The SRI24 multichannel atlas of normal adult human brain structure

Abstract

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.

Figure 1

Images of the youngest (19 years; top row) and oldest (84 years; bottom row) of the 24 subjects. Images are shown here after within‐subject alignment and, for SPGR, early‐ and late‐echo images, with MR bias field correction.

Figure 2

Axial slices from all subjects (a) after groupwise affine registration, and (b) after groupwise nonrigid registration. Enlargements of the respective slices from the oldest (left) and youngest subject (right) in the population are also shown. The images in each panel are sorted by increasing subject age, in rows from top left to bottom right.

Figure 3

Average SPGR image after (a) groupwise affine and (b) groupwise nonrigid registration. The nonrigid registration clearly improves the level of anatomical detail visible in the atlas.

Figure 4

Illustration of the registration links between the images that the SRI24 atlas is constructed from. For each subject, the b = 0 EPI is registered to the late‐echo FSE image, which in turn is registered to the SPGR image. The early‐ and late‐echo FSE images from each subject were acquired in a single, dual‐echo acquisition and are, therefore, in perfect registration. The SPGR images from all 24 subjects are coregistered using a simultaneous, template‐free registration algorithm.

Figure 5

Axial slices through the channels of the SRI24 atlas in 10 mm increments, from z = 20 mm (inferior) to z = 130 mm (superior). The structural image channels are grouped on the left, the tissue segmentation channels are grouped in the center, and the diffusion‐related channels are grouped on the right. Columns from left to right: SPGR, early‐echo FSE, late‐echo FSE, CSF probability map, GM probability map, WM probability map, tissue label map, mean DWI, FA map, MD map, λ₁ map.

Figure 6

Axial slices of the SRI24/TZO cortical parcellation map, overlaid onto the SPGR structural atlas channel, in 5‐mm increments from z = 15 mm (inferior) to z = 135 mm (superior).

Figure 7

Three‐dimensional renderings of the SRI24/TZO parcellation map, projected onto a cortical surface model. (a) View from left. (b) View from anterior. (c) View from superior. (d) View from right. (e) View from posterior. (f) View from inferior.

Figure 8

Axial slices of the SRI24/LPBA40 cortical parcellation map, overlaid onto the SPGR structural atlas channel, in 5‐mm increments from z = 15 mm (inferior) to z = 135 mm (superior).

Figure 9

Three‐dimensional renderings of the SRI24/LPBA40 parcellation map, projected onto a cortical surface model. (a) View from left. (b) View from anterior. (c) View from superior. (d) View from right. (e) View from posterior. (f) View from inferior.

Figure 10

Examples of other publicly available MRI‐based brain atlases. Only T1‐weighted images are available for all atlases in this figure. (a) Colin27 brain atlas generated by averaging 27 independently acquired SPGR images of the same subject. (b) MNI152 atlas (at 2‐mm resolution, as distributed with FSL). (c) ICBM452/air12 atlas. (d) ICBM452/warp5 atlas. (e) LPBA40/SPM5 atlas. Note the still substantial fuzziness of all atlases other than Colin27, especially when compared with the SRI24.

Figure 11

Illustration of the SRI24 atlas for spatial normalization. (a) Average FA maps for 10 young (left) and 10 elderly (right) subjects after registration and reformatting into the atlas space. (b) Line profile reveals lower FA in the elderly subjects (gray line) than the young subjects (black line). White line in (a) marks the level at which FA is quantified.

Figure 12

Example of region definition for fiber tracking using the SRI24 atlas and label propagation. (a) Parcellation of the corpus callosum into nine segments defined in the SRI24 atlas (top row) and propagated onto a subject's FA map (bottom row). (b) 3D rendering of fiber tracts determined in subject's diffusion tensor image and colored according to the parcellation of the corpus callosum.

Figure 13

Volume renderings of four atlases illustrate the level of sharpness with which the cortical surface is defined in each. (a) SRI24. (b) Colin27. (c) ICBM452/air12. (d) ICBM452/warp5.