Publications

Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications
L. Marata, O.L.A. López, A. Hauptmann, H. Djelouat, H. Alves
Download here

Domain independent post-processing with graph U-nets: Applications to Electrical Impedance Tomographic Imaging
W. Herzberg, A. Hauptmann, S.J. Hamilton

Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography
A. Hauptmann and J. Poimala
Download here

Unsupervised denoising for sparse multi-spectral computed tomography
S. I. Inkinen, M. A. K. Brix, M. T. Nieminen, S. Arridge, A. Hauptmann
Download here

Compensating unknown speed of sound in learned fast 3D limited-view photoacoustic tomography
J. Poimala, B. Cox, A. Hauptmann

Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs
S. Springer, A. Glielmo, A. Senchukova, T. Kauppi, J. Suuronen, L. Roininen, H. Haario, A. Hauptmann
Accepted for Applied Mathematics for Modern Challenges, 2023.
Download here

Sequential model correction for nonlinear inverse problems
A. Arjas, MJ. Sillanpää, A. Hauptmann
Accepted for SIAM Journal on Imaging Science, 2023.
Download here

Sparsity promoting reconstructions via hierarchical prior models in diffuse optical tomography
A. Manninen, M. Mozumder, T. Tarvainen, A. Hauptmann
Accepted for Inverse Problems and Imaging, 2023.
Download here

Robust Data-Driven Accelerated Mirror Descent
H.Y. Tan, S. Mukherjee, J. Tang, A. Hauptmann, CB. Schönlieb
Published in IEEE International Conference on Acoustics, Speech and Signal Processing, 2023. (Link)
Download here

Learned Reconstruction Methods With Convergence Guarantees: A survey of concepts and applications
S. Mukherjee, A. Hauptmann, O. Öktem, M. Pereyra, CB. Schönlieb
Published in IEEE Signal Processing Magazine, 2023. (Link)
Download here

Enhancement of instrumented ultrasonic tracking images using deep learning
E. Maneas, A. Hauptmann, EJ. Alles, W. Xia, S. Noimark, AL. David, S. Arridge, and AE. Desjardins
Published in International Journal of Computer Assisted Radiology and Surgery, 2023. (Link)
Download here

An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
R. Barbano, J. Leuschner, M. Schmidt, A. Denker, A. Hauptmann, P. Maaß, B. Jin
Published in IEEE Transactions on Computational Imaging, 2022. (Link)
Download here

Unsupervised Knowledge-Transfer for Learned Image Reconstruction
R. Barbano, Z. Kereta, A. Hauptmann, S. Arridge, B. Jin
Published in Inverse Problems, 2022. (Link)
Download here

Hierarchical Deconvolution for Incoherent Scatter Radar Data
S. Ross, A. Arjas, I. Virtanen, M. Sillanpää, L. Roininen, A. Hauptmann
Published in Atmospheric Measurement Techniques, 2022. (Link)
Download here

Joint Reconstruction and Low-Rank Decomposition for Dynamic Inverse Problems
S. Arridge, P. Fernsel, A. Hauptmann
Published in Inverse Problems and Imaging, 2022. (Link)
Download here

Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data
A. Arjas, EJ. Alles, E. Maneas, S. Arridge, AE. Desjardins, MJ. Sillanpää, A. Hauptmann
Published in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2022. (Link)
Download here

A model-based iterative learning approach for diffuse optical tomography
M. Mozumder, A. Hauptmann, I. Nissilä, S. Arridge, T. Tarvainen
Published in IEEE Transactions on Medical Imaging, 2022. (Link)
Download here

NeuralLasso: Neural Networks Meet Lasso in Genomic Prediction
B. Mathew, A. Hauptmann, J. Léon, MJ. Sillanpää
Published in Frontiers in Plant Science, 2022. (Link)
Download here

Deep Learning for Instrumented Ultrasonic Tracking: From synthetic training data to in vivo application
E. Maneas, A. Hauptmann, EJ. Alles, W. Xia, T. Vercauteren, S. Ourselin, AL. David, S. Arridge, and AE. Desjardins
Published in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2022. (Link)
Download here

Structural engineering from an inverse problems perspective
A. Gallet, S. Rigby, T. Tallman, X. Kong, I. Hajirasouliha, A. Liew, D. Liu, L. Chen, A. Hauptmann, D. Smyl
Published in Proceedings of the Royal Society A, 2022. (Link)
Download here

Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems
W. Herzberg, D. Rowe, A. Hauptmann, and S. Hamilton
Published in IEEE Transactions on Computational Imaging, 2021. (Link)
Download here

Sequentially optimized projections in X-ray imaging
M. Burger, A. Hauptmann, T. Helin, N. Hyvönen, JP Puska
Published in Inverse Problems, 2021. (Link)
Download here

Fusing electrical and elasticity imaging
A. Hauptmann and D. Smyl
Published in Philosophical Transactions of the Royal Society A, 2021. (Link)
Download here

An efficient Quasi-Newton method for nonlinear inverse problems via learned singular values
D. Smyl, TN. Tallman, D. Liu, A. Hauptmann
Published in IEEE Signal Processing Letters, 2021. (Link)
Download here

Machine Learning in Magnetic Resonance Imaging: Image Reconstruction
J. Montalt-Tordera, V. Muthurangu, A. Hauptmann, JA. Steeden
Published in Physica Medica, 2021. (Link)
Download here

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)
Download here

Image reconstruction in dynamic inverse problems with temporal models
A. Hauptmann, O. Öktem, CB. Schönlieb
Published in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2021. (Link)
Download here

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)
Download here

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)
Download here

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)
Download here

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)
Download here

Blind hierarchical deconvolution
A. Arjas, L. Roininen, M. Sillanpää, A. Hauptmann
Published in IEEE Machine Learning and Signal Processing (MLSP), 2020. (Link)
Download here

Deep Learning in Photoacoustic Tomography: Current approaches and future directions
A. Hauptmann, B. Cox
Published in Journal of Biomedical Optics, 2020. (Link)
Download here

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)
Download here

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)
Download here

On the unreasonable effectiveness of CNNs
A. Hauptmann and J. Adler
Published in (non peer-reviewed) technical report on TechRxiv, 2020. (Link)
Download here

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)
Download here

Multi-Scale Learned Iterative Reconstruction
A. Hauptmann, J. Adler, S. Arridge, and O. Öktem
Published in IEEE Transactions on Computational Imaging, 2020. (Link)
Download here

Estimation of dynamic SNP-heritability with Bayesian Gaussian process models
A. Arjas, A. Hauptmann, MJ. Sillanpää
Published in Bioinformatics, 2020. (Link)
Download here

Networks for Nonlinear Diffusion Problems in Imaging
S. Arridge and A. Hauptmann
Published in Journal of Mathematical Imaging and Vision, 2020. (Link)
Download here

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)
Download here

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)
Download here

Revealing cracks inside conductive bodies by electric surface measurements
A. Hauptmann, M. Ikehata, H. Itou, and S. Siltanen
Published in Inverse Problems, 2019. (Link)
Download here

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)
Download here

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)
Download here

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)
Download here

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)
Download here

Approximation of full-boundary data from partial-boundary electrode measurements
A. Hauptmann
Published in Inverse Problems, 2017. (Link)
Download here

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)
Download here

Direct inversion from partial-boundary data in electrical impedance tomography
A. Hauptmann, S. Santacesaria, S. Siltanen
Published in Inverse Problems, 2017. (Link)
Download here

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)
Download here

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.
Download here