Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence, domain adaptation, harmonization, neuroimage analysis
Motivation: The technical variability caused by the batch differences could hinder the ability to aggregate neuroimaging data across sites negatively impacting the reliability of study-level results.
Goal(s): We propose a denoising diffusion probabilistic model-based method which handles multiple-domain harmonization task by harmonizing domain variant features while retaining domain invariant biological features.
Approach: We have demonstrated the diffusion model’s superior capability in harmonizing images from multiple domains. We also incorporated 3D fusion network to overcome the in-between-slice difference issue led by 2D harmonization work.
Results: Our proposed method yields harmonization results with consistent anatomy preservation and 12.7% FID score improvement compared to other GAN-based methods.
Impact: This work showed efficacy of using diffusion model to tackle neuroimaging harmonization problem with the preservation of anatomical and biological details. It is specifically evaluated to harmonize the imaging texture heterogeneity present in the large cohorts of multi-center dataset.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords