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

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. In this article, authors show how to train a convolutional neural network to reduce noise on medical images, especially low-dose CT images from the recent American Association of Physicists in Medicine low-dose challenge dataset. Human visual feature weighting can be used as a part of the loss term to improve the visual appearance of the filtered images. Medical imaging is driven to produce the best possible images while reducing the radiation dose or acquisition time. Normally, this trade-off is dealt with by using the best possible detection systems and experimenting with different acquisition techniques. Recently, deep learning methods have been applied to images acquired with low dose (or less acquisition time in the case of MRI) to produce images that appear similar to full-dose images.