Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidence
This work addresses a critical issue for radiologists by enhancing small lesion visibility in noisy MRI scans, though it is incremental as it builds on existing deep learning architectures.
The authors tackled the problem of denoising 3D MRI images to improve diagnostic confidence for small lesions, proposing a voxel-wise hybrid residual MLP-CNN model that outperformed state-of-the-art methods in quantitative and visual evaluations on 720 T2-FLAIR brain images, with radiologists confirming better recovery of small lesions at moderate and high noise levels.
Small lesions in magnetic resonance imaging (MRI) images are crucial for clinical diagnosis of many kinds of diseases. However, the MRI quality can be easily degraded by various noise, which can greatly affect the accuracy of diagnosis of small lesion. Although some methods for denoising MR images have been proposed, task-specific denoising methods for improving the diagnosis confidence of small lesions are lacking. In this work, we propose a voxel-wise hybrid residual MLP-CNN model to denoise three-dimensional (3D) MR images with small lesions. We combine basic deep learning architecture, MLP and CNN, to obtain an appropriate inherent bias for the image denoising and integrate each output layers in MLP and CNN by adding residual connections to leverage long-range information. We evaluate the proposed method on 720 T2-FLAIR brain images with small lesions at different noise levels. The results show the superiority of our method in both quantitative and visual evaluations on testing dataset compared to state-of-the-art methods. Moreover, two experienced radiologists agreed that at moderate and high noise levels, our method outperforms other methods in terms of recovery of small lesions and overall image denoising quality. The implementation of our method is available at https://github.com/laowangbobo/Residual_MLP_CNN_Mixer.