The SRI24 Multi-Channel Atlas of Normal Adult Human Brain Structure
By Torsten Rohlfing, Natalie M. Zahr, Edith V. Sullivan, Adolf Pfefferbaum. Human Brain Mapping, 2009
518 citations recorded by the source corpus.
This article describes the SRI24 atlas, a new standard reference system of normal human brain anatomy, that was created using template‐free population registration of high‐resolution magnetic resonance images acquired at 3T in a group of 24 normal control subjects. The atlas comprises anatomical channels (T1, T2, and proton density weighted), diffusion‐related channels (fractional anisotropy, mean diffusivity, longitudinal diffusivity, mean diffusion‐weighted image), tissue channels (CSF probability, gray matter probability, white matter probability, tissue labels), and two cortical parcellation maps. The SRI24 atlas enables multichannel atlas‐to‐subject image registration. It is uniquely versatile in that it is equally suited for the two fundamentally different atlas applications: label propagation and spatial normalization. Label propagation, herein demonstrated using diffusion tensor image fiber tracking, is enabled by the increased sharpness of the SRI24 atlas compared with other available atlases. Spatial normalization, herein demonstrated using data from a young–old group comparison study, is enabled by its unbiased average population shape property. For both propagation and normalization, we also report the results of quantitative comparisons with seven other published atlases: Colin27, MNI152, ICBM452 (warp5 and air12), and LPBA40 (SPM5, FLIRT, AIR). Our results suggest that the SRI24 atlas, although based on 3T MR data, allows equally accurate spatial normalization of data acquired at 1.5T as the comparison atlases, all of which are based on 1.5T data. Furthermore, the SRI24 atlas is as suitable for label propagation as the comparison atlases and detailed enough to allow delineation of anatomical structures for this purpose directly in the atlas. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.
Subfields: Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/95c4c21f580db269ee81e18143de781771061661
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In Radiology, Nuclear Medicine and Imaging
- Nassir Navab
- Benedikt Wiestler
- Shadi Albarqouni
- Christoph Baur
- Adolf Pfefferbaum
- Edith V. Sullivan
- Elfar Adalsteinsson
- Berkin Bilgic
Citations in the corpus
- Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study (2020, methodology): “All scans have been brought to the SRI24 ATLAS [20] space to ensure all data share the same volume size and orientation.”
- MRI Estimates of Brain Iron Concentration in Normal Aging Using Quantitative Susceptibility Mapping (2011, methodology): “The 3.0T SPGR image from each subject, after brain extraction using BET, finally was registered nonrigidly to the SPGR channel of the SRI24 atlas (Rohlfing et al., 2010) [http://nitrc.org/projects/sri24/].”
- Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images (2018, methodology): “To reduce variability and relax the reconstruction problem, all images have been rigidly co-registered to the SRI24 ATLAS [10].”