The R2D2 Deep Neural Network Series for Scalable Non-Cartesian Magnetic Resonance Imaging
Journal:
arXiv
Published Date:
Mar 12, 2025
Abstract
We introduce the R2D2 Deep Neural Network (DNN) series paradigm for fast and
scalable image reconstruction from highly-accelerated non-Cartesian k-space
acquisitions in Magnetic Resonance Imaging (MRI). While unrolled DNN
architectures provide a robust image formation approach via data-consistency
layers, embedding non-uniform fast Fourier transform operators in a DNN can
become impractical to train at large scale, e.g in 2D MRI with a large number
of coils, or for higher-dimensional imaging. Plug-and-play approaches that
alternate a learned denoiser blind to the measurement setting with a
data-consistency step are not affected by this limitation but their highly
iterative nature implies slow reconstruction. To address this scalability
challenge, we leverage the R2D2 paradigm that was recently introduced to enable
ultra-fast reconstruction for large-scale Fourier imaging in radio astronomy.
R2D2's reconstruction is formed as a series of residual images iteratively
estimated as outputs of DNN modules taking the previous iteration's data
residual as input. The method can be interpreted as a learned version of the
Matching Pursuit algorithm. A series of R2D2 DNN modules were sequentially
trained in a supervised manner on the fastMRI dataset and validated for 2D
multi-coil MRI in simulation and on real data, targeting highly under-sampled
radial k-space sampling. Results suggest that a series with only few DNNs
achieves superior reconstruction quality over its unrolled incarnation R2D2-Net
(whose training is also much less scalable), and over the state-of-the-art
diffusion-based "Decomposed Diffusion Sampler" approach (also characterised by
a slower reconstruction process).
