Journal
MAGNETIC RESONANCE IN MEDICINE
Volume 72, Issue 1, Pages 260-268Publisher
WILEY-BLACKWELL
DOI: 10.1002/mrm.24914
Keywords
MRI relaxometry; pixel-by-pixel; R2*mapping; noise; nonlocal means
Funding
- National Basic Research Program of China [2010CB732502]
- National Natural Science Funds of China [81371539, 61172034]
- UK NIHR Cardiovascular Biomedical Research Unit of Royal Brompton Hospital and Imperial College, London
- Wellcome Trust Value In People (VIP) award
- British Heart Foundation (BHF) Intermediate Basic Science Fellowship [FS/08/26225]
- Wellcome Trust
- Higher Education Funding for England (HEFCE)
- Siemens
- British Heart Foundation [PG/09/074/27961] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0508-10081] Funding Source: researchfish
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Purpose: To investigate the feasibility of improving MRI R2* mapping by filtering the images before curve-fitting. Methods: Pixel-by-pixel curve-fitting for the quantification of MRI relaxometry remains a challenge for low signal-to-noise ratio images. By computing the weighted mean of spatially adjacent pixels, the low-pass Gaussian (LPG) filter can suppress the noise but at the expense of blurring. By assigning high weights to pixels with similar neighborhood patches, the nonlocal means (NLM) algorithm can reduce noise while retaining intrinsic signals, however, its potential has not been explored in pixel-by-pixel MRI relaxometry, and in this study, we aimed to investigate the impact of the LPG and the NLM filtering on decay signals and MRI R2* mapping. These two filtering methods were compared on both simulated and in vivo data. Results: Both LPG and NLM algorithms produces R2* maps with decreased root-mean-square-errors. The LPG filter blurs edges of R2* maps while the NLM algorithm preserves details well. The NLM consistently yields R2* mapping with smaller errors than the LPG filtering in all cases. Conclusion: Pixel-by-pixel fitting can skew MRI relaxometry. The NLM outperforms the conventional LPG filter and has the potential to provide more accurate pixel-by-pixel MRI relaxometry for improved tissue characterization. (C) 2013 Wiley Periodicals, Inc.
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