Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study.
Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study.
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DOI：
10.2214/ajr.20.23313
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发表时间：
2020-12
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通讯作者：
Zitnick CL
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作者：
Recht MP;Zbontar J;Sodickson DK;Knoll F;Yakubova N;Sriram A;Murrell T;Defazio A;Rabbat M;Rybak L;Kline M;Ciavarra G;Alaia EF;Samim M;Walter WR;Lin DJ;Lui YW;Muckley M;Huang Z;Johnson P;Stern R;Zitnick CL
深度学习（DL）图像重建有可能通过显着减少MRI检查所需的时间来破坏MRI的当前状态。我们的目标是使用DL加速MRI，以便在不影响图像质量或诊断准确性的情况下对膝关节进行5分钟的全面检查。利用变分网络对图像重建的DL模型进行了优化。该模型使用专用的多序列训练进行训练，其中单个重建模型使用来自具有不同对比度和方向的多个序列的数据进行训练。训练后，对108名患者的数据进行了回顾性欠采样，其方式相当于完全采样数据采集的净加速3.49倍，与我们的标准两倍加速并行采集相比，加速1.88倍。进行了一项可重复性研究，其中比较了6名阅片员检测临床和DL加速图像的膝关节内部紊乱的能力。我们发现标准和DL加速图像之间的可重复性很高。特别是，结果表明，互换序列将产生不一致的临床意见不超过4%的时间为任何功能评价。此外，所有6名阅片人均认为加速序列的质量优于临床序列。优化的DL模型允许加速膝关节图像，其与标准图像互换执行，用于检测膝关节的内部紊乱。重要的是，读片者更喜欢加速图像的质量而不是标准临床图像的质量。
Deep learning (DL) image reconstruction has the potential to disrupt the current state of MRI by significantly decreasing the time required for MRI examinations. Our goal was to use DL to accelerate MRI to allow a 5-minute comprehensive examination of the knee without compromising image quality or diagnostic accuracy. A DL model for image reconstruction using a variational network was optimized. The model was trained using dedicated multisequence training, in which a single reconstruction model was trained with data from multiple sequences with different contrast and orientations. After training, data from 108 patients were retrospectively undersampled in a manner that would correspond with a net 3.49-fold acceleration of fully sampled data acquisition and a 1.88-fold acceleration compared with our standard twofold accelerated parallel acquisition. An interchangeability study was performed, in which the ability of six readers to detect internal derangement of the knee was compared for clinical and DL-accelerated images. We found a high degree of interchangeability between standard and DL-accelerated images. In particular, results showed that interchanging the sequences would produce discordant clinical opinions no more than 4% of the time for any feature evaluated. Moreover, the accelerated sequence was judged by all six readers to have better quality than the clinical sequence. An optimized DL model allowed acceleration of knee images that performed interchangeably with standard images for detection of internal derangement of the knee. Importantly, readers preferred the quality of accelerated images to that of standard clinical images.