Tensor PCA denoising improves reproducibility of quantitative Multi-Parameter Mapping.

Motivation: Quantitative biomarkers such as proton density, longitudinal- and transverse relaxation rates, and magnetization transfer saturation measured by multi-parameter mapping (MPM) reflect microstructural tissue characteristics. However, resolution and reproducibility of MPM are constrained by SNR limits of the imaging process resulting in long acquisition times. Goal(s): We investigate tMPPCA denoising performance for improved SNR and reproducibility of quantitative maps at different voxel volumes for a clinically optimized protocol. Approach: Denoising of repeated MPM acquisitions at different voxel volumes and its effects on model-based SNR and reproducibility of parameter maps. Results: Denoising increase SNR of quantitative maps up to sixfold while scan-rescan fluctuations were halved. Impact: Tensor-based MPPCA denoising of multi-contrast images enhances SNR of quantitative Multi-Parameter Mapping which results in improved reproducibility with greater benefits for high-resolution applications. Consequently, tMPPCA denoising could be considered for future studies and for retrospectively enhancing sensitivity of MPM studies.