  The emergence of tau PET imaging over the last decade has enabled Alzheimer's
disease (AD) researchers to examine tau pathology in vivo and more effectively
characterize the disease trajectories of AD. Current tau PET analysis methods,
however, typically perform inferences on large cortical ROIs and are limited in
the detection of localized tau pathology that varies across subjects.
Furthermore, a high-resolution MRI is required to carry out conventional tau
PET analysis, which is not commonly acquired in clinical practices and may not
be acquired for many elderly patients with dementia due to strong motion
artifacts, claustrophobia, or certain metal implants. In this work, we propose
a novel conditional diffusion model to perform MRI-free anomaly detection from
tau PET imaging data. By including individualized conditions and two
complementary loss maps from pseudo-healthy and pseudo-unhealthy
reconstructions, our model computes an anomaly map across the entire brain area
that allows simply training a support vector machine (SVM) for classifying
disease severity. We train our model on ADNI subjects (n=534) and evaluate its
performance on a separate dataset from the preclinical subjects of the A4
clinical trial (n=447). We demonstrate that our method outperforms baseline
generative models and the conventional Z-score-based method in anomaly
localization without mis-detecting off-target bindings in sub-cortical and
out-of-brain areas. By classifying the A4 subjects according to their anomaly
map using the SVM trained on ADNI data, we show that our method can
successfully group preclinical subjects with significantly different cognitive
functions, which further demonstrates the effectiveness of our method in
capturing biologically relevant anomaly in tau PET imaging.
