Denoising AutoEncoder as a Pre-processor for Knee MRI Analysis

Motivation: Pre-processing MR images is a necessary step prior to image analysis due to variability of intensity scale in MR images. Goal(s): To develop a deep learning algorithm for standardizing knee MR images prior to analysis. Approach: We developed a denoising autoencoder with VNet architecture achieving on-the-fly image pre-processing (Bias field correction and intensity normalization) and denoising. Image quality was evaluated using SNR, NMSE, PSNR, and SSIM. Results: Our approach achieved an improved SNR with an efficient runtime compared to conventional pre-processing methods. Impact: Our DL-based knee MRI pre-processing tool generates standardized MRI outputs for image analysis and DL model development. This tool can be incorporated into a wide range of image analysis pipelines for the knee.