Keywords: Analysis/Processing, Data Processing
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.
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