44 citations · 56 across the 10 of their papers we have counts for
7 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…
Photoacoustic Reconstruction Using Sparsity in Curvelet Frame: Image versus Data Domain
Bolin Pan, Simon R. Arridge, Felix Lucka +5
Curvelet frame is of special significance for photoacoustic tomography (PAT) due to its sparsifying and microlocalisation properties. We derive a one-to-one map between wavefront d…
Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent
Riccardo Barbano, Chen Zhang, Simon Arridge +1
Recent advances in reconstruction methods for inverse problems leverage powerful data-driven models, e.g., deep neural networks. These techniques have demonstrated state-of-the-art…
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…
Fast Estimation of Haemoglobin Concentration in Tissue Via Wavelet Decomposition
Geoffrey Jones, Neil T Clancy, Xiaofei Du +4
Tissue oxygenation and perfusion can be an indicator for organ viability during minimally invasive surgery, for example allowing real-time assessment of tissue perfusion and oxygen…