Aali Asad, Arvinte Marius, Kumar Sidharth, Arefeen Yamin I, Tamir Jonathan I
Department of Radiology, Stanford University, Stanford, California.
Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas.
Magn Reson Med. 2025 Jun 2. doi: 10.1002/mrm.30591.
To examine the effect of incorporating self-supervised denoising as a pre-processing step for training deep learning (DL) based reconstruction methods on data corrupted by Gaussian noise. K-space data employed for training are typically multi-coil and inherently noisy. Although DL-based reconstruction methods trained on fully sampled data can enable high reconstruction quality, obtaining large, noise-free datasets is impractical.
We leverage Generalized Stein's Unbiased Risk Estimate (GSURE) for denoising. We evaluate two DL-based reconstruction methods: Diffusion Probabilistic Models (DPMs) and Model-Based Deep Learning (MoDL). We evaluate the impact of denoising on the performance of these DL-based methods in solving accelerated multi-coil magnetic resonance imaging (MRI) reconstruction. The experiments were carried out on T2-weighted brain and fat-suppressed proton-density knee scans.
We observed that self-supervised denoising enhances the quality and efficiency of MRI reconstructions across various scenarios. Specifically, employing denoised images rather than noisy counterparts when training DL networks results in lower normalized root mean squared error (NRMSE), higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) across different SNR levels, including 32, 22, and 12 dB for T2-weighted brain data, and 24, 14, and 4 dB for fat-suppressed knee data.
We showed that denoising is an essential pre-processing technique capable of improving the efficacy of DL-based MRI reconstruction methods under diverse conditions. By refining the quality of input data, denoising enables training more effective DL networks, potentially bypassing the need for noise-free reference MRI scans.
研究将自监督去噪作为预处理步骤，对基于深度学习（DL）的重建方法在受高斯噪声污染的数据上进行训练的效果。用于训练的k空间数据通常是多线圈的且固有噪声较大。尽管在全采样数据上训练的基于DL的重建方法能够实现高重建质量，但获取大量无噪声数据集是不切实际的。
我们利用广义斯坦无偏风险估计（GSURE）进行去噪。我们评估两种基于DL的重建方法：扩散概率模型（DPM）和基于模型的深度学习（MoDL）。我们评估去噪对这些基于DL的方法在解决加速多线圈磁共振成像（MRI）重建性能方面的影响。实验在T2加权脑扫描和脂肪抑制质子密度膝关节扫描上进行。
我们观察到自监督去噪在各种情况下都能提高MRI重建的质量和效率。具体而言，在训练DL网络时使用去噪后的图像而非有噪声的图像，在不同信噪比水平下会导致更低的归一化均方根误差（NRMSE）、更高的结构相似性指数测量值（SSIM）和峰值信噪比（PSNR），对于T2加权脑数据，信噪比水平分别为32、22和12 dB，对于脂肪抑制膝关节数据，信噪比水平分别为24、14和4 dB。
我们表明去噪是一种重要的预处理技术，能够在不同条件下提高基于DL的MRI重建方法的效能。通过提高输入数据的质量，去噪能够训练出更有效的DL网络，可能无需无噪声的参考MRI扫描。
