A novel transfer learning framework for low-dose CT

摘要

Over the past few years, deep neural networks have made significant processes in denoising low-dose CT images. A trained denoising network, however, may not generalize very well to different dose levels, which follows from the dose-dependent noise distribution. To address this practically, a trained network requires re-training to be applied to a new dose level, which limits the generalization abilities of deep neural networks for clinical applications. This article introduces a deep learning approach that does not require re-training and relies on a transfer learning strategy. More precisely, the transfer learning framework utilizes a progressive denoising model, where an elementary neural network serves as a basic denoising unit. The basic units are then cascaded to successively process towards a denoising task; i.e. the output of one network unit is the input to the next basic unit. The denoised image is then a linear combination of outputs of the individual network units. To demonstrate the application of this transfer learning approach, a basic CNN unit is trained using the Mayo low- dose CT dataset. Then, the linear parameters of the successive denoising units are trained using a different image dataset, i.e. the MGH low-dose CT dataset, containing CT images that were acquired at four different dose levels. Compared to a commercial iterative reconstruction approach, the transfer learning framework produced a substantially better denoising performance.