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Andreas Hauptmann
Andreas Hauptmann
Academy Research Fellow & Associate Professor, University of Oulu
Geverifieerd e-mailadres voor oulu.fi - Homepage
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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
2452018
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
1932018
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
1492019
Deep learning in photoacoustic tomography: current approaches and future directions
A Hauptmann, B Cox
Journal of Biomedical Optics 25 (11), 112903-112903, 2020
892020
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
472019
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
392017
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
372020
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
332018
Open 2D electrical impedance tomography data archive
A Hauptmann, V Kolehmainen, NM Mach, T Savolainen, A Seppänen, ...
arXiv preprint arXiv:1704.01178, 2017
332017
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
332014
Multi-scale learned iterative reconstruction
A Hauptmann, J Adler, S Arridge, O Öktem
IEEE transactions on computational imaging 6, 843-856, 2020
302020
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
282017
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
27*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
262016
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), 1-13, 2020
242020
Machine learning in magnetic resonance imaging: image reconstruction
J Montalt-Tordera, V Muthurangu, A Hauptmann, JA Steeden
Physica Medica 83, 79-87, 2021
182021
Learning and correcting non-Gaussian model errors
D Smyl, TN Tallman, JA Black, A Hauptmann, D Liu
Journal of Computational Physics 432, 110152, 2021
172021
Networks for nonlinear diffusion problems in imaging
S Arridge, A Hauptmann
Journal of Mathematical Imaging and Vision 62, 471–487, 2020
172020
Direct inversion from partial-boundary data in electrical impedance tomography
A Hauptmann, M Santacesaria, S Siltanen
Inverse Problems 33 (2), 025009, 2017
152017
Revealing cracks inside conductive bodies by electric surface measurements
A Hauptmann, M Ikehata, H Itou, S Siltanen
Inverse Problems 35 (2), 2019
142019
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Artikelen 1–20