Brain Imaging Generation with Latent Diffusion Models

作     者：Pinaya, Walter H.L. Tudosiu, Petru-Daniel Dafflon, Jessica Da Costa, Pedro F. Fernandez, Virginia Nachev, Parashkev Ourselin, Sebastien Cardoso, M. Jorge 

作者机构：Department of Biomedical Engineering School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Data Science and Sharing Team Functional Magnetic Resonance Imaging Facility National Institute of Mental Health BethesdaMD20892 United States Machine Learning Team Functional Magnetic Resonance Imaging Facility National Institute of Mental Health BethesdaMD20892 United States Institute of Psychiatry Psychology & Neuroscience King’s College London United Kingdom Centre for Brain and Cognitive Development Birkbeck College United Kingdom Institute of Neurology University College London United Kingdom 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Deep neural networks 

摘      要：Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects might be hindering their full potential. Generating synthetic data provides a promising alternative, allowing to complement training datasets and conducting medical image research at a larger scale. Diffusion models recently have caught the attention of the computer vision community by producing photorealistic synthetic images. In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images. We used T1w MRI images from the UK Biobank dataset (N=31,740) to train our models to learn about the probabilistic distribution of brain images, conditioned on covariables, such as age, sex, and brain structure volumes. We found that our models created realistic data, and we could use the conditioning variables to control the data generation effectively. Besides that, we created a synthetic dataset with 100,000 brain images and made it openly available to the scientific community. © 2022, CC BY.