A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging
Journal:
arXiv
Published Date:
Jun 4, 2025
Abstract
In medical imaging, 4D MRI enables dynamic 3D visualization, yet the
trade-off between spatial and temporal resolution requires prolonged scan time
that can compromise temporal fidelity--especially during rapid, large-amplitude
motion. Traditional approaches typically rely on registration-based
interpolation to generate intermediate frames. However, these methods struggle
with large deformations, resulting in misregistration, artifacts, and
diminished spatial consistency. To address these challenges, we propose
TSSC-Net, a novel framework that generates intermediate frames while preserving
spatial consistency. To improve temporal fidelity under fast motion, our
diffusion-based temporal super-resolution network generates intermediate frames
using the start and end frames as key references, achieving 6x temporal
super-resolution in a single inference step. Additionally, we introduce a novel
tri-directional Mamba-based module that leverages long-range contextual
information to effectively resolve spatial inconsistencies arising from
cross-slice misalignment, thereby enhancing volumetric coherence and correcting
cross-slice errors. Extensive experiments were performed on the public ACDC
cardiac MRI dataset and a real-world dynamic 4D knee joint dataset. The results
demonstrate that TSSC-Net can generate high-resolution dynamic MRI from
fast-motion data while preserving structural fidelity and spatial consistency.
