Phenotype-Guided Generative Model for High-Fidelity Cardiac MRI Synthesis: Advancing Pretraining and Clinical Applications
Journal:
arXiv
Published Date:
May 6, 2025
Abstract
Cardiac Magnetic Resonance (CMR) imaging is a vital non-invasive tool for
diagnosing heart diseases and evaluating cardiac health. However, the limited
availability of large-scale, high-quality CMR datasets poses a major challenge
to the effective application of artificial intelligence (AI) in this domain.
Even the amount of unlabeled data and the health status it covers are difficult
to meet the needs of model pretraining, which hinders the performance of AI
models on downstream tasks. In this study, we present Cardiac Phenotype-Guided
CMR Generation (CPGG), a novel approach for generating diverse CMR data that
covers a wide spectrum of cardiac health status. The CPGG framework consists of
two stages: in the first stage, a generative model is trained using cardiac
phenotypes derived from CMR data; in the second stage, a masked autoregressive
diffusion model, conditioned on these phenotypes, generates high-fidelity CMR
cine sequences that capture both structural and functional features of the
heart in a fine-grained manner. We synthesized a massive amount of CMR to
expand the pretraining data. Experimental results show that CPGG generates
high-quality synthetic CMR data, significantly improving performance on various
downstream tasks, including diagnosis and cardiac phenotypes prediction. These
gains are demonstrated across both public and private datasets, highlighting
the effectiveness of our approach. Code is availabel at
https://anonymous.4open.science/r/CPGG.
