Choi Kihwan, Kim Sungwon
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1303-1306. doi: 10.1109/EMBC44109.2020.9176265.
Deep learning has recently attracted widespread interest as a means of reducing noise in low-dose CT (LDCT) images. Deep convolutional neural networks (CNNs) are typically trained to transfer high-quality image features of normal-dose CT (NDCT) images to LDCT images. However, existing deep learning approaches for denoising LDCT images often overlook the statistical property of CT images. In this paper, we propose an approach to statistical image restoration for LDCT using deep learning (StatCNN). We introduce a loss function to incorporate the noise property in the image domain derived from the noise statistics in the sinogram domain. In order to capture the spatially-varying statistics of axial CT images, we increase the receptive fields of the proposed network to cover full-size CT slices. In addition, the proposed network utilizes z-directional correlation by taking multiple consecutive CT slices as input. For performance evaluation, the proposed network was thoroughly trained and tested by leave-one-out cross-validation with a dataset consisting of LDCT-NDCT image pairs. The experimental results showed that the denoising networks successfully reduced the noise level and restored the image details without adding artifacts. This study demonstrates that the statistical deep learning approach can transfer the image style from NDCT images to LDCT images without loss of anatomical information.
深度学习作为一种降低低剂量CT（LDCT）图像噪声的方法，最近引起了广泛关注。深度卷积神经网络（CNN）通常经过训练，将正常剂量CT（NDCT）图像的高质量图像特征转移到LDCT图像上。然而，现有的用于LDCT图像去噪的深度学习方法往往忽略了CT图像的统计特性。在本文中，我们提出了一种使用深度学习进行LDCT统计图像恢复的方法（StatCNN）。我们引入了一个损失函数，将从正弦图域中的噪声统计得出的图像域中的噪声特性纳入其中。为了捕捉轴向CT图像的空间变化统计特性，我们扩大了所提出网络的感受野，以覆盖全尺寸的CT切片。此外，所提出的网络通过将多个连续的CT切片作为输入来利用z方向的相关性。为了进行性能评估，我们使用由LDCT-NDCT图像对组成的数据集，通过留一法交叉验证对所提出的网络进行了全面训练和测试。实验结果表明，去噪网络成功降低了噪声水平，并在不添加伪影的情况下恢复了图像细节。这项研究表明，统计深度学习方法可以将图像风格从NDCT图像转移到LDCT图像，而不会丢失解剖信息。
