Applications of monotonic noise reduction algorithms in fMRI, phase estimation, and contrast enhancement
✍ Scribed by John B. Weaver
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 425 KB
- Volume
- 10
- Category
- Article
- ISSN
- 0899-9457
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✦ Synopsis
Noise reduction using monotonic fits between extrema has been shown to work well on images, especially those with very low signal-to-noise ratios (SNRs). In this article we will explore three applications of monotonic noise reduction in magnetic resonance imaging (MRI). The first application is reducing noise in function MRI (fMRI) studies. Reduced noise allows greater flexibility. For example, it allows the activated regions to be identified using noisier images acquired in less time or fewer cycles of stimulation. Activation maps were calculated from the images after noise reduction had been applied to each image in the series. The parameters used in the noise reduction were optimized so the images produced best matched the average of the entire series. The CNR was improved significantly in the activation maps. The results can be extended to any other fMRI paradigm. The second application was reducing noise in complex data to improve the SNR of the phase in the complex MRI image. The error in the phase was reduced by a factor of three in the simulations shown. In the third application, we introduced a simple contrastenhancement method using monotonic noise reduction. To enhance contrast, the coarse features were reduced in size; the smaller size features were increased in size; very small features that are likely to be noise were attenuated. The result is a simple, effective method of improving the contrast of features of a selected size in images with no false features introduced.