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Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451

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Amyloid burden quantification depends on PET and MR image processing methodology.

Guilherme D Kolinger¹, David Vállez García¹, Antoon T M Willemsen¹

¹Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

|March 5, 2021

Summary

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Image processing methods significantly impact Alzheimer's Disease amyloid quantification. Intensity-based metrics are robust, while volumetric measures require careful processing in Native Space for reliable results.

Area of Science:

Neuroimaging
Biomarkers
Alzheimer's Disease Research

Background:

Positron emission tomography (PET) enables in-vivo quantification of amyloid load, crucial for Alzheimer's Disease (AD) assessment.
Image processing methodologies can introduce variability, potentially affecting the accuracy of amyloid quantification in AD.
Standardized and robust quantification methods are essential for reliable clinical assessment and research in AD.

Purpose of the Study:

To investigate the influence of different semi-automatic image processing pipelines on amyloid quantification in AD.
To compare the impact of various spatial normalization techniques and grey matter segmentation strategies on quantification metrics.
To identify processing pipelines that yield robust and reproducible amyloid load measurements for AD assessment.

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Main Methods:

Analysis of PET images in Native Space and Standard Space using non-rigid spatial transformations.
Exploration of maximum a posteriori (MAP) approaches and tissue probability maps (TPM) for regularization.
Evaluation of grey matter segmentation performed before and after spatial normalization, and using a population-based template.
Assessment of five quantification metrics: two intensity-based, two volumetric-based, and one multi-parametric feature.

Main Results:

Intensity-based metrics showed minimal impact (≤10%) from spatial normalization and segmentation methods, comparable to test-retest variability.
Volumetric and multi-parametric features exhibited significant sensitivity to image processing, with variability up to 45%.
Analysis in Native Space without non-rigid transformations was recommended for volumetric measurements.

Conclusions:

Image processing methodology significantly influences amyloid quantification, particularly for volumetric and multi-parametric metrics.
Intensity-based metrics offer greater robustness against variations in image processing pipelines.
For Standard Space analysis, spatial normalization regularized by TPM is preferred; volumetric measurements are best performed in Native Space.