A simple method for rectified noise floor suppression: Phase‐corrected real data reconstruction with application to diffusion‐weighted imaging
Autor: | Eric S. Paulson, D.E. Prah, Andrew S. Nencka, Kathleen M. Schmainda |
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Rok vydání: | 2010 |
Předmět: |
Computer science
Phase (waves) Magnitude (mathematics) Iterative reconstruction Sensitivity and Specificity Article Image Interpretation Computer-Assisted Humans Radiology Nuclear Medicine and imaging Computer vision Diffusion (business) Image resolution Phantoms Imaging business.industry Brain Reproducibility of Results Image Enhancement Real image Noise floor Diffusion Magnetic Resonance Imaging Artificial intelligence Artifacts business Algorithm Algorithms Diffusion MRI |
Zdroj: | Magnetic Resonance in Medicine. 64:418-429 |
ISSN: | 1522-2594 0740-3194 |
DOI: | 10.1002/mrm.22407 |
Popis: | Diffusion-weighted MRI is an intrinsically low signal-to-noise ratio application due to the application of diffusion-weighting gradients and the consequent longer echo times. The signal-to-noise ratio worsens with increasing image resolution and diffusion imaging methods that use multiple and higher b-values. At low signal-to-noise ratios, standard magnitude reconstructed diffusion-weighted images are confounded by the existence of a rectified noise floor, producing poor estimates of diffusion metrics. Herein, we present a simple method of rectified noise floor suppression that involves phase correction of the real data. This approach was evaluated for diffusion-weighted imaging data, obtained from ethanol and water phantoms and the brain of a healthy volunteer. The parameter fits from monoexponential, biexponential, and stretched-exponential diffusion models were computed using phase-corrected real data and magnitude data. The results demonstrate that this newly developed simple approach of using phase-corrected real images acts to reduce or even suppress the confounding effects of a rectified noise floor, thereby producing more accurate estimates of diffusion parameters. |
Databáze: | OpenAIRE |
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