Model based learning for accelerated, limited-view 3D photoacoustic tomography. - Andreas Hauptmann; Felix Lucka; Marta M. Betcke; Nam Huynh; Ben T. Cox; Paul C. Beard; Sébastien Ourselin; Simon R. Arridge (CoRR, 2017)

Approximate k-Space Models and Deep Learning for Fast Photoacoustic Reconstruction. - Andreas Hauptmann; Ben T. Cox; Felix Lucka; Nam Huynh; Marta M. Betcke; Paul C. Beard; Simon R. Arridge (MLMIR@MICCAI, 2018)

Real-time Cardiovascular MR with Spatio-temporal De-aliasing using Deep Learning - Proof of Concept in Congenital Heart Disease. - Andreas Hauptmann; Simon R. Arridge; Felix Lucka; Vivek Muthurangu; Jennifer Anne Steeden (CoRR, 2018)

Revealing cracks inside conductive bodies by electric surface measurements. - Andreas Hauptmann; Masaru Ikehata; Hiromichi Itou; Samuli Siltanen (CoRR, 2018)

Beltrami-Net: Domain Independent Deep D-bar Learning for Absolute Imaging with Electrical Impedance Tomography (a-EIT). - Sarah Jane Hamilton; Asko Hänninen; Andreas Hauptmann; Ville Kolehmainen (CoRR, 2018)

Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks. - Sarah Jane Hamilton; Andreas Hauptmann (IEEE Trans. Medical Imaging, 2018)

Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography. - Andreas Hauptmann; Felix Lucka; Marta M. Betcke; Nam Huynh; Jonas Adler; Ben T. Cox; Paul C. Beard; Sébastien Ourselin; Simon R. Arridge (IEEE Trans. Medical Imaging, 2018)

Rapid Whole-Heart CMR with Single Volume Super-resolution. - Jennifer Anne Steeden; Michael Quail; Alexander Gotschy; Andreas Hauptmann; Simon R. Arridge; Rodney Jones; Vivek Muthurangu (CoRR, 2019)

Convolutional Neural Network for Material Decomposition in Spectral CT Scans. - Suzanne Bussod; Juan F. P. J. Abascal; Simon R. Arridge; Andreas Hauptmann; Christine Chappard; Nicolas Ducros; Françoise Peyrin (EUSIPCO, 2020)

Material Decomposition Problem in Spectral CT: A Transfer Deep Learning Approach. - Juan F. P. J. Abascal; Nicolas Ducros; Valeriya Pronina; Suzanne Bussod; Andreas Hauptmann; Simon R. Arridge; Philippe Douek; Françoise Peyrin (ISBI Workshops, 2020)

Blind Hierarchical Deconvolution. - Arttu Arjas; Lassi Roininen; Mikko J. Sillanpää; Andreas Hauptmann (MLSP, 2020)

Estimation of dynamic SNP-heritability with Bayesian Gaussian process models. - Arttu Arjas; Andreas Hauptmann; Mikko J. Sillanpää (Bioinform., 2020)

On Learned Operator Correction. - Sebastian Lunz; Andreas Hauptmann; Tanja Tarvainen; Carola-Bibiane Schönlieb; Simon R. Arridge (CoRR, 2020)

Joint Reconstruction and Low-Rank Decomposition for Dynamic Inverse Problems. - Simon Robert Arridge; Pascal Fernsel; Andreas Hauptmann (CoRR, 2020)

Sequentially optimized projections in X-ray imaging. - Martin Burger 001; Andreas Hauptmann; Tapio Helin; Nuutti Hyvönen; Juha-Pekka Puska (CoRR, 2020)

Image reconstruction in dynamic inverse problems with temporal models. - Andreas Hauptmann; Ozan Öktem; Carola Schönlieb (CoRR, 2020)

On the unreasonable effectiveness of CNNs. - Andreas Hauptmann; Jonas Adler (CoRR, 2020)

Deep Learning in Photoacoustic Tomography: Current approaches and future directions. - Andreas Hauptmann; Ben T. Cox (CoRR, 2020)

