POWDR: Pathology-Preserving Outpainting with Wavelet Diffusion for 3D MRI

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Machine Learning

Background:

• Medical imaging datasets often face class imbalance and a scarcity of pathology-rich cases, hindering machine learning model performance.
• Existing methods struggle with generating realistic synthetic data that preserves crucial pathological details.

Purpose of the Study:

• To introduce POWDR, a pathology-preserving outpainting framework for 3D MRI data generation.
• To address limitations in medical imaging datasets by creating diverse, anatomically plausible synthetic data.

Main Methods:

• Leveraging wavelet-domain conditioning to enhance high-frequency details and reduce blurring in latent diffusion models.
• Implementing a random connected mask training strategy to improve data diversity and prevent conditioning-induced collapse.
• Evaluating POWDR on brain MRI (BraTS) and extending its application to knee MRI.

Main Results:

• Quantitative metrics (FID, MS-SSIM, LPIPS) confirmed the realism of generated images.
• Random connected mask training significantly improved data diversity, as indicated by reduced cosine similarity and increased KL divergence.
• Downstream segmentation tasks showed modest but statistically significant performance improvements (Dice score increase from 0.6992 to 0.7137) after augmenting with synthetic data.
• Synthetic images demonstrated comparable tissue volumes for CSF and GM, with a slight reduction in WM volume.

Conclusions:

• POWDR effectively generates diverse, pathology-preserving synthetic MRI data.
• The framework shows potential for data augmentation in medical imaging, maintaining clinically relevant lesion characteristics.
• This approach can help overcome data limitations in training machine learning models for various medical imaging tasks.