CT Image Denoising with Perceptive Deep Neural Networks

Qingsong Yang, Ge Wang, Pingkun Yan, Mannudeep K. Kalra


Published in:Fully3D 2017 Proceedings


low dose CT, image denoising, deep learning, perceptual loss
Increasing use of CT in modern medical practice has raised concerns over associated radiation dose. Reduction of radiation dose associated with CT can increase noise and artifacts, which can adversely affect diagnostic confidence. Denoising of low-dose CT images on the other hand can help improve diagnostic confidence, which however is a challenging problem due to its ill-posed nature, since one noisy image patch may correspond to many different output patches. In the past decade, machine learning based approaches have made quite impressive progress in this direction. However, most of those methods, including the recently popularized deep learning techniques,aimforminimizingmean-squared-error(MSE)betweena denoised CT image and the ground truth, which results in losing important structural details due to over-smoothing, although the PSNR based performance measure looks great. In this work, we introduce a new perceptual similarity measure as the objective function for a deep convolutional neural network to facilitate CT image denoising. Instead of directly computing MSE for pixelto-pixel intensity loss, we compare the perceptual features of a denoised output against those of the ground truth in a feature space. Therefore, our proposed method is capable of not only reducing the image noise levels, but also keeping the critical structural information at the same time. Promising results have been obtained in our experiments with a large number of CT images. 
Qingsong Yang
Rensselaer Polytechnic Institute, USA
Ge Wang
Biomedical Imaging Center, Rensselaer Polytechnic Institute, USA
Pingkun Yan
Philips Research North America, USA
Mannudeep K. Kalra
Massachusetts General Hospital, Harvard Medical School, USA
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