A user independent denoising method for x-nuclei MRI and MRS
Nichlas Vous Christensen, Michael Vaeggemose, Nikolaj Bøgh, Esben S. S. Hansen, Jonas L. Olesen, Yaewon Kim, Daniel B. Vigneron, Jeremy W. Gordon, Sune N. Jespersen, Christoffer Laustsen
Abstract
Purpose
X-nuclei (also called non-proton MRI) MRI and spectroscopy are limited by the intrinsic low SNR as compared to conventional proton imaging. Clinical translation of x-nuclei examination warrants the need of a robust and versatile tool improving image quality for diagnostic use. In this work, we compare a novel denoising method with fewer inputs to the current state-of-the-art denoising method.
Methods
Denoising approaches were compared on human acquisitions of sodium (23Na) brain, deuterium (2H) brain, carbon (13C) heart and brain, and simulated dynamic hyperpolarized 13C brain scans, with and without additional noise. The current state-of-the-art denoising method Global-local higher order singular value decomposition (GL-HOSVD) was compared to the few-input method tensor Marchenko-Pastur principal component analysis (tMPPCA). Noise-removal was quantified by residual distributions, and statistical analyses evaluated the differences in mean-square-error and Bland–Altman analysis to quantify agreement between original and denoised results of noise-added data.
Results
GL-HOSVD and tMPPCA showed similar performance for the variety of x-nuclei data analyzed in this work, with tMPPCA removing ˜5% more noise on average over GL-HOSVD. The mean ratio between noise-added and denoising reproducibility coefficients of the Bland–Altman analysis when compared to the original are also similar for the two methods with 3.09 ± 1.03 and 2.83 ± 0.79 for GL-HOSVD and tMPPCA, respectively.
Conclusion
The strength of tMPPCA lies in the few-input approach, which generalizes well to different data sources. This makes the use of tMPPCA denoising a robust and versatile tool in x-nuclei imaging improvements and the preferred denoising method.