6 citations · 12 across the 5 of their papers we have counts for
3 papers · 1 filter
Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction
Riccardo Barbano, Željko Kereta, Chen Zhang +3
Image reconstruction methods based on deep neural networks have shown outstanding performance, equalling or exceeding the state-of-the-art results of conventional approaches, but o…
Approximate k-space models and Deep Learning for fast photoacoustic reconstruction
Andreas Hauptmann, Ben Cox, Felix Lucka +4
We present a framework for accelerated iterative reconstructions using a fast and approximate forward model that is based on k-space methods for photoacoustic tomography. The appro…
Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease
Andreas Hauptmann, Simon Arridge, Felix Lucka +2
PURPOSE: Real-time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts ca…