no code implementations • 21 Nov 2023 • Simon Arridge, Andreas Hauptmann, Yury Korolev
The first one is completely agnostic to the forward operator and learns its restriction to the subspace spanned by the training data.
1 code implementation • 28 Oct 2023 • Derick Nganyu Tanyu, Jianfeng Ning, Andreas Hauptmann, Bangti Jin, Peter Maass
A suite of performance metrics is employed to assess the efficacy of these methods.
no code implementations • 18 Jul 2023 • Andreas Hauptmann, Subhadip Mukherjee, Carola-Bibiane Schönlieb, Ferdia Sherry
While a significant amount of research has gone into establishing the convergence of the PnP iteration for different regularity conditions on the denoisers, not much is known about the asymptotic properties of the converged solution as the noise level in the measurement tends to zero, i. e., whether PnP methods are provably convergent regularization schemes under reasonable assumptions on the denoiser.
no code implementations • 8 May 2023 • William Herzberg, Andreas Hauptmann, Sarah J. Hamilton
We demonstrate effectiveness and flexibility of the graph U-Net for improving reconstructions from electrical impedance tomographic (EIT) measurements, a nonlinear and highly ill-posed inverse problem.
no code implementations • 4 May 2023 • Leatile Marata, Onel Luis Alcaraz López, Andreas Hauptmann, Hamza Djelouat, Hirley Alves
Compressed sensing multi-user detection (CS-MUD) algorithms play a key role in optimizing grant-free (GF) non-orthogonal multiple access (NOMA) for massive machine-type communications (mMTC).
no code implementations • 4 Apr 2023 • Andreas Hauptmann, Jenni Poimala
In this work we advance the methodological and theoretical basis for model corrections in learned image reconstructions by embedding the model correction in a learned primal-dual framework.
no code implementations • 2 Nov 2022 • Satu I. Inkinen, Mikael A. K. Brix, Miika T. Nieminen, Simon Arridge, Andreas Hauptmann
However, these issues are especially exacerbated when sparse imaging scenarios are encountered due to a significant reduction in photon counts.}
no code implementations • 20 Jun 2022 • Sebastian Springer, Aldo Glielmo, Angelina Senchukova, Tomi Kauppi, Jarkko Suuronen, Lassi Roininen, Heikki Haario, Andreas Hauptmann
Thus, here we propose the use of a Dimension reduced Kalman Filter to accumulate information between slices and allow for sufficiently accurate reconstructions for further assessment of the object.
no code implementations • 11 Jun 2022 • Subhadip Mukherjee, Andreas Hauptmann, Ozan Öktem, Marcelo Pereyra, Carola-Bibiane Schönlieb
In recent years, deep learning has achieved remarkable empirical success for image reconstruction.
3 code implementations • 23 Nov 2021 • Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt, Alexander Denker, Andreas Hauptmann, Peter Maaß, Bangti Jin
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks.
no code implementations • 18 Nov 2021 • Arttu Arjas, Erwin J. Alles, Efthymios Maneas, Simon Arridge, Adrien Desjardins, Mikko J. Sillanpää, Andreas Hauptmann
Many interventional surgical procedures rely on medical imaging to visualise and track instruments.
no code implementations • 6 Jul 2021 • Riccardo Barbano, Zeljko Kereta, Andreas Hauptmann, Simon R. Arridge, Bangti Jin
Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities.
1 code implementation • 28 Mar 2021 • William Herzberg, Daniel B. Rowe, Andreas Hauptmann, Sarah J. Hamilton
This gives rise to the proposed iterative Graph Convolutional Newton-type Method (GCNM), which includes the forward model in the solution of the inverse problem, while all updates are directly computed by the network on the problem specific mesh.
no code implementations • 14 Dec 2020 • Danny Smyl, Tyler N. Tallman, Dong Liu, Andreas Hauptmann
Here we present a highly efficient data-driven Quasi-Newton method applicable to nonlinear inverse problems.
no code implementations • 9 Dec 2020 • Javier Montalt-Tordera, Vivek Muthurangu, Andreas Hauptmann, Jennifer Anne Steeden
Following the success of machine learning in a wide range of imaging tasks, there has been a recent explosion in the use of machine learning in the field of MRI image reconstruction.
no code implementations • 17 Nov 2020 • Riccardo Barbano, Željko Kereta, Chen Zhang, Andreas Hauptmann, Simon Arridge, Bangti Jin
Image reconstruction methods based on deep neural networks have shown outstanding performance, equalling or exceeding the state-of-the-art results of conventional approaches, but often do not provide uncertainty information about the reconstruction.
1 code implementation • 16 Sep 2020 • Andreas Hauptmann, Ben Cox
The need for rapid image formation and the practical restrictions on data acquisition that arise from the constraints of a clinical workflow are presenting new image reconstruction challenges.
no code implementations • 29 Jul 2020 • Andreas Hauptmann, Jonas Adler
Deep learning methods using convolutional neural networks (CNN) have been successfully applied to virtually all imaging problems, and particularly in image reconstruction tasks with ill-posed and complicated imaging models.
no code implementations • 22 Jul 2020 • Arttu Arjas, Lassi Roininen, Mikko J. Sillanpää, Andreas Hauptmann
Deconvolution is a fundamental inverse problem in signal processing and the prototypical model for recovering a signal from its noisy measurement.
1 code implementation • 14 May 2020 • Sebastian Lunz, Andreas Hauptmann, Tanja Tarvainen, Carola-Bibiane Schönlieb, Simon Arridge
We discuss the possibility to learn a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to obtain regularised reconstructions.
no code implementations • 22 Dec 2019 • Jennifer A. Steeden, Michael Quail, Alexander Gotschy, Andreas Hauptmann, Simon Arridge, Rodney Jones, Vivek Muthurangu
Conclusion: This paper demonstrates the potential of using a residual U-Net for super-resolution reconstruction of rapidly acquired low-resolution whole heart bSSFP data within a clinical setting.
1 code implementation • 1 Aug 2019 • Andreas Hauptmann, Jonas Adler, Simon Arridge, Ozan Öktem