Magnetic resonance image denoising for Rician noise using a novel hybrid transformer‐CNN network (HTC‐net) and self‐supervised pretraining

Abstract

BackgroundMagnetic resonance imaging (MRI) is a crucial technique for both scientific research and clinical diagnosis. However, noise generated during MR data acquisition degrades image quality, particularly in hyperpolarized (HP) gas MRI. While deep learning (DL) methods have shown promise for MR image denoising, most of them fail to adequately utilize the long‐range information which is important to improve denoising performance. Furthermore, the sample size of paired noisy and noise‐free MR images also limits denoising performance.PurposeTo develop an effective DL method that enhances denoising performance and reduces the requirement of paired MR images by utilizing the long‐range information and pretraining.MethodsIn this work, a hybrid Transformer‐convolutional neural network (CNN) network (HTC‐net) and a self‐supervised pretraining strategy are proposed, which effectively enhance the denoising performance. In HTC‐net, a CNN branch is exploited to extract the local features. Then a Transformer‐CNN branch with two parallel encoders is designed to capture the long‐range information. Within this branch, a residual fusion block (RFB) with a residual feature processing module and a feature fusion module is proposed to aggregate features at different resolutions extracted by two parallel encoders. After that, HTC‐net exploits the comprehensive features from the CNN branch and the Transformer‐CNN branch to accurately predict noise‐free MR images through a reconstruction module. To further enhance the performance on limited MRI datasets, a self‐supervised pretraining strategy is proposed. This strategy employs self‐supervised denoising to equip the HTC‐net with denoising capabilities during pretraining, and then the pre‐trained parameters are transferred to facilitate subsequent supervised training.ResultsExperimental results on the pulmonary HP 129Xe MRI dataset (1059 images) and IXI dataset (5000 images) all demonstrate the proposed method outperforms the state‐of‐the‐art methods, exhibiting superior preservation of edges and structures. Quantitatively, on the pulmonary HP 129Xe MRI dataset, the proposed method outperforms the state‐of‐the‐art methods by 0.254–0.597 dB in PSNR and 0.007–0.013 in SSIM. On the IXI dataset, the proposed method outperforms the state‐of‐the‐art methods by 0.3–0.927 dB in PSNR and 0.003–0.016 in SSIM.ConclusionsThe proposed method can effectively enhance the quality of MR images, which helps improve the diagnosis accuracy in clinical.