Deep Learning-Based Denoising Techniques for Low-Field Knee MRI

Abstract

Magnetic resonance imaging (MRI) is a widely used non-invasive medical imaging tool. Recent advancements include whole-body low-field MRI systems (0.55T) to address challenges like high cost and infrastructure demands of high-field MRI. However, low-field images suffer from low signal-to-noise ratio (SNR), which reduces image quality and limits spatial resolution. Although averaging multiple images can achieve the desired SNR while maintaining high spatial resolution, this approach increases total scan time which compromises patient comfort, elevates the risk of motion artifacts, and reduces clinical throughput. Post-processing denoising techniques, such as a patch-based low rank denoiser (PROST) and block match ing 3D filtering (BM3D) have been proposed to address this issue, and more recently, deep learning (DL) methods (both supervised and self-supervised). However, supervised DL requires large datasets, often unavailable for low-field MRI. Most notably, there is no clarify on which of these methods works best, nor a systematic comparison exists for kneeimaging at 0.55T. In this work, we address this by implementing and evaluating two classical methods (PROST, BM3D) and four learning-based approaches (self-supervised: Noisier2Noise,Blind2Unblind and Accelerated Deep Image Prior; supervised: DnCNN) on both public fastMRI (3T/1.5T) and In-house 0.55T knee MRI datasets. Performance is analyzed across configurations to determine optimal denoising strategies and whether self-supervised deep learning-based methods are more effective compared to classical and supervised DL based techniques. Results show that: 1) DnCNN outperforms all methods on the fastMRI database yet it lacks generalization when evaluated on low-field data. 2) On higher simulated levels of noise the self-supervised methods are more effective than classical methods, while on lower levels, classical denoising method are more effective. 3) On 0.55T low-field MRI data, Blind2Unblind is more effective than classical and supervised DL methods. In conclusion, in this study self-supervised methods showed superior performance over supervised and classical methods for low-field knee MR images. However, validation in a larger dataset of knee at 0.55T is needed to further support this conclusion. These findings aim to advance the development of practical denoising strategies that deliver high-quality images while enabling shorter acquisition times and broader accessibility of low-field knee MRI.