Fusing electrical and elasticity imaging. - Andreas Hauptmann; Danny Smyl (CoRR, 2020)

Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction. - Riccardo Barbano; Zeljko Kereta; Chen Zhang 023; Andreas Hauptmann; Simon R. Arridge; Bangti Jin (CoRR, 2020)

Machine Learning in Magnetic Resonance Imaging: Image Reconstruction. - Javier Montalt-Tordera; Vivek Muthurangu; Andreas Hauptmann; Jennifer Anne Steeden (CoRR, 2020)

Networks for Nonlinear Diffusion Problems in Imaging. - Simon R. Arridge; Andreas Hauptmann (J. Math. Imaging Vis., 2020)

Multi-Scale Learned Iterative Reconstruction. - Andreas Hauptmann; Jonas Adler; Simon R. Arridge; Ozan Öktem (IEEE Trans. Computational Imaging, 2020)

Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach. - Juan F. P. J. Abascal; Nicolas Ducros; Valeriya Pronina; Simon Rit; Pierre-Antoine Rodesch; Thomas Broussaud; Suzanne Bussod; Philippe C. Douek; Andreas Hauptmann; Simon R. Arridge; Françoise Peyrin (IEEE Access, 2021)

Unsupervised Knowledge-Transfer for Learned Image Reconstruction. - Riccardo Barbano; Zeljko Kereta; Andreas Hauptmann; Simon R. Arridge; Bangti Jin (CoRR, 2021)

Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data. - Arttu Arjas; Erwin J. Alles; Efthymios Maneas; Simon R. Arridge; Adrien E. Desjardins; Mikko J. Sillanpää; Andreas Hauptmann (CoRR, 2021)

Is Deep Image Prior in Need of a Good Education? - Riccardo Barbano; Johannes Leuschner; Maximilian Schmidt; Alexander Denker; Andreas Hauptmann; Peter Maaß; Bangti Jin (CoRR, 2021)

Learning and correcting non-Gaussian model errors. - Danny Smyl; Tyler N. Tallman; Jonathan A. Black; Andreas Hauptmann; Dong Liu 007 (J. Comput. Phys., 2021)

On Learned Operator Correction in Inverse Problems. - Sebastian Lunz; Andreas Hauptmann; Tanja Tarvainen; Carola-Bibiane Schönlieb; Simon R. Arridge (SIAM J. Imaging Sci., 2021)

An Efficient Quasi-Newton Method for Nonlinear Inverse Problems via Learned Singular Values. - Danny Smyl; Tyler N. Tallman; Dong Liu 007; Andreas Hauptmann (IEEE Signal Process. Lett., 2021)

Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems. - William Herzberg; Daniel B. Rowe; Andreas Hauptmann; Sarah Jane Hamilton (IEEE Trans. Computational Imaging, 2021)

Learned reconstruction with convergence guarantees. - Subhadip Mukherjee; Andreas Hauptmann; Ozan Öktem; Marcelo Pereyra; Carola-Bibiane Schönlieb (CoRR, 2022)

Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs. - Sebastian Springer; Aldo Glielmo; Angelina Senchukova; Tomi Kauppi; Jarkko Suuronen; Lassi Roininen; Heikki Haario; Andreas Hauptmann (CoRR, 2022)

Sparsity promoting reconstructions via hierarchical prior models in diffuse optical tomography. - Anssi Manninen; Meghdoot Mozumder; Tanja Tarvainen; Andreas Hauptmann (CoRR, 2022)

Unsupervised denoising for sparse multi-spectral computed tomography. - Satu I. Inkinen; Mikael A. K. Brix; Miika T. Nieminen; Simon R. Arridge; Andreas Hauptmann (CoRR, 2022)

An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction. - Riccardo Barbano; Johannes Leuschner; Maximilian Schmidt; Alexander Denker; Andreas Hauptmann; Peter Maass; Bangti Jin (IEEE Trans. Computational Imaging, 2022)

