Enhancing quality and speed in database-free neural network reconstructions of undersampled MRI with SCAMPI.
Journal:
Magnetic resonance in medicine
PMID:
38748852
Abstract
PURPOSE: We present SCAMPI (Sparsity Constrained Application of deep Magnetic resonance Priors for Image reconstruction), an untrained deep Neural Network for MRI reconstruction without previous training on datasets. It expands the Deep Image Prior approach with a multidomain, sparsity-enforcing loss function to achieve higher image quality at a faster convergence speed than previously reported methods.