Image Restoration for Low-Dose CT via Transfer Learning and Residual Network

Abstract

Deep learning has recently been extensively investigated to remove artifacts in low-dose computed tomography (LDCT). However, the power of transfer learning for medical image denoising tasks has not been fully explored. In this work, we proposed a transfer learning residual convolutional neural network (TLR-CNN) to restore LDCT images at single and blind noise levels. A residual network was implemented to effectively estimate the difference between denoised image and its original map, and a noise-free image was obtained by subtracting the residual map from the LDCT image. The results were compared to competing baseline denoising methods in terms of quantitative metrics including the PSNR, RMSE, SSIM and FSIM. For the single noise level, the proposed method demonstrated better denoising performance than the other algorithms for both simulation data and clinical data. As for the blind denoising, the image qualities were improved for all noise levels for all the quantitative metrics, but such improvements were decreasing as the noise level decrease (higher mAs). Comparative experiments suggested that the proposed network could effectively suppress artifacts and preserve image details with faster converge rate and reduced computational time.