✦ LIBER ✦

Quantitative data analysis of in vivo MRS data sets

✍ Scribed by A. van den Boogaart

Publisher: John Wiley and Sons
Year: 1997
Tongue: English
Weight: 277 KB
Volume: 35
Category: Article
ISSN: 0749-1581
DOI: 10.1002/(sici)1097-458x(199712)35:13<s146::aid-omr196>3.0.co;2-x

No coin nor oath required. For personal study only.

✦ Synopsis

The di †erent characteristics between frequency domain and time domain analysis techniques are detailed for their application to in vivo MRS data sets. With the aim of quantitative analysis of MRS signals, i.e. estimation of parameters in the physical model function that describes the MRS experiment, it is considered desirable to avoid any preprocessing of the data, resulting in a preference for time domain parameter estimation techniques. A historical overview is provided for the time domain analysis methods presented in the literature, and a number of time domain methods are described in detail. Finally, the MRUI software package, providing an interactive graphical user interface for a variety of time domain methods, is summarized.

📜 SIMILAR VOLUMES

Analysis of five sets of color differenc

Analysis of five sets of color difference data

✍ Rolf G. Kuehni 📂 Article 📅 2001 🏛 John Wiley and Sons 🌐 English ⚖ 110 KB 👁 1 views

Analysis of variance by randomization wi

Analysis of variance by randomization with small data sets

✍ Liliana Gonzalez; Bryan F. J. Manly 📂 Article 📅 1998 🏛 John Wiley and Sons 🌐 English ⚖ 132 KB 👁 2 views

A simulation study has been carried out to compare the results from using dierent randomization methods to assess the signi®cance of the F-statistics for factor eects with analysis of variance. Two-way and threeway designs with and without replication were considered, with the randomization of obser

Variable selection in large environmenta

Variable selection in large environmental data sets using principal components analysis

✍ Jacquelynne R. King; Donald A. Jackson 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 117 KB 👁 2 views

In many large environmental datasets redundant variables can be discarded without the loss of extra variation. Principal components analysis can be used to select those variables that contain the most information. Using an environmental dataset consisting of 36 meteorological variables spanning 37 y

Bipolar disorder and chromosome 18: An a

Bipolar disorder and chromosome 18: An analysis of multiple data sets

✍ Judith A. Badner; Lynn R. Goldin 📂 Article 📅 1997 🏛 John Wiley and Sons 🌐 English ⚖ 29 KB 👁 2 views

There have been conflicting reports of linkage between bipolar disorder and chromosome 18. We have analyzed five separate data sets using the two-point affected sib-pair method and family based association methods. We have found evidence of linkage in multiple data sets and differences in maternal a

Description of the Genetic Analysis Work

Description of the Genetic Analysis Workshop 10 bipolar disorder linkage data sets

✍ L.R. Goldin; E.S. Gershon; W.H. Berrettini; O.C. Stine; R. DePaulo; F. McMahon; 📂 Article 📅 1997 🏛 John Wiley and Sons 🌐 English ⚖ 27 KB 👁 2 views

Fast principal component analysis of lar

Fast principal component analysis of large data sets based on information extraction

✍ F. Vogt; M. Tacke 📂 Article 📅 2002 🏛 John Wiley and Sons 🌐 English ⚖ 349 KB 👁 1 views

## Abstract Principal component analysis (PCA) and principal component regression (PCR) are routinely used for calibration of measurement devices and for data evaluation. However, their use is hindered in some applications, e.g. hyperspectral imaging, by excessive data sets that imply unacceptable