  As acquiring MRIs is expensive, neuroscience studies struggle to attain a
sufficient number of them for properly training deep learning models. This
challenge could be reduced by MRI synthesis, for which Generative Adversarial
Networks (GANs) are popular. GANs, however, are commonly unstable and struggle
with creating diverse and high-quality data. A more stable alternative is
Diffusion Probabilistic Models (DPMs) with a fine-grained training strategy. To
overcome their need for extensive computational resources, we propose a
conditional DPM (cDPM) with a memory-efficient process that generates
realistic-looking brain MRIs. To this end, we train a 2D cDPM to generate an
MRI subvolume conditioned on another subset of slices from the same MRI. By
generating slices using arbitrary combinations between condition and target
slices, the model only requires limited computational resources to learn
interdependencies between slices even if they are spatially far apart. After
having learned these dependencies via an attention network, a new
anatomy-consistent 3D brain MRI is generated by repeatedly applying the cDPM.
Our experiments demonstrate that our method can generate high-quality 3D MRIs
that share a similar distribution to real MRIs while still diversifying the
training set. The code is available at
https://github.com/xiaoiker/mask3DMRI_diffusion and also will be released as
part of MONAI, at https://github.com/Project-MONAI/GenerativeModels.
