Amyloid burden quantification depends on PET and MR image processing methodology

Abstract

Quantification of amyloid load with positron emission tomography can be useful to assess Alzheimer's Disease in-vivo. However, quantification can be affected by the image processing methodology applied. This study's goal was to address how amyloid quantification is influenced by different semi-automatic image processing pipelines. Images were analysed in their Native Space and Standard Space; non-rigid spatial transformation methods based on maximum a posteriori approaches and tissue probability maps (TPM) for regularisation were explored. Furthermore, grey matter tissue segmentations were defined before and after spatial normalisation, and also using a population-based template. Five quantification metrics were analysed: two intensity-based, two volumetric-based, and one multi-parametric feature. Intensity-related metrics were not substantially affected by spatial normalisation and did not significantly depend on the grey matter segmentation method, with an impact similar to that expected from test-retest studies (≤10%). Yet, volumetric and multi-parametric features were sensitive to the image processing methodology, with an overall variability up to 45%. Therefore, the analysis should be carried out in Native Space avoiding non-rigid spatial transformations. For analyses in Standard Space, spatial normalisation regularised by TPM is preferred. Volumetric-based measurements should be done in Native Space, while intensity-based metrics are more robust against differences in image processing pipelines.

Fig 1. Image processing pipelines scheme.

Scheme of the different image processing pipelines applied to the PET image, MRI, and grey matter (GM) tissue maps. The rigid transformation is shown by a dotted line, elastic transformations by continuous lines, and tissue segmentation by dashed lines. Images were transformed from Native Space into Standard Space using one of three possible spatial normalisation methods (upper and middle section). Spatial transformation matrices were calculated based on the MRI T1 image and these matrices were then applied to the other relevant images, while the inverse matrix was applied to transform the images from Standard Space to Native Space, for example when moving the GM derived from a standard template image or the cerebellum (CER) volume-of-interest (bottom section). Note that subscripts ‘s’ and ‘n’ denote the space in which the tissue images were defined.

Fig 2. Distribution of [¹¹C]PiB mean uptake ratio in grey matter tissue (SUVR_mean).

On the left panels, values from images analysed in the Native Space, on the right images that were spatially normalised to Standard Space. The different spatial normalisation methods are represented in sub-columns within each column, notice that spatial transformations for images in Native Space are required to transform template images from Standard Space. Rows show the different grey matter mask definitions. Diagnosis is indicated on the legend.

Fig 3. Distribution of Aβ+ mean uptake in all grey matter tissue (SUVR_meanAβ+).

On the left panels, values from images analysed in the Native Space, on the right images that were spatially normalised to Standard Space. The different spatial normalisation methods are represented in sub-columns within each column, notice that spatial transformations for images in Native Space are required to transform template images from Standard Space. Rows show the different grey matter mask definitions. Diagnosis is indicated on the legend.

Fig 4. Volume of high [¹¹C]PiB deposition region in grey matter (Aβ+ volume in GM).

On the left panels, values from images analysed in the Native Space, on the right images that were spatially normalised to Standard Space. The different spatial normalisation methods are represented in sub-columns within each column, notice that spatial transformations for images in Native Space are required to transform template images from Standard Space. Rows show the different grey matter mask definitions. Diagnosis is indicated on the legend.

Fig 5. Amyloid fractional volume (percentage of voxels within grey matter classified as Aβ+) values distribution.

On the left panels, values from images analysed in the Native Space, on the right images that were spatially normalised to Standard Space. The different spatial normalisation methods are represented in sub-columns within each column, notice that spatial transformations for images in Native Space are required to transform template images from Standard Space. Rows show the different grey matter mask definitions. Diagnosis is indicated on the legend.

Fig 6. Total amyloid burden value distribution.

On the left panels, values from images analysed in the Native Space, on the right images that were spatially normalised to Standard Space. The different spatial normalisation methods are represented in sub-columns within each column, notice that spatial transformations for images in Native Space are required to transform template images from Standard Space. Rows show the different grey matter mask definitions. Diagnosis is on the legend.