Realignment strategies for awake-monkey fMRI data
| Autor: | John S. Duncan, S Stoewer, Andreas Bartels, Georgios A. Keliris, Natasha Sigala, Nikos K. Logothetis, Jozien Goense |
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| Rok vydání: | 2011 |
| Předmět: |
Male
Computer science Movement Biomedical Engineering Biophysics Sensitivity and Specificity Pattern Recognition Automated Software Motion artifacts Distortion Image Interpretation Computer-Assisted medicine Animals Radiology Nuclear Medicine and imaging Computer vision Animal body Wakefulness medicine.diagnostic_test business.industry Brain Reproducibility of Results Image Enhancement Macaca mulatta Magnetic Resonance Imaging Subtraction Technique Sandbox (software development) High field Artificial intelligence Analysis tools Artifacts business Functional magnetic resonance imaging Algorithms |
| Zdroj: | Magnetic Resonance Imaging |
| Popis: | Functional magnetic resonance imaging (fMRI) experiments with awake nonhuman primates (NHPs) have recently seen a surge of applications. However, the standard fMRI analysis tools designed for human experiments are not optimal for NHP data collected at high fields. One major difference is the experimental setup. Although real head movement is impossible for NHPs, MRI image series often contain visible motion artifacts. Animal body movement results in image position changes and geometric distortions. Since conventional realignment methods are not appropriate to address such differences, algorithms tailored specifically for animal scanning become essential. We have implemented a series of high-field NHP specific methods in a software toolbox, fMRI Sandbox (http://kyb.tuebingen.mpg.de/~stoewer/), which allows us to use different realignment strategies. Here we demonstrate the effect of different realignment strategies on the analysis of awake-monkey fMRI data acquired at high field (7 T). We show that the advantage of using a nonstandard realignment algorithm depends on the amount of distortion in the dataset. While the benefits for less distorted datasets are minor, the improvement of statistical maps for heavily distorted datasets is significant. |
| Databáze: | OpenAIRE |
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