Synthesizing Quantitative T2 Maps in Right Lateral Knee Femoral Condyles from Multicontrast Anatomic Data with a Conditional Generative Adversarial Network.
Synthesizing Quantitative T2 Maps in Right Lateral Knee Femoral Condyles from Multicontrast Anatomic Data with a Conditional Generative Adversarial Network.
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DOI：
10.1148/ryai.2021200122
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发表时间：
2021-05
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通讯作者：
B. Sveinsson;A. Chaudhari;Bo Zhu;Neha Koonjoo;M. Torriani;G. Gold;M. Rosen
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作者：
B. Sveinsson;A. Chaudhari;Bo Zhu;Neha Koonjoo;M. Torriani;G. Gold;M. Rosen
目的 开发概念验证卷积神经网络 (CNN)，通过使用条件生成对抗网络 (cGAN) 从解剖 MR 图像合成右侧股骨髁关节软骨中的 T2 图。材料和方法 在这项回顾性研究中，2004-2006 年骨关节炎计划中纳入的 4621 名患者的右膝解剖图像（来自稳态扫描中的涡轮自旋回波和双回波）被用作基于 cGAN 的 CNN 的输入，并生成预测的 CNN T2 作为输出。这些患者包括所有种族的男性和女性，年龄在 45-79 岁，患有膝骨关节炎或处于膝骨关节炎发病或进展的高风险，他们是在美国四个不同的中心招募的。这些数据分为 3703 个（80%）用于训练，462 个（10%）用于验证，456 个（10%）用于测试。在测试数据集中的多回波自旋回波 (MESE) 和 CNN T2 之间进行线性回归分析。通过两名肌肉骨骼放射科医生的评估和软骨分区的量化，对 30 名随机选择的患者进行了更详细的分析。通过使用双边 t 检验来比较放射科医生的评估。结果 读者在区分 CNN T2 和 MESE T2 方面的准确度中等，其中一名读者进行了随机机会分类。 CNN T2 值与 30 名患者的分区以及所有患者的批量分析中的 MESE 值相关，最佳拟合线斜率在 0.55 至 0.83 之间。结论 使用基于神经网络的 cGAN 方法，可以从解剖 MRI 序列合成股骨软骨中的 T2 图，与 MESE 扫描具有良好的一致性。另请参阅 Yi 和 Fritz 在本期中的评论。关键词：软骨成像、膝关节、实验研究、量化、视觉、应用领域、卷积神经网络 (CNN)、深度学习算法、机器学习算法©北美放射学会，2021 年。
Purpose To develop a proof-of-concept convolutional neural network (CNN) to synthesize T2 maps in right lateral femoral condyle articular cartilage from anatomic MR images by using a conditional generative adversarial network (cGAN). Materials and Methods In this retrospective study, anatomic images (from turbo spin-echo and double-echo in steady-state scans) of the right knee of 4621 patients included in the 2004-2006 Osteoarthritis Initiative were used as input to a cGAN-based CNN, and a predicted CNN T2 was generated as output. These patients included men and women of all ethnicities, aged 45-79 years, with or at high risk for knee osteoarthritis incidence or progression who were recruited at four separate centers in the United States. These data were split into 3703 (80%) for training, 462 (10%) for validation, and 456 (10%) for testing. Linear regression analysis was performed between the multiecho spin-echo (MESE) and CNN T2 in the test dataset. A more detailed analysis was performed in 30 randomly selected patients by means of evaluation by two musculoskeletal radiologists and quantification of cartilage subregions. Radiologist assessments were compared by using two-sided t tests. Results The readers were moderately accurate in distinguishing CNN T2 from MESE T2, with one reader having random-chance categorization. CNN T2 values were correlated to the MESE values in the subregions of 30 patients and in the bulk analysis of all patients, with best-fit line slopes between 0.55 and 0.83. Conclusion With use of a neural network-based cGAN approach, it is feasible to synthesize T2 maps in femoral cartilage from anatomic MRI sequences, giving good agreement with MESE scans.See also commentary by Yi and Fritz in this issue.Keywords: Cartilage Imaging, Knee, Experimental Investigations, Quantification, Vision, Application Domain, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms© RSNA, 2021.