FetalDiffusion: Pose-Controllable 3D Fetal MRI Synthesis with Conditional Diffusion Model
This work addresses fetal motion issues in MRI for medical imaging by enhancing pose estimation models, though it is incremental as it builds on existing diffusion models for a specific domain.
The study tackled the problem of limited real fetal MRI data for pose estimation by generating synthetic 3D fetal MRI images with controllable poses using a conditional diffusion model, resulting in a 15.4% increase in PCK and 50.2% reduction in mean error for pose estimation when integrated with real data.
The quality of fetal MRI is significantly affected by unpredictable and substantial fetal motion, leading to the introduction of artifacts even when fast acquisition sequences are employed. The development of 3D real-time fetal pose estimation approaches on volumetric EPI fetal MRI opens up a promising avenue for fetal motion monitoring and prediction. Challenges arise in fetal pose estimation due to limited number of real scanned fetal MR training images, hindering model generalization when the acquired fetal MRI lacks adequate pose. In this study, we introduce FetalDiffusion, a novel approach utilizing a conditional diffusion model to generate 3D synthetic fetal MRI with controllable pose. Additionally, an auxiliary pose-level loss is adopted to enhance model performance. Our work demonstrates the success of this proposed model by producing high-quality synthetic fetal MRI images with accurate and recognizable fetal poses, comparing favorably with in-vivo real fetal MRI. Furthermore, we show that the integration of synthetic fetal MR images enhances the fetal pose estimation model's performance, particularly when the number of available real scanned data is limited resulting in 15.4% increase in PCK and 50.2% reduced in mean error. All experiments are done on a single 32GB V100 GPU. Our method holds promise for improving real-time tracking models, thereby addressing fetal motion issues more effectively.