Causality-Aware Multitask Diffusion Models for Joint Dynamic Cardiac MRI Super-Resolution and Functional Assessment

Meng Niu

doi:10.54254/2753-8818/2025.25683

Theoretical and Natural ScienceOpen access

Causality-Aware Multitask Diffusion Models for Joint Dynamic Cardiac MRI Super-Resolution and Functional Assessment

Research Article

Open Access

Causality-Aware Multitask Diffusion Models for Joint Dynamic Cardiac MRI Super-Resolution and Functional Assessment

Meng Niu ^1*

¹ Shihezi University

^*Corresponding author: mengmeng0711@outlook.com

Published on 6 August 2025

TNS Vol.134

ISSN (Print): 2753-8826

ISSN (Online): 2753-8818

ISBN (Print): 978-1-80590-307-9

ISBN (Online): 978-1-80590-308-6

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Abstract

Cardiac magnetic resonance imaging (Cardiac MRI) is an important noninvasive tool for evaluating cardiac structure and function, but its spatial resolution and temporal consistency are often limited by imaging equipment, which affects the accurate portrayal of complex cardiac dynamics. Existing methods mostly regard image reconstruction and functional assessment as independent tasks, failing to establish a causal link between structure and function, resulting in inefficient information utilization and unstable prediction accuracy. To solve the above problems, this paper proposes a causality-aware multitask diffusion model, which embeds causal reasoning mechanism into the diffusion denoising process to realize the joint assessment of super-resolution reconstruction of cardiac MRI images and functional indexes such as ejection fraction and ventricular volume. The model architecture includes a causal encoder, a multi-task diffusion network and a joint decoder, and the causal consistency loss is introduced during the training process to constrain the structure-function dynamic association. Experiments are conducted on multiple cardiac MRI public datasets, and the results show that the model outperforms existing methods in PSNR, SSIM, temporal consistency, and functional prediction error, and has stronger interpretability and clinical potential. This study provides new ideas for building an interpretable medical AI system that integrates image quality and functional reasoning.

Keywords:

Diffusion models, Causal inference, Multitask learning, Cardiac MRI, Super-resolution

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