Effect of a Low-Rank Denoising Algorithm on Quantitative Magnetic Resonance Imaging-Based Measures of Liver Fat and Iron.
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 vs 52.8 ± 41.1 s, 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.
Allen, BC; Lugauer, F; Nickel, D; Bhatti, L; Dafalla, RA; Dale, BM; Jaffe, TA; Bashir, MR
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