A Survey of fMRI to Image Reconstruction

TL;DR

This paper provides the first comprehensive review of fMRI-to-image reconstruction, discussing current challenges, methodologies, and future research directions in decoding human perception through neuroimaging and deep learning.

Contribution

It introduces the concept of fMRI2Image learning and systematically categorizes existing methodologies, offering a foundational reference for future research in the field.

Findings

Identifies key challenges like data scarcity and variability.

Categorizes methodologies such as signal encoding and image generation.

Proposes promising directions for advancing fMRI-to-image reconstruction.

Abstract

Functional magnetic resonance imaging (fMRI) based image reconstruction plays a pivotal role in decoding human perception, with applications in neuroscience and brain-computer interfaces. While recent advancements in deep learning and large-scale datasets have driven progress, challenges such as data scarcity, cross-subject variability, and low semantic consistency persist. To address these issues, we introduce the concept of fMRI-to-Image Learning (fMRI2Image) and present the first systematic review in this field. This review highlights key challenges, categorizes methodologies such as fMRI signal encoding, feature mapping, and image generator. Finally, promising research directions are proposed to advance this emerging frontier, providing a reference for future studies.