Nie Dong, Cao Xiaohuan, Gao Yaozong, Wang Li, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Deep Learn Data Label Med Appl (2016). 2016;2016:170-178. doi: 10.1007/978-3-319-46976-8_18. Epub 2016 Sep 27.
Computed tomography (CT) is critical for various clinical applications, e.g., radiotherapy treatment planning and also PET attenuation correction. However, CT exposes radiation during CT imaging, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve any radiation. Therefore, recently researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiotherapy planning. In this paper, we propose a 3D deep learning based method to address this challenging problem. Specifically, a 3D fully convolutional neural network (FCN) is adopted to learn an end-to-end nonlinear mapping from MR image to CT image. Compared to the conventional convolutional neural network (CNN), FCN generates structured output and can better preserve the neighborhood information in the predicted CT image. We have validated our method in a real pelvic CT/MRI dataset. Experimental results show that our method is accurate and robust for predicting CT image from MRI image, and also outperforms three state-of-the-art methods under comparison. In addition, the parameters, such as network depth and activation function, are extensively studied to give an insight for deep learning based regression tasks in our application.
计算机断层扫描（CT）在各种临床应用中至关重要，例如放射治疗计划以及正电子发射断层扫描（PET）衰减校正。然而，CT成像过程中会产生辐射，这可能会给患者带来副作用。与CT相比，磁共振成像（MRI）要安全得多，且不涉及任何辐射。因此，最近在放射治疗计划的情况下，研究人员受到极大激励，试图从同一受试者的相应MR图像估计CT图像。在本文中，我们提出了一种基于3D深度学习的方法来解决这一具有挑战性的问题。具体而言，采用3D全卷积神经网络（FCN）来学习从MR图像到CT图像的端到端非线性映射。与传统卷积神经网络（CNN）相比，FCN生成结构化输出，并且能够在预测的CT图像中更好地保留邻域信息。我们已在真实的盆腔CT/MRI数据集中验证了我们的方法。实验结果表明，我们的方法在从MRI图像预测CT图像方面准确且稳健，并且在比较中也优于三种最先进的方法。此外，我们还对网络深度和激活函数等参数进行了广泛研究，以便为我们应用中基于深度学习的回归任务提供见解。
