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首页> 外文会议>IEEE Nuclear Science Symposium;Medical Imaging Conference >Deep learning for MRI-based CT synthesis: a comparison of MRI sequences and neural network architectures
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Deep learning for MRI-based CT synthesis: a comparison of MRI sequences and neural network architectures

机译：基于MRI的CT合成的深度学习：MRI序列和神经网络架构的比较

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Synthetic computed tomography (CT) images derived from magnetic resonance images (MRI) are of interest for radiotherapy planning and positron emission tomography (PET) attenuation correction. In recent years, deep learning implementations have demonstrated improvement over atlas-based and segmentation-based methods. Nevertheless, several open questions remain to be addressed, such as which is the best of MRI sequences and neural network architectures. In this work, we compared the performance of different combinations of two common MRI sequences (T1- and T2-weighted), and three state-of-the-art neural networks designed for medical image processing (Vnet, HighRes3dNet and ScaleNet). The experiments were conducted on brain datasets from a public database. Our results suggest that T1 images perform better than T2, but the results further improve when combining both sequences. The lowest mean average error over the entire head (MAE = 101.76 ± 10.4 HU) was achieved combining T1 and T2 scans with HighRes3dNet. All tested deep learning models achieved significantly lower MAE (p < 0.01) than a well-known atlas-based method.
机译：源自磁共振图像（MRI）的合成计算机断层扫描（CT）图像对于放射治疗计划和正电子发射断层扫描（PET）衰减校正非常重要。近年来，深度学习实现已证明比基于图集和基于细分的方法有所改进。然而，仍然有一些悬而未决的问题需要解决，例如MRI序列和神经网络架构中最好的问题。在这项工作中，我们比较了两种常见的MRI序列（加权的T1和T2）和设计用于医学图像处理的三个最新的神经网络（Vnet，HighRes3dNet和ScaleNet）的不同组合的性能。实验是对来自公共数据库的大脑数据集进行的。我们的结果表明，T1图像的性能优于T2，但是当组合两个序列时，结果会进一步改善。将T1和T2扫描与HighRes3dNet结合使用，可实现整个头部的最低平均平均误差（MAE = 101.76±10.4 HU）。与众所周知的基于图集的方法相比，所有测试的深度学习模型均实现了更低的MAE（p <0.01）。

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