Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2

arXiv:2403.17905v1 Announce Type: cross
Abstract: We propose a new approach for non-Cartesian magnetic resonance image reconstruction. While unrolled architectures provide robustness via data-consistency layers, embedding measurement operators in Deep Neural Network (DNN) can become impractical at large scale. Alternative Plug-and-Play (PnP) approaches, where the denoising DNNs are blind to the measurement setting, are not affected by this limitation and have also proven effective, but their highly iterative nature also affects scalability. To address this scalability challenge, we leverage the "Residual-to-Residual …

abstract architectures arxiv become blind cs.cv cs.lg data deep neural network denoising dnn eess.iv eess.sp embedding image imaging measurement network neural network operators pnp robustness scalable scale type via