SOCR Data June2008 ID NI
From Socr
Contents |
SOCR Datasets - Neuroimaging study of super-resolution image enhancing
Data Overview
Neuroimaging MRI data from asymptomatic subjects was acquired to develop a structural brain atlas (Shattuck, et al., 2008). The raw data was processed in two different ways to test a research hypothesis that super-resolution image enhancement would improve automated volume parcellation. Thus, an automated volume parcelation (Tu, et al., 2008) was applied first to the raw data and then to the super-resolved enhanced volumes (Marquina and Osher, 2007). Two measures of quality of the automated volume parsing were used -- sensitivity and specificity.
Finally the results of the Sensitivity and Specificity measures were compared for the two analysis protocols -- applying the auto-volume parser directly to the native data (Standard method) and preprocesisng the native data with super-resolution enhancement (Super-Resolved) before using hte automated volume parser. Of interest was whether the second protocol (supre-resolved analysis) would produce more reliable, consistent and accurate automated volume tesselations, compared to the first protocol (standard method) where a super-resolution preprocessing was not applied.
Data Description
Data Table
References
- Shattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Salamon G, Narr KL, Poldrack RA, Bilder RM, Toga AW (2008) Construction of a 3D Probabilistic Atlas of Human Cortical Structures. NeuroImage, doi: 10.1016/j.neuroimage.2007.09.031.
- Tu, Z., Narr, K. L., Dinov, I., Dollar, P., Thompson, P. M., & Toga, A. W. (2008). Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models. IEEE Transactions on Medical Imaging. (in press).
- Marquina, A. and Osher, SJ. Image super-resolution by TV-regularization, UCLA CAM Report 2007-18), July 2007.
Translate this page:
