A noise-robust post-processing pipeline for accelerated phase-cycled 23Na Multi-Quantum Coherences MRI
Abstract
Purpose To develop an improved post-processing pipeline for noise-robust accelerated phase-cycled Cartesian Single (SQ) and Triple Quantum (TQ) sodium (23Na) Magnetic Resonance Imaging (MRI) of in vivo human brain at 7 T. Theory and Methods Our pipeline aims to tackle the challenges of 23Na Multi-Quantum Coherences (MQC) MRI including low Signal-to-Noise Ratio (SNR) and time-consuming Radiofrequency (RF) phase-cycling. Our method combines low-rank k-space denoising for SNR enhancement with Dynamic Mode Decomposition (DMD) to robustly separate SQ and TQ signal components. This separation is crucial for computing the TQ/SQ ratio, a key parameter of 23Na MQC MRI. We validated our pipeline in silico, in vitro and in vivo in healthy volunteers, comparing it with conventional denoising and Fourier transform (FT) methods. Additionally, we assessed its robustness through ablation experiments simulating a corrupted RF phase-cycle step. Results Our denoising algorithm doubled SNR compared to non-denoised images and enhanced SNR by up to 29% compared to Wavelet denoising. The low-rank approach produced high-quality images even at later echo times, allowing reduced signal averaging. DMD effectively separated the SQ and TQ signals, even with missing RF phase cycle steps, resulting in superior Structural Similarity (SSIM) of 0.89±0.024 and lower Root Mean Squared Error (RMSE) of 0.055±0.008 compared to conventional FT methods (SSIM=0.71±0.061, RMSE=0.144±0.036). This pipeline enabled…
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