Diffusion Models for Computational Neuroimaging: A Survey
Journal:
arXiv
Published Date:
Feb 10, 2025
Abstract
Computational neuroimaging involves analyzing brain images or signals to
provide mechanistic insights and predictive tools for human cognition and
behavior. While diffusion models have shown stability and high-quality
generation in natural images, there is increasing interest in adapting them to
analyze brain data for various neurological tasks such as data enhancement,
disease diagnosis and brain decoding. This survey provides an overview of
recent efforts to integrate diffusion models into computational neuroimaging.
We begin by introducing the common neuroimaging data modalities, follow with
the diffusion formulations and conditioning mechanisms. Then we discuss how the
variations of the denoising starting point, condition input and generation
target of diffusion models are developed and enhance specific neuroimaging
tasks. For a comprehensive overview of the ongoing research, we provide a
publicly available repository at https://github.com/JoeZhao527/dm4neuro.
