Andreas Hauptmann
Andreas Hauptmann
Academy Research Fellow & Associate Professor, University of Oulu
Verified email at - Homepage
Cited by
Cited by
Model based learning for accelerated, limited-view 3D photoacoustic tomography
A Hauptmann, F Lucka, M Betcke, N Huynh, J Adler, B Cox, P Beard, ...
IEEE Transactions on Medical Imaging, 2018
Deep D-bar: Real-time electrical impedance tomography imaging with deep neural networks
SJ Hamilton, A Hauptmann
IEEE transactions on medical imaging 37 (10), 2367-2377, 2018
Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease
A Hauptmann, S Arridge, F Lucka, V Muthurangu, JA Steeden
Magnetic Resonance in Medicine 81 (2), 2019
Deep learning in photoacoustic tomography: current approaches and future directions
A Hauptmann, B Cox
Journal of Biomedical Optics 25 (11), 112903-112903, 2020
Beltrami-net: domain-independent deep D-bar learning for absolute imaging with electrical impedance tomography (a-EIT)
SJ Hamilton, A Hänninen, A Hauptmann, V Kolehmainen
Physiological measurement 40 (7), 074002, 2019
Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions
C Bench, A Hauptmann, B Cox
Journal of Biomedical Optics 25 (8), 085003-085003, 2020
Rapid whole-heart CMR with single volume super-resolution
JA Steeden, M Quail, A Gotschy, KH Mortensen, A Hauptmann, S Arridge, ...
Journal of Cardiovascular Magnetic Resonance 22 (1), 56, 2020
A variational reconstruction method for undersampled dynamic x-ray tomography based on physical motion models
M Burger, H Dirks, L Frerking, A Hauptmann, T Helin, S Siltanen
Inverse Problems 33 (12), 124008, 2017
Learned reconstruction methods with convergence guarantees: A survey of concepts and applications
S Mukherjee, A Hauptmann, O Öktem, M Pereyra, CB Schönlieb
IEEE Signal Processing Magazine 40 (1), 164-182, 2023
Machine learning in magnetic resonance imaging: image reconstruction
J Montalt-Tordera, V Muthurangu, A Hauptmann, JA Steeden
Physica Medica 83, 79-87, 2021
Open 2D electrical impedance tomography data archive
A Hauptmann, V Kolehmainen, NM Mach, T Savolainen, A Seppänen, ...
arXiv preprint arXiv:1704.01178, 2017
Multi-scale learned iterative reconstruction
A Hauptmann, J Adler, S Arridge, O Öktem
IEEE transactions on computational imaging 6, 843-856, 2020
On learned operator correction in inverse problems
S Lunz, A Hauptmann, T Tarvainen, CB Schönlieb, S Arridge
SIAM Journal on Imaging Sciences 14 (1), 2021
Approximate k-space models and deep learning for fast photoacoustic reconstruction
A Hauptmann, B Cox, F Lucka, N Huynh, M Betcke, P Beard, S Arridge
Machine Learning for Medical Image Reconstruction: First International …, 2018
Graph convolutional networks for model-based learning in nonlinear inverse problems
W Herzberg, DB Rowe, A Hauptmann, SJ Hamilton
IEEE transactions on computational imaging 7, 1341-1353, 2021
Tomographic X-ray data of a lotus root filled with attenuating objects
TA Bubba, A Hauptmann, S Huotari, J Rimpeläinen, S Siltanen
arXiv preprint arXiv:1609.07299, 2016
Total variation regularization for large-scale X-ray tomography
K Hämäläinen, L Harhanen, A Hauptmann, A Kallonen, E Niemi, ...
Int. J. Tomogr. Simul 25 (1), 1-25, 2014
A direct D-bar method for partial boundary data electrical impedance tomography with a priori information
M Alsaker, SJ Hamilton, A Hauptmann
Inverse Problems and Imaging 11 (3), 427 - 454, 2017
Material decomposition in spectral CT using deep learning: a Sim2Real transfer approach
JFPJ Abascal, N Ducros, V Pronina, S Rit, PA Rodesch, T Broussaud, ...
IEEE Access 9, 25632-25647, 2021
A model-based iterative learning approach for diffuse optical tomography
M Mozumder, A Hauptmann, I Nissilä, SR Arridge, T Tarvainen
IEEE Transactions on Medical Imaging, 2022
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