GANcMRI: Cardiac magnetic resonance video generation and physiologic guidance using latent space prompting.

ABSTRACT: Generative artificial intelligence can be applied to medical imaging on tasks such as privacy-preserving image generation and superresolution and denoising of existing images. Few prior approaches have used cardiac magnetic resonance imaging (cMRI) as a modality given the complexity of videos (the addition of the temporal dimension) as well as the limited scale of publicly available datasets. We introduce GANcMRI, a generative adversarial network that can synthesize cMRI videos with physiological guidance based on latent space prompting. GANcMRI uses a StyleGAN framework to learn the latent space from individual video frames and leverages the timedependent trajectory between end-systolic and end-diastolic frames in the latent space to predict progression and generate motion over time. We p

SUBMITTER: Vukadinovic M 

PROVIDER: S-EPMC10783442 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature