Self-Diffusion Maps from Wavelet De-Noised NMR Images
✍ Scribed by Gordon Sarty; Edward Kendall
- Publisher
- Elsevier Science
- Year
- 1996
- Tongue
- English
- Weight
- 458 KB
- Volume
- 111
- Category
- Article
- ISSN
- 1064-1866
No coin nor oath required. For personal study only.
✦ Synopsis
A wavelet transform was used to improve the signal-to-noise in nient technology for examining flow and diffusion through NMR microimages of Phaseolus vulgaris cotyledonary tissues. Two the net phase shift resulting from the former and through cases were tested: a relatively clean image and, at longer TE, a the echo signal attenuation that diffusion causes.
relatively noisy image. Significant improvement in the image qual-
For the case of seed imbibition, although the water moveity of both test cases was achieved, with the most dramatic imment has not been fully characterized, there are both diffuprovement noted in the noisy image. The conditions were extended sive and coherent flow events occurring simultaneously at to include diffusion weighting with the aim of computing diffusion various points in the imbibitional process. However, once maps for the tissues. Again, the wavelet procedure was effective the seed has achieved its equilibrium concentration, diffusion in improving the appearance of the (diffusion-weighted) images.
drives water redistribution 5).
Finally, diffusion maps from the raw and de-noised data were
Diffusion influences the coherence of the transverse-magcomputed and compared. The de-noised images produced maps with better detail and more consistent features. It is concluded netization component. After the RF pulse, excited spins prethat wavelet de-noising is a very useful approach for enhancing cess in the transverse plane at their characteristic Larmour the image quality of hard-to-image tissues.