Enhanced Parallel MRI Reconstruction Using PCA-Based Machine Learning Denoising

Abstract

Compressed Sensing (CS) has been widely adopted in Magnetic Resonance Imaging (MRI) to enable accurate image reconstruction from highly undersampled k-space data, thereby reducing acquisition time. In parallel MRI (pMRI), multi-coil measurements further accelerate imaging but are inherently sensitive to noise amplification and reconstruction artifacts, especially under aggressive undersampling and low Signal-to-Noise Ratio (SNR) conditions. This work presents a machine learning–inspired PCA-based denoising framework integrated into a CS-driven pMRI reconstruction process. Specifically, undersampled k-space data are first reconstructed using a CS-SENSE formulation, after which a patch-based Principal Component Analysis (PCA) is applied to suppress noise by exploiting the low-rank structure of local image patches. Unlike conventional filtering approaches, the proposed PCA denoising adaptively learns dominant signal subspaces, effectively preserving structural details while attenuating both acquisition noise and reconstruction-induced artifacts. Extensive numerical simulations on multi-coil MRI data under various sampling ratios (0.15-0.30) and noise levels (SNR=1-20 dB) demonstrate the effectiveness of the proposed method. The results indicate that the proposed framework effectively enhances reconstruction quality across a wide range of conditions. By combining global sparsity constraints (CS) with local low-rank learning (PCA), the method provides a robust and efficient solution for noise-resilient accelerated MRI reconstruction, with strong potential for practical and clinical applications.