Anatomy-Guided Surface Diffusion Model for Alzheimer’s Disease Normative Modeling

Jianwei Zhang, Yonggang Shi

Keywords: Diffusion Model, Alzheimer's Disease, Cortical Surface

TL;DR: A novel Surface diffusion model conditioned on anatomy of the cortical surface structure for Alzheimer's Disease normative modeling.

Abstract:

Normative modeling has emerged as a pivotal approach for characterizing heterogeneity
and individual variance in neurodegenerative diseases, notably Alzheimer’s disease (AD).
One of the challenges of cortical normative modeling is the anatomical structure mismatch
due to folding pattern variability. Traditionally, registration is applied to address this issue
and recently deep generative models are employed to generate anatomically aligned sam-
ples for analyzing disease progression; however, these models are predominantly applied
to volume-based data, which often falls short in capturing intricate morphological changes
on the brain cortex. As an alternative, surface-based analysis has been proven to be more
sensitive in disease modeling such as AD. Yet, like volume-based data, it also suffers from
the mismatch problem. To address these limitations, we propose a novel generative nor-
mative modeling framework by transferring the conditional diffusion generative model to
the spherical domain. Furthermore, the proposed model generates normal feature map
distributions by explicitly conditioning on individual anatomical segmentation to ensure
better geometrical alignment which helps to reduce variance between subjects in norma-
tive analysis. We find that our model can generate samples that are better anatomically
aligned than registered reference data and through ablation study and normative assess-
ment experiments, the samples are able to better measure individual differences from the
normal distribution and increase sensitivity in differentiating cognitively normal (CN), mild
cognitive impairment (MCI), and Alzheimer’s disease (AD) patients.

Primary Subject Area: Image Synthesis

Secondary Subject Area: Detection and Diagnosis

Paper Type: Methodological Development

Registration Requirement: Yes

Reproducibility: https://github.com/jianweizhang17/AnatomyCortexDiffusion.git

Visa & Travel: Yes

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Submission Number: 83