ProxNF: Neural Field Proximal Training for High-Resolution 4D Dynamic Image Reconstruction.

ABSTRACT: Accurate spatiotemporal image reconstruction methods are needed for a wide range of biomedical research areas but face challenges due to data incompleteness and computational burden. Data incompleteness arises from the undersampling often required to increase frame rates, while computational burden emerges due to the memory footprint of high-resolution images with three spatial dimensions and extended time horizons. Neural fields (NFs), an emerging class of neural networks that act as continuous representations of spatiotemporal objects, have previously been introduced to solve these dynamic imaging problems by reframing image reconstruction as a problem of estimating network parameters. Neural fields can address the twin challenges of data incompleteness and computational burden by exploi

SUBMITTER: Lozenski L 

PROVIDER: S-EPMC12303064 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature