Macro2Micro: Cross-modal Magnetic Resonance Imaging Synthesis Leveraging Multi-scale Brain Structures
Journal:
arXiv
Published Date:
Dec 15, 2024
Abstract
Spanning multiple scales-from macroscopic anatomy down to intricate
microscopic architecture-the human brain exemplifies a complex system that
demands integrated approaches to fully understand its complexity. Yet, mapping
nonlinear relationships between these scales remains challenging due to
technical limitations and the high cost of multimodal Magnetic Resonance
Imaging (MRI) acquisition. Here, we introduce Macro2Micro, a deep learning
framework that predicts brain microstructure from macrostructure using a
Generative Adversarial Network (GAN). Grounded in the scale-free, self-similar
nature of brain organization-where microscale information can be inferred from
macroscale patterns-Macro2Micro explicitly encodes multiscale brain
representations into distinct processing branches. To further enhance image
fidelity and suppress artifacts, we propose a simple yet effective auxiliary
discriminator and learning objective. Our results show that Macro2Micro
faithfully translates T1-weighted MRIs into corresponding Fractional Anisotropy
(FA) images, achieving a 6.8% improvement in the Structural Similarity Index
Measure (SSIM) compared to previous methods, while preserving the individual
neurobiological characteristics.
