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Updated: Jun 10, 2026
Diffusion Imaging in the Rat Cervical Spinal Cord
Published on: April 7, 2015
A simple method for rectified noise floor suppression: Phase-corrected real data reconstruction with application to
Douglas E Prah1, Eric S Paulson, Andrew S Nencka
1Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Abstract:
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.
Insights
This study introduces a simple phase correction method to improve low signal-to-noise ratio diffusion-weighted MRI. The technique suppresses noise, leading to more accurate diffusion parameter estimation in phantoms and brain imaging.
Area of Science:
- Magnetic Resonance Imaging
- Biomedical Engineering
- Image Processing
Background:
- Diffusion-weighted MRI (DW-MRI) suffers from low signal-to-noise ratio (SNR) due to diffusion gradients and long echo times.
- Increased image resolution and higher b-values in DW-MRI further degrade SNR.
- Rectified noise floor in magnitude reconstructed DW-images leads to inaccurate diffusion metric estimation.
Purpose of the Study:
- To present a straightforward phase correction method for real diffusion-weighted MRI data.
- To evaluate the effectiveness of this method in suppressing the rectified noise floor.
- To assess the impact of phase correction on diffusion parameter estimation accuracy.
Main Methods:
- A phase correction technique applied to the real component of diffusion-weighted MRI data was developed.
- The method was tested using diffusion-weighted imaging data from ethanol/water phantoms and human brain.
- Diffusion models (monoexponential, biexponential, stretched-exponential) were fitted using both phase-corrected real data and standard magnitude data.
Main Results:
- Phase-corrected real data effectively reduced or suppressed the rectified noise floor.
- This suppression led to improved accuracy in diffusion parameter fits compared to magnitude data.
- The method demonstrated efficacy across phantom and in-vivo human brain datasets.
Conclusions:
- Phase correction of real diffusion-weighted MRI data is a simple yet effective method to mitigate noise floor artifacts.
- This technique enhances the reliability and accuracy of diffusion parameter estimation.
- The proposed approach offers a valuable improvement for low SNR diffusion imaging applications.
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