量评估。
材料和方法
回顾性分析了 2021 年 6 月至 2022 年 1 月期间使用各种 DL 增强采集进行膝关节磁共振成像的成年患者的数据。参与者接受了常规的 2 倍 PI 和 4-8 倍加速成像（P2S2[2 倍 PI 与 2 倍 SMS]、P3S2 和 P4S2）的 DL 协议。三位读者评估了膝关节内部紊乱和整体图像质量。使用共识阅读作为标准参考计算诊断性能，并进行了比较评估。我们分别使用 ERD 和 NP 计算图像锐度和噪声的定量评估。使用 Fleiss κ 计算读者间和方法间的一致性。
结果
本研究共纳入 33 名患者（平均年龄 49±19 岁，20 名女性）。对于膝关节内部紊乱和整体图像质量的评估，各评估方案的诊断性能相似。与常规成像相比，DL 方案的 NP 值显著降低（P<0.001），而这些方法的 ERD 值相似（P>0.12）。读者间和方法间的一致性为中等至极好（κ=0.574-0.838）和极好（κ=0.755-1.000）。此外，与常规的 P2 成像相比，使用 P2S2 时 DL 的平均采集时间减少了 47%，使用 P3S2 时减少了 62%，使用 P4S2 时减少了 71%。
结论
与传统的 2 倍 PI 相比，DL 增强的 8 倍加速成像（4 倍 PI 与 2 倍 SMS）联合使用在评估膝关节内部紊乱方面具有相当的性能，同时采集时间减少了 71%。
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