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Detection of significant features and statistical analysis of 2D and 3D images of 31p metabolites

✍ Scribed by Sarah J. Nelson; Daniel B. Vigneron; Truman R. Brown

Publisher
John Wiley and Sons
Year
1992
Tongue
English
Weight
642 KB
Volume
25
Category
Article
ISSN
0740-3194

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✦ Synopsis

Abstract

The application of spectroscopy techniques to the study of normal and pathological tissue is currently limited by the difficulties of acquiring precisely localized spectraand analyzing the resulting, relatively low signal‐to‐noise data. Interpretation of multivoxel spectroscopy data is greatly facilitated by estimating peak parameters and representing their spatial distribution as metabolite images. We present herethe new algorithm MIM‐STATS, which performs an analysisof these images and determines whether the variations in the images are statistically significant. The algorithmhas been developed and tested by application to ^31^P CSI data from the human forearm and brain. Our results demonstrate that MIMSTATS can detect signal at a very low signal‐to‐noise ratio in a reliable and reproducible fashion and provides a sound basis for testinghypotheses concerning differences in distribution of metabolites.

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✍ Per-Erik Danielsson; Qingfen Lin; Qin-Zhong Ye πŸ“‚ Article πŸ“… 2001 πŸ› Elsevier Science 🌐 English βš– 895 KB

Estimation of local second-degree variation should be a natural first step in computerized image analysis, just as it seems to be in human vision. A prevailing obstacle is that the second derivatives entangle the three features, signal strength (i.e., magnitude or energy), orientation, and shape. To