Name
Abstract | ||
|---|---|---|
Multivariate pattern classification and prediction offers an alternative to standard univariate analysis techniques, and has recently been applied in MR imaging using support vector machines (SVM), and used to attain real-time feedback. The standard approach has been to use reconstructed image magnitude data. However, information is also present in the image phase data, and in the k-space data itself. Further, multi-echo imaging offers possibilities of increased functional sensitivity and quantitative imaging. In this study, we explore applying SVM techniques to complex and multi-echo fMRI data, using both phase information and earlier echo-times for prediction. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/PRNI.2013.65 | PRNI |
Keywords | Field | DocType |
quantitative imaging,standard approach,k-space data,mr imaging,multi-echo fmri data,svm technique,image phase data,multi-echo imaging,multivariate classification,phase information,reconstructed image magnitude data,accuracy,support vector machines,multivariate,imaging,sensitivity,support vector machine,complex data,classification,image reconstruction,image classification | Iterative reconstruction,Mr imaging,Computer vision,Magnitude (mathematics),Pattern recognition,Multivariate statistics,Computer science,Support vector machine,Complex data type,Artificial intelligence,Quantitative imaging,Contextual image classification | Conference |
ISSN | Citations | PageRank |
2330-9989 | 0 | 0.34 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Scott Peltier | 1 | 78 | 7.35 |
| Douglas C Noll | 2 | 324 | 50.84 |
| Jonathan Lisinski | 3 | 0 | 1.01 |
| Stephen LaConte | 4 | 265 | 28.11 |