Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model

TL;DR

This paper introduces a novel diffusion-based model guided by motion features for unpaired cardiac video translation, enabling high-quality synthesis of cine CMR images from lower-contrast sequences, with improved accuracy and clinical utility.

Contribution

It proposes a new motion feature guided diffusion model with a specialized encoder and registration network for unpaired cardiac video translation, advancing the state-of-the-art.

Findings

Outperforms existing methods in quantitative metrics

Produces high-fidelity, dynamic cine CMR images

Enhances downstream clinical tasks

Abstract

This paper presents a novel motion feature guided diffusion model for unpaired video-to-video translation (MFD-V2V), designed to synthesize dynamic, high-contrast cine cardiac magnetic resonance (CMR) from lower-contrast, artifact-prone displacement encoding with stimulated echoes (DENSE) CMR sequences. To achieve this, we first introduce a Latent Temporal Multi-Attention (LTMA) registration network that effectively learns more accurate and consistent cardiac motions from cine CMR image videos. A multi-level motion feature guided diffusion model, equipped with a specialized Spatio-Temporal Motion Encoder (STME) to extract fine-grained motion conditioning, is then developed to improve synthesis quality and fidelity. We evaluate our method, MFD-V2V, on a comprehensive cardiac dataset, demonstrating superior performance over the state-of-the-art in both quantitative metrics and qualitative assessments. Furthermore, we show the benefits of our synthesized cine CMRs improving downstream clinical and analytical tasks, underscoring the broader impact of our approach. Our code is publicly available at https://github.com/SwaksharDeb/MFD-V2V.