Boosting Magnetic Resonance Image Denoising With Generative Adversarial Networks
Miao Tian, Kaikai Song
Abstract
Denoising plays an important role in the Magnetic Resonance Imaging (MRI) applications for medical diagnosis. MRI images usually contain undesired noises which would negatively affect the exactitude of pathological diagnosis. Recently, many models for MRI denoising have been developed from deep learning networks. In this paper, we propose a novel MRI image denoising method using the conditional Generative Adversarial Networks (GANs). Specifically, a Convolutional Neural Network (CNN) is utilized as the discriminator in the process to distinguish whether the image pair obtained from the conditional GAN is a real pair which consists of a noisy image and a noise-free image or a fake pair which, on the other hand, contains a noisy image and a denoised image. In our design, the convolutional encoder-decoder networks-based generator is used to remove the noise in the noisy MRI images as much as possible. The whole architecture is trained by adversarial learning. Experiments using both synthetic and real clinical MRI datasets are conducted. When tested on the synthetic T1w images with 10% noise level, our method performed better in terms of reaching a high structural similarity index (SSIM) at 0.9489 while that of the next best method was only 0.7485. Moreover, when the image noise level was increased from 1% to 10%, our method was more stable that the SSIM only dropped about 3.2% while that of the next best method dropped about 23.7%. Simulation results demonstrate that the proposed method is more robust and outperforms the conventional methods in both the denoising level and preservation of the anatomical structures and defined contrast.