Effect of a Low-Rank Denoising Algorithm on Quantitative Magnetic Resonance Imaging-Based Measures of Liver Fat and Iron

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Abstract

Purpose

This study aimed to assess the effect of a low-rank denoising algorithm on quantitative magnetic resonance imaging-based measures of liver fat and iron.

Materials and Methods

This was an institutional review board–approved, Health Insurance Portability and Accountability Act–compliant, retrospective analysis of 42 consecutive subjects who were imaged at 3T using a multiecho gradient echo sequence that was reconstructed using the multistep adaptive fitting algorithm to obtain quantitative proton density fat fraction (PDFF) and R2* maps (original maps). A patch-wise low-rank denoising algorithm was then applied, and PDFF and R2* maps were created (denoised maps). Three readers independently rated the PDFF maps in terms of vessel and liver edge sharpness and image noise using a 5-point scale. Two other readers independently measured mean and standard deviation of PDFF and R2* values for the original and denoised maps; values were compared using intraclass correlation coefficients (ICCs) and mean difference analyses.

Results

Qualitatively, the denoised maps were preferred by all 3 readers based on image noise (P < 0.001) and by 2 of 3 readers based on vessel edge sharpness (P < 0.001–0.99). No reader had a significant preference regarding liver edge sharpness (P = 0.16–0.48). Quantitatively, agreement was near perfect between the original and denoised maps for PDFF (ICC = 0.995) and R2* (ICC = 0.995) values. Mean quantitative values obtained from the original and denoised maps were similar for liver PDFF (7.6 ± 7.7% vs 7.7 ± 7.8%; P = 0.63) and R2* (52.9 ± 40.3s−1 vs 52.8 ± 41.1 s−1, P = 0.74).

Conclusions

Applying the low-rank denoising algorithm to liver fat and iron quantification reduces image noise in PDFF and R2* maps without adversely affecting mean quantitative values or subjective image quality.

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