A Model-Based Iterative Learning Approach for Diffuse Optical Tomography. - Meghdoot Mozumder; Andreas Hauptmann; Ilkka Nissilä; Simon R. Arridge; Tanja Tarvainen (IEEE Trans. Medical Imaging, 2022)

Guest Editorial: MLSP 2020 Special Issue. - Simo Särkkä; Lassi Roininen; Manon Kok; Roland Hostettler; Andreas Hauptmann (J. Signal Process. Syst., 2022)

Robust Data-Driven Accelerated Mirror Descent. - Hong Ye Tan; Subhadip Mukherjee; Junqi Tang; Andreas Hauptmann; Carola-Bibiane Schönlieb (ICASSP, 2023)

Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography. - Andreas Hauptmann; Jenni Poimala (CoRR, 2023)

Domain independent post-processing with graph U-nets: Applications to Electrical Impedance Tomographic Imaging. - William Herzberg; Andreas Hauptmann; Sarah Jane Hamilton (CoRR, 2023)

Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches. - Derick Nganyu Tanyu; Jianfeng Ning; Andreas Hauptmann; Bangti Jin; Peter Maass (CoRR, 2023)

Inverse Problems with Learned Forward Operators. - Simon R. Arridge; Andreas Hauptmann; Yury Korolev (CoRR, 2023)

Model-based reconstructions for quantitative imaging in photoacoustic tomography. - Andreas Hauptmann; Tanja Tarvainen (CoRR, 2023)

Learned Reconstruction Methods With Convergence Guarantees: A survey of concepts and applications. - Subhadip Mukherjee; Andreas Hauptmann; Ozan Öktem; Marcelo Pereyra; Carola-Bibiane Schönlieb (IEEE Signal Process. Mag., 2023)

Enhancement of instrumented ultrasonic tracking images using deep learning. - Efthymios Maneas; Andreas Hauptmann; Erwin J. Alles; Wenfeng Xia 001; Sacha Noimark; Anna L. David; Simon R. Arridge; Adrien E. Desjardins (Int. J. Comput. Assist. Radiol. Surg., 2023)

Sequential Model Correction for Nonlinear Inverse Problems. - Arttu Arjas; Mikko J. Sillanpää; Andreas Hauptmann (SIAM J. Imaging Sci., 2023)

A Multi-Filter and Multi-Scale U-Net for Cone-Beam Computed Tomography with Hardware Constraints. - Andreas Hauptmann; Mustafa Al-Rubaye; Miika T. Nieminen; Mikael A. K. Brix (ICASSP Workshops, 2024)

Self-Supervised Denoiser Framework. - Emilien Valat; Andreas Hauptmann; Ozan Öktem (CoRR, 2024)

Graph convolutional networks enable fast hemorrhagic stroke monitoring with electrical impedance tomography. - Jussi Toivanen; Ville Kolehmainen; Antti Paldanius; Asko Hänninen; Andreas Hauptmann; Sarah Jane Hamilton (CoRR, 2024)

Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications. - Leatile Marata; Onel Luis Alcaraz López; Andreas Hauptmann; Hamza Djelouat; Hirley Alves (IEEE Trans. Wirel. Commun., 2024)

Learned enclosure method for experimental EIT data. - Sara Sippola; Siiri Rautio; Andreas Hauptmann; Takanori Ide; Samuli Siltanen (CoRR, 2025)

Digital twins enable full-reference quality assessment of photoacoustic image reconstructions. - Janek Gröhl; Leonid Kunyansky; Jenni Poimala; Thomas R. Else; Francesca Di Cecio; Sarah E. Bohndiek; Ben T. Cox; Andreas Hauptmann (CoRR, 2025)

Regularization for time-dependent inverse problems: Geometry of Lebesgue-Bochner spaces and algorithms. - Gesa Sarnighausen; Thorsten Hohage; Martin Burger 001; Andreas Hauptmann; Anne Wald (CoRR, 2025)

Convergent Regularization in Inverse Problems and Linear Plug-and-Play Denoisers. - Andreas Hauptmann; Subhadip Mukherjee; Carola-Bibiane Schönlieb; Ferdia Sherry (Found. Comput. Math., 2025)