You can also find my articles on my Google Scholar profile.


Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems
W. Herzberg, D. Rowe, A. Hauptmann, and S. Hamilton
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Fast Learned Spectral Computed Tomography Reconstruction
S. Inkinen, A. Hauptmann, M. Juntunen, M. Nieminen and S. Arridge

Sequentially optimized projections in X-ray imaging
M. Burger, A. Hauptmann, T. Helin, N. Hyvönen, JP Puska
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Joint Reconstruction and Low-Rank Decomposition for Dynamic Inverse Problems
S. Arridge, P. Fernsel, A. Hauptmann
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An efficient Quasi-Newton method for nonlinear inverse problems via learned singular values
D. Smyl, TN. Tallman, D. Liu, A. Hauptmann
Accepted for IEEE Signal Processing Letters, 2021.
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Fusing electrical and elasticity imaging
A. Hauptmann and D. Smyl
Accepted for Philosophical Transactions of the Royal Society A, 2021.
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Image reconstruction in dynamic inverse problems with temporal models
A. Hauptmann, O. Öktem, CB. Schönlieb
Accepted for Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2021. (Link)
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Machine Learning in Magnetic Resonance Imaging: Image Reconstruction
J. Montalt-Tordera, V. Muthurangu, A. Hauptmann, JA. Steeden
Published in Physica Medica, 2021. (Link)
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Learning and correcting non-Gaussian model errors
D. Smyl, TN. Tallman, JA. Black, A. Hauptmann, D. Liu
Published in Journal of Computational Physics, 2021. (Link)
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On Learned Operator Correction in Inverse Problems
S. Lunz, A. Hauptmann, T. Tarvainen, CB. Schönlieb, S. Arridge
Published in SIAM Journal on Imaging Sciences, 2021. (Link)
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Material Decomposition in Spectral CT using deep learning: A Sim2Real transfer approach
JFPJ. Abascal, N. Ducros, S. Rit, PA. Rodesch, T. Broussaud, S. Bussod, P. Douek, A. Hauptmann, S. Arridge, F. Peyrin
Published in IEEE Access, 2021. (Link)
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Convolutional Neural Network for Material Decomposition in Spectral CT scans
S. Bussod, JFP. Abascal, S. Arridge, A. Hauptmann, C. Chappard, N. Ducros, F. Peyrin
Published in 28th European Signal Processing Conference (EUSIPCO 2020), 2021. (Link)
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Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction
R. Barbano, Ž. Kereta, C. Zhang, A. Hauptmann, S. Arridge, B. Jin
Published in NeurIPS 2020 Deep Inverse Workshop, 2020. (Link)
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Blind hierarchical deconvolution
A. Arjas, L. Roininen, M. Sillanpää, A. Hauptmann
Published in IEEE Machine Learning and Signal Processing (MLSP), 2020. (Link)
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Deep Learning in Photoacoustic Tomography: Current approaches and future directions
A. Hauptmann, B. Cox
Published in Journal of Biomedical Optics, 2020. (Link)
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Towards accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in 3D
C. Bench, A. Hauptmann, B. Cox
Published in Journal of Biomedical Optics, 2020. (Link)
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Rapid Whole-Heart CMR with Single Volume Super-resolution
JA. Steeden, M. Quail, A. Gotschy, K. Mortensen, A. Hauptmann, S. Arridge, R. Jones, and V. Muthurangu
Published in Journal of Cardiovascular Magnetic Resonance, 2020. (Link)
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On the unreasonable effectiveness of CNNs
A. Hauptmann and J. Adler
Published in (non peer-reviewed) technical report on TechRxiv, 2020. (Link)
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Material Decomposition problem in spectral CT: A transfer deep learning approach
J. Abascal, N. Ducros, V. Pronina, S. Bussod, A. Hauptmann, S. Arridge, P. Douek, F. Peyrin
Published in 2020 IEEE ISBI Workshops: Deep Learning for Biomedical Image Reconstruction, 2020. (Link)
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Multi-Scale Learned Iterative Reconstruction
A. Hauptmann, J. Adler, S. Arridge, and O. Öktem
Published in IEEE Transactions on Computational Imaging, 2020. (Link)
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Estimation of dynamic SNP-heritability with Bayesian Gaussian process models
A. Arjas, A. Hauptmann, MJ. Sillanpää
Published in Bioinformatics, 2020. (Link)
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Networks for Nonlinear Diffusion Problems in Imaging
S. Arridge and A. Hauptmann
Published in Journal of Mathematical Imaging and Vision, 2020. (Link)
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Beltrami-Net: Domain Independent Deep D-bar Learning for Absolute Imaging with Electrical Impedance Tomography (a-EIT)
S. Hamilton, A. Hänninen, A. Hauptmann, and V. Kolehmainen
Published in Physiological Measurement, 2019. (Link)
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Application of Proximal Alternating Linearized Minimization (PALM) and inertial PALM to dynamic 3D CT
N. Djurabekova, A. Goldberg, A. Hauptmann, D. Hawkes, G. Long, F. Lucka, and M. Betcke
Published in Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019. (Link)

Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning - proof of concept in congenital heart disease (Editor's pick)
A. Hauptmann, S. Arridge, F. Lucka, V. Muthurangu, and J. Steeden
Published in Magnetic Resonance in Medicine, 2019. (Link)
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Revealing cracks inside conductive bodies by electric surface measurements
A. Hauptmann, M. Ikehata, H. Itou, and S. Siltanen
Published in Inverse Problems, 2019. (Link)
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Approximate k-space models and Deep Learning for fast photoacoustic reconstruction
A. Hauptmann, B. Cox, F. Lucka, N. Huynh, M. Betcke, P. Beard, and S. Arridge
Published in Machine Learning for Medical Image Reconstruction, 2018. (Link)
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Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks
SJ. Hamilton and A. Hauptmann
Published in IEEE Transactions on Medical Imaging, 2018. (Link)
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Model-based learning for accelerated, limited-view 3-d photoacoustic tomography
A. Hauptmann, F. Lucka, M. Betcke, N. Huynh, J. Adler, B. Cox, P. Beard, S. Ourselin, S. Arridge
Published in IEEE Transactions on Medical Imaging, 2018. (Link)
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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, and S. Siltanen
Published in Inverse Problems, 2017. (Link)
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Approximation of full-boundary data from partial-boundary electrode measurements
A. Hauptmann
Published in Inverse Problems, 2017. (Link)
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A Direct D-bar Method for Partial Boundary Data Electrical Impedance Tomography with A Priori Information
M. Alsaker, S. Hamilton, and A. Hauptmann
Published in Inverse Problems and Imaging, 2017. (Link)
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Direct inversion from partial-boundary data in electrical impedance tomography
A. Hauptmann, S. Santacesaria, S. Siltanen
Published in Inverse Problems, 2017. (Link)
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A Data-Driven Edge-Preserving D-bar Method for Electrical Impedance Tomography
S. Hamilton, A. Hauptmann, and S. Siltanen
Published in Inverse Problems and Imaging, 2014. (Link)
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Total variation regularization for large-scale X-ray tomography
K. Hämäläinen, L. Harhanen, A. Hauptmann, A. Kallonen, E. Niemi, and S. Siltanen
Published in International Journal of Tomography and Simulation, 2014.
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