4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model

TL;DR

This paper introduces a hybrid GAN-based method that synthesizes realistic, controllable 4D cardiac MRI images with anatomical labels using the XCAT model, enhancing data diversity for medical imaging research.

Contribution

The novel integration of the XCAT anatomical model with SPADE-based GANs enables controllable, annotated 4D cardiac MRI synthesis, addressing data scarcity and variability issues.

Findings

Successfully generated 25-frame cardiac cycle images with anatomical labels.

Enabled style transfer to produce diverse image styles.

Demonstrated potential for creating virtual populations for research.

Abstract

We propose a hybrid controllable image generation method to synthesize anatomically meaningful 3D+t labeled Cardiac Magnetic Resonance (CMR) images. Our hybrid method takes the mechanistic 4D eXtended CArdiac Torso (XCAT) heart model as the anatomical ground truth and synthesizes CMR images via a data-driven Generative Adversarial Network (GAN). We employ the state-of-the-art SPatially Adaptive De-normalization (SPADE) technique for conditional image synthesis to preserve the semantic spatial information of ground truth anatomy. Using the parameterized motion model of the XCAT heart, we generate labels for 25 time frames of the heart for one cardiac cycle at 18 locations for the short axis view. Subsequently, realistic images are generated from these labels, with modality-specific features that are learned from real CMR image data. We demonstrate that style transfer from another cardiac image can be accomplished by using a style encoder network. Due to the flexibility of XCAT in creating new heart models, this approach can result in a realistic virtual population to address different challenges the medical image analysis research community is facing such as expensive data collection. Our proposed method has a great potential to synthesize 4D controllable CMR images with annotations and adaptable styles to be used in various supervised multi-site, multi-vendor applications in medical image analysis.