Spatiotemporal coarse-to-fine diffusion model for automatic brain network generation
Abstract
BACKGROUND: Functional magnetic resonance imaging (fMRI) has emerged as a transformative tool in analyzing and understanding brain diseases. It is a challenge to learn effective features from the high-dimensional fMRI. Most studies have focused on extracting connectivity-based features for disease analysis. However, they heavily rely on the software toolboxes to construct connectivity-based features, which may suffer from large errors because of different manual parameter settings and thus lead to bad performance in brain disorder analysis.
PURPOSE: A novel brain denoiser model is proposed to transform four-dimensional fMRI (4D fMRI) into a brain network in a unified framework for brain disease analysis.
METHODS: By introducing anatomical knowledge, the proposed model first reduces the 4D fMRI into a 2D coarse region-of-interest(ROI)-based time series and then diffuses it into noisy status by gradually adding Gaussian noise. Moreover, the coarse-to-fine transformer refinement is designed to capture multi-scale temporal dynamics and iteratively remove unrelated multi-frequency noise. Besides, the low-frequency preservation module is devised to enhance the effective signal at low frequencies during the denoising process. This can improve the signal-to-noise ratio at each timestep, which ensures accurate restoration of ROI time series and improves the performance of brain network construction.
RESULTS: We evaluate the performance of the Brain Denoiser on the Alzheimer's Disease…
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