Low-dose CT with a deep convolutional neural network blocks model using mean squared error loss and structural similar loss

Abstract

X-Ray computed tomography (CT) is one of the most popular imaging modality in the medical image analysis for clinical application. Meanwhile, the potential risk of X-Ray radiation dose to patients has attracted the public attention. Over the past decades, extensive efforts have been made for developing low-dose CT. However, X-Ray radiation dose reduction may result in increased noise and artifacts, which can significantly compromise the image quality and deteriorate the diagnostic performance. Hence, restoring CT image from low-dose CT and improving the diagnostic performance is a challenging for the vast researchers and developers. In this paper, a method based on deep learning techniques is proposed for low-dose CT noise reduction. Our method integrates convolutional neural network (CNN) blocks, residual learning, exponential linear units (ELUs) into a deep learning framework. Especially, loss of structural similarity index (SSIM) is combines to the final objective function to improve the image quality. Differs from general deep learning based denoising method, our deep CNN blocks architecture learning noise directly from original low-dose CT images, then restores denoised CT image by subtracting the obtained noise image from the original low-dose CT. After training patch by patch, the proposed method attains promising performance compared to state of the art traditional methods (non-local means and Block-matching 3D) and representative deep learning methods (primary three layers convolutional neural networks and residual encoder-decoder convolutional neural network) in visual effects and quantitative measurements. Extensive experiments was implemented for how choosing the coefficients of overall loss function and the number of CNN blocks. The experimental results demonstrate that our noise reduction method is effective for low-dose CT and potential clinic application.