MR-based synthetic CT generation using a deep convolutional neural network method
MR-based synthetic CT generation using a deep convolutional neural network method
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DOI：
10.1002/mp.12155
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发表时间：
2017-04-01
期刊：
影响因子：
3.8
通讯作者：
Han, Xiao
中科院分区：
文献类型：
--
作者：
Han, Xiao
目的：由于MRI提供的上级软组织对比度以及减少不必要的辐射剂量的期望，在放射治疗领域中，用磁共振成像（MRI）代替CT的兴趣已经迅速增长。仅MR放疗还简化了临床工作流程，并避免了MR与CT对齐的不确定性。然而，需要从患者MR图像中获得CT等效表示（通常称为合成CT（sCT））的方法，用于剂量计算和基于DR的患者定位。合成CT估计对于混合PET-MR系统中的PET衰减校正也是重要的。在这项工作中，我们提出了一种新的深度卷积神经网络（DCNN）的sCT生成方法，并评估其性能的一组脑肿瘤患者images.Methods：所提出的方法建立在深度学习和卷积神经网络在计算机视觉文献的最新发展。提出的DCNN模型有27个卷积层，与池化和非池化层交织，以及3500万个自由参数，可以训练这些参数来学习从MR图像到其相应CT的直接端到端映射。在我们有限的数据上训练如此大的模型是通过迁移学习的原理和从预训练模型初始化模型权重来实现的。18例脑肿瘤患者的CT和T1加权MR图像被用作实验数据，并进行了六重交叉验证研究。将生成的每个sCT与同一患者的真实的CT图像逐个体素进行比较。比较还作出了一个基于图集的方法，涉及变形图集注册和补丁为基础的图集fusion.Results：建议的DCNN方法产生的平均绝对误差（MAE）低于85 HU的13个测试科目。所有受试者的总体平均MAE为84.8 +/- 17.3 HU，显著优于基于图谱的方法的平均MAE 94.5 +/- 17.8 HU。当使用其他两个指标进行评估时，DCNN方法也提供了更好的准确性：均方误差（188.6 +/- 33.7 vs 198.3 +/- 33.0）和Pearson相关系数（0.906 +/- 0.03 vs 0.896 +/- 0.03）。虽然训练DCNN模型可能很慢，但训练只需要完成一次。应用训练模型为每个新的患者MR图像生成一个完整的sCT体积只需要9 s，这是比atlas为基础的approach.Conclusions快得多：一个DCNN模型方法的开发，并被证明能够产生高度准确的sCT估计从传统的，单序列的MR图像在近真实的时间。定量结果还表明，所提出的方法与基于地图集的方法在测试时的准确性和计算速度方面具有良好的竞争力。需要进一步验证剂量计算准确性和更大的患者队列。所述方法的扩展也可以进一步提高准确度或处理多序列MR图像。(C)2017年美国医学物理学家协会
Purpose: Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for dose calculation and DRR-based patient positioning. Synthetic CT estimation is also important for PET attenuation correction in hybrid PET-MR systems. We propose in this work a novel deep convolutional neural network (DCNN) method for sCT generation and evaluate its performance on a set of brain tumor patient images.Methods: The proposed method builds upon recent developments of deep learning and convolutional neural networks in the computer vision literature. The proposed DCNN model has 27 convolutional layers interleaved with pooling and unpooling layers and 35 million free parameters, which can be trained to learn a direct end-to-end mapping from MR images to their corresponding CTs. Training such a large model on our limited data is made possible through the principle of transfer learning and by initializing model weights from a pretrained model. Eighteen brain tumor patients with both CT and T1-weighted MR images are used as experimental data and a sixfold cross-validation study is performed. Each sCT generated is compared against the real CT image of the same patient on a voxel-by-voxel basis. Comparison is also made with respect to an atlas-based approach that involves deformable atlas registration and patch-based atlas fusion.Results: The proposed DCNN method produced a mean absolute error (MAE) below 85 HU for 13 of the 18 test subjects. The overall average MAE was 84.8 +/- 17.3 HU for all subjects, which was found to be significantly better than the average MAE of 94.5 +/- 17.8 HU for the atlas-based method. The DCNN method also provided significantly better accuracy when being evaluated using two other metrics: the mean squared error (188.6 +/- 33.7 versus 198.3 +/- 33.0) and the Pearson correlation coefficient(0.906 +/- 0.03 versus 0.896 +/- 0.03). Although training a DCNN model can be slow, training only need be done once. Applying a trained model to generate a complete sCT volume for each new patient MR image only took 9 s, which was much faster than the atlas-based approach.Conclusions: A DCNN model method was developed, and shown to be able to produce highly accurate sCT estimations from conventional, single-sequence MR images in near real time. Quantitative results also showed that the proposed method competed favorably with an atlas-based method, in terms of both accuracy and computation speed at test time. Further validation on dose computation accuracy and on a larger patient cohort is warranted. Extensions of the method are also possible to further improve accuracy or to handle multi-sequence MR images. (C) 2017 American Association of Physicists in Medicine