Andreas Hauptmann
Geciteerd door
Geciteerd door
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
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 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
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
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
International Workshop on Machine Learning for Medical Image Reconstruction …, 2018
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
Open 2D electrical impedance tomography data archive
A Hauptmann, V Kolehmainen, NM Mach, T Savolainen, A Seppänen, ...
arXiv preprint arXiv:1704.01178, 2017
Direct inversion from partial-boundary data in electrical impedance tomography
A Hauptmann, M Santacesaria, S Siltanen
Inverse Problems 33 (2), 025009, 2017
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
A Data-Driven Edge-Preserving D-bar Method for Electrical Impedance Tomography
S Hamilton, A Hauptmann, S Siltanen
Inverse Problems and Imaging 8 (4), 1053-1072, 2014
Networks for nonlinear diffusion problems in imaging
S Arridge, A Hauptmann
Journal of Mathematical Imaging and Vision, 471–487, 2020
Revealing cracks inside conductive bodies by electric surface measurements
A Hauptmann, M Ikehata, H Itou, S Siltanen
Inverse Problems 35 (2), 2019
Approximation of full-boundary data from partial-boundary electrode measurements
A Hauptmann
Inverse Problems 33 (12), 125017, 2017
Multi-scale learned iterative reconstruction
A Hauptmann, J Adler, SR Arridge, O Oktem
IEEE Transactions on Computational Imaging, 2020
Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions
C Bench, A Hauptmann, BT Cox
Journal of Biomedical Optics 25 (8), 085003, 2020
On learned operator correction
S Lunz, A Hauptmann, T Tarvainen, CB Schönlieb, S Arridge
arXiv preprint arXiv:2005.07069, 2020
Deep Learning in Photoacoustic Tomography: Current approaches and future directions
A Hauptmann, B Cox
arXiv preprint arXiv:2009.07608, 2020
On the unreasonable effectiveness of CNNs
A Hauptmann, J Adler
arXiv preprint arXiv:2007.14745, 2020
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Artikelen 1–20