Improving the Accuracy, Quality, and Signal-To-Noise Ratio of MRI Parametric Mapping Using Rician Bias Correction and Parametric-Contrast-Matched Principal Component Analysis (PCM-PCA).

MRI parametric mapping, including T2 mapping, can quantitatively characterize tissue properties and is an important MRI procedure in biomedical research and studies of diseases [1-3]. However, the accuracy, quality, and signal-to-noise ratio (SNR) of MRI parametric mapping may be negatively impacted by Rician noise in multi-contrast MRI data [4]. As such, it is important to develop a post-processing method to minimize the negative impact of Rician noise. In this study, we report a new parametric-contrast-matched principal component analysis (PCM-PCA) denoising method that involves 1) identifying voxels with similar T2 decay characteristics and 2) using the principal component analysis (PCA) to denoise multi-contrast MRI data along the echo time (TE) dimension. We additionally evaluated the effects of integrating Rician bias correction and the new PCM-PCA method. In this study, we mathematically added Rician noise at various levels to human brain MRI data and performed different combinations of denoising and Rician bias correction on the magnitude-valued images. We found that MRI denoising using the PCM-PCA method resulted in improved image quality, SNR, and accuracy of the measured T2 relaxation time constants. Additionally, we found that for data with low SNR (