Owing to the heterogeneity of living tissues, it is challenging to quantify tissue properties using magnetic resonance imaging. Within a single voxel, contributions to the signal may result from several types of 1H nuclei with varied chemical (e.g., -CH2-, -OH) and physical environments (e.g., tissu
Multivariate Image Analysis of Magnetic Resonance Images with the Direct Exponential Curve Resolution Algorithm (DECRA): Part 1: Algorithm and Model Study
โ Scribed by W. Windig; J.P. Hornak; B. Antalek
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 209 KB
- Volume
- 132
- Category
- Article
- ISSN
- 1090-7807
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โฆ Synopsis
Antalek and Windig recently presented a fast method to resolve a series of NMR mixture spectra, where the contribution of the components varies with a decaying exponential [B. Antalek and W. Windig, J. Am. Chem. Soc. 118, 10,331-10,332 (1996); W. Windig and B. Antalek, Chemom. Intell. Lab. Syst. 37, 241-254 (1997)]. The method was called DECRA (direct exponential curve resolution algorithm). In this paper DECRA will be applied to two series of magnetic resonance images. The signal of one series is based upon T2 relaxation, and the other is based upon T1 relaxation. In order to evaluate the technique, the magnetic resonance images of a phantom where used. A transformation is introduced to enable the application of DECRA to a T1 series of magnetic resonance images. A separate paper in this issue will describe the application of the techniques to magnetic resonance images of the human brain. Copyright 1998 Academic Press.
๐ SIMILAR VOLUMES
Recently, a new multivariate analysis tool was developed to resolve mixture data sets, where the contributions ('concentrations') have an exponential profile. The new approach is called DECRA (direct exponential curve resolution algorithm). DECRA is based on the generalized rank annihilation method