Low-dose CT denoising via CNN trained using images with activation map

Abstract

Convolutional neural network (CNN) based denoisers have shown promising results in low-dose CT (LDCT) denoising. However, image blur is a problem that needs to be addressed because it deforms or eliminates small features, which interferes with the diagnosis. Pixel level loss, such as mean-squared-error (MSE) loss, used for CNN training is the cause of the image blur in the denoised image because the pixel level loss computes the average of all pixel value differences without attention on important features. To resolve the image blur, we propose to use an activation map for training CNN denoiser. The activation map indicates the area where the CNN classifier focuses for classifying the image. We train CNN classifier to classify lesion-present and lesion-absent CT images (i.e., binary detection task), and then, obtain activation map of image using trained CNN classifier. It is observed that lesions and edges of images are activated in activation map, and therefore, when the activation map is multiplied by image, small features are emphasized. We train CNN denoiser in two steps. First, we train CNN denoiser using LDCT and normal-dose CT (NDCT) image pairs. In the second step, we fine-tune network parameters of CNN denoiser using LDCT and NDCT image pairs multiplied by NDCT activation map. The two- step trained CNN denoiser effectively reduces noise while preserving small features.