A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging

TL;DR

This paper introduces TSSC-Net, a diffusion-based framework for 4D MRI that achieves 6x temporal super-resolution and improves spatial consistency, effectively handling rapid motion and large deformations in dynamic imaging.

Contribution

TSSC-Net is a novel framework combining diffusion-based super-resolution with a tri-directional module to enhance temporal fidelity and spatial consistency in 4D MRI.

Findings

Achieves 6x temporal super-resolution in a single inference.

Effectively preserves spatial consistency and structural fidelity.

Demonstrates superior performance on cardiac and knee MRI datasets.

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.