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A simple method for rectified noise floor suppression: Phase-corrected real data reconstruction with application to diffusion-weighted imaging. Magn Reson Med 2010 Aug;64(2):418-29



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Scopus ID

2-s2.0-77955111675   23 Citations


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.

Author List

Prah DE, Paulson ES, Nencka AS, Schmainda KM


Andrew S. Nencka PhD Director, Associate Professor in the Radiology department at Medical College of Wisconsin
Eric Paulson PhD Associate Professor in the Radiation Oncology department at Medical College of Wisconsin
Douglas Prah PhD Assistant Professor in the Radiation Oncology department at Medical College of Wisconsin
Kathleen M. Schmainda PhD Professor in the Biophysics department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Diffusion Magnetic Resonance Imaging
Image Enhancement
Image Interpretation, Computer-Assisted
Phantoms, Imaging
Reproducibility of Results
Sensitivity and Specificity