6 citations · 12 across the 5 of their papers we have counts for
11 papers · 1 filter
Machine Learning in Magnetic Resonance Imaging: Image Reconstruction
Javier Montalt-Tordera, Vivek Muthurangu, Andreas Hauptmann +1
Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20…
An efficient Quasi-Newton method for nonlinear inverse problems via learned singular values
Danny Smyl, Tyler N. Tallman, Dong Liu +1
Solving complex optimization problems in engineering and the physical sciences requires repetitive computation of multi-dimensional function derivatives. Commonly, this requires co…
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…
Deep Learning in Photoacoustic Tomography: Current approaches and future directions
Andreas Hauptmann, Ben Cox
Biomedical photoacoustic tomography, which can provide high resolution 3D soft tissue images based on the optical absorption, has advanced to the stage at which translation from th…
On the unreasonable effectiveness of CNNs
Andreas Hauptmann, Jonas Adler
Deep learning methods using convolutional neural networks (CNN) have been successfully applied to virtually all imaging problems, and particularly in image reconstruction tasks wit…
Blind hierarchical deconvolution
Arttu Arjas, Lassi Roininen, Mikko J. Sillanpää +1
Deconvolution is a fundamental inverse problem in signal processing and the prototypical model for recovering a signal from its noisy measurement. Nevertheless, the majority of mod…