Multimodal magnetic resonance imaging synthesis via cross-modal feature fusion and hierarchical feature extraction.

ABSTRACT:

Background

Multimodal magnetic resonance imaging (MRI) provides complementary tissue contrasts that are essential for accurate clinical diagnosis. However, high scanning costs, long acquisition times, and motion-induced artifacts often result in incomplete or degraded multimodal data. This study aimed to develop a robust synthesis framework to generate missing or corrupted MRI modalities, thereby improving diagnostic reliability and reducing acquisition burden.

Methods

We propose a novel multimodal MRI synthesis framework that integrates the global contextual modeling capability of Transformer modules with the multi-frequency local feature extraction of ResOctave blocks in a unified architecture. An attention-based feature fusion module is introduced to facilitate cross-moda

SUBMITTER: Guo H 

PROVIDER: S-EPMC12682472 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature