Magician's Corner: 7. Using Convolutional Neural Networks to Reduce Noise in Medical Images

Abstract

This article shows how to train a convolutional neural network to reduce noise in CT images, although the principles apply to medical and nonmedical images; authors also explore mathematical and visually weighted loss functions to adjust the appearance.

Figure 1:

Schematic diagram of data used. Standard sinogram data collected at full dose are reconstructed in a normal fashion to produce the reference full-dose image. The sinogram data also have a copy made that has noise injected into it, modeling what a lower-dose sinogram image would look like. This is then reconstructed, producing a simulated low-dose image, but there is exact correspondence of the structures in the images, which is important for training purpose.

Figure 2:

Schematic of the simple model architecture used for denoising. This model employs five layers of two-dimensional convolutions. Each layer will have an associated activation function (rectified linear unit [ReLU] in this case) that adds nonlinearity to the filtering function. There is no pooling, and thus the output resolution matches the input resolution.

Figure 3:

Schematic of the feature loss term. The first three blocks of VGG16 were applied to the convolutional neural network (CNN) output and target image to extract features from the images. The feature contents were then compared using mean squared error (MSE). One advantage of using feature loss is improved retention of realistic CT texture within the CNN output.

Figure 4:

A comparison of the original low-dose image (left), the image denoised using the simple convolutional neural network (CNN) with mean squared error (MSE) as the loss function (middle), and image denoised using the same model with a combination of MSE and VGG16 features as the loss function (right). Although the model architecture and the training data are the same, the different loss function has a significant impact on the quality of the output image. A line profile was included to assess the network’s ability to retain bone structure after noise reduction.