A Plug-and-Play Approach To Multiparametric Quantitative MRI: Image Reconstruction Using Pre-Trained Deep Denoisers
TL
TL;DR
An iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process and shows consistent dealiasing performance against both acquisition schemes and accurate mapping of tissues’ quantitative bio-properties.
Abstract
Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern which is used for fast (compressed) acquisition. This may not be useful when the acquisition process is unknown during training of the deep learning model and/or changes during testing time. This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process. Spatiotemporal image priors are learned by an image denoiser i.e. a Convolutional Neural Network (CNN), trained to remove generic white gaussian noise (not a particular subsampling artefact) from data. This CNN denoiser is then used as a data-driven shrinkage operator within the iterative reconstruction algorithm. This algorithm with the same denoiser model is then tested on two simulated acquisition processes with distinct subsampling patterns. The results show consistent dealiasing performance against both acquisition schemes and accurate mapping of tissues’ quantitative bio-properties. Software available: https://github.com/ketanfatania/QMRI-PnP-Recon-POC
References17 items
1
Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks
2
Plug-and-Play Image Restoration With Deep Denoiser Prior
3
Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations
4
Scalable Plug-and-Play ADMM with Convergence Guarantees
5