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CardioDiT: Latent Diffusion Transformers for 4D Cardiac MRI Synthesis

Marvin Seyfarth, Sarah Kaye Müller, Arman Ghanaat, Isabelle Ayx, Fabian Fastenrath, Philipp Wild, Alexander Hertel, Theano Papavassiliu, Salman Ul Hassan Dar, Sandy Engelhardt

Abstract

Latent diffusion models (LDMs) have recently achieved strong performance in 3D medical image synthesis. However, modalities like cine cardiac MRI (CMR), representing a temporally synchronized 3D volume across the cardiac cycle, add an additional dimension that most generative approaches do not model directly. Instead, they factorize space and time or enforce temporal consistency through auxiliary mechanisms such as anatomical masks. Such strategies introduce structural biases that may limit global context integration and lead to subtle spatiotemporal discontinuities or physiologically inconsistent cardiac dynamics. We investigate whether a unified 4D generative model can learn continuous cardiac dynamics without architectural factorization. We propose CardioDiT, a fully 4D latent diffusion framework for short-axis cine CMR synthesis based on diffusion transformers. A spatiotemporal VQ-VAE encodes 2D+t slices into compact latents, which a diffusion transformer then models jointly as complete 3D+t volumes, coupling space and time throughout the generative process. We evaluate CardioDiT on public CMR datasets and a larger private cohort, comparing it to baselines with progressively stronger spatiotemporal coupling. Results show improved inter-slice consistency, temporally coherent motion, and realistic cardiac function distributions, suggesting that explicit 4D modeling with a diffusion transformer provides a principled foundation for spatiotemporal cardiac image synthesis. Code and models trained on public data are available at https://github.com/Cardio-AI/cardiodit.

CardioDiT: Latent Diffusion Transformers for 4D Cardiac MRI Synthesis

Abstract

Latent diffusion models (LDMs) have recently achieved strong performance in 3D medical image synthesis. However, modalities like cine cardiac MRI (CMR), representing a temporally synchronized 3D volume across the cardiac cycle, add an additional dimension that most generative approaches do not model directly. Instead, they factorize space and time or enforce temporal consistency through auxiliary mechanisms such as anatomical masks. Such strategies introduce structural biases that may limit global context integration and lead to subtle spatiotemporal discontinuities or physiologically inconsistent cardiac dynamics. We investigate whether a unified 4D generative model can learn continuous cardiac dynamics without architectural factorization. We propose CardioDiT, a fully 4D latent diffusion framework for short-axis cine CMR synthesis based on diffusion transformers. A spatiotemporal VQ-VAE encodes 2D+t slices into compact latents, which a diffusion transformer then models jointly as complete 3D+t volumes, coupling space and time throughout the generative process. We evaluate CardioDiT on public CMR datasets and a larger private cohort, comparing it to baselines with progressively stronger spatiotemporal coupling. Results show improved inter-slice consistency, temporally coherent motion, and realistic cardiac function distributions, suggesting that explicit 4D modeling with a diffusion transformer provides a principled foundation for spatiotemporal cardiac image synthesis. Code and models trained on public data are available at https://github.com/Cardio-AI/cardiodit.

Paper Structure

This paper contains 12 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Spatiotemporal Latent Autoencoder
  4. 4D Diffusion Transformer
  5. Datasets
  6. Experimental Setup
  7. Results
  8. Visual Assessment of 4D Consistency
  9. Quantitative Evaluation
  10. Architecture Ablation Study
  11. Discussion

Figures (3)

  • Figure 1: CardioDiT framework for 4D CMR synthesis. A VQ-GAN encodes 2D+t slices into latents stacked along $d$ to form a 4D representation. A 4D Diffusion Transformer denoises the noisy latent, followed by VQ-GAN decoding to reconstruct the full volume.
  • Figure 2: Visualized slices across time and depth for one synthetic sample per model, with corresponding LV meshes. Noteworthy are strong inter-slice discontinuities for the 2D+t LDM, blurry anatomical boundaries for the 3D+t U-Net, and anatomical consistency for CardioDiT.
  • Figure 3: Normalized left ventricular volume-time curves derived from cine CMR blood pool segmentation, temporally aligned to peak-to-peak volume and scaled to equal length $T$. The mean curve (dark blue), inter-quartile range (IQR, light blue), and individual subject curves (light gray) are shown for the studied generative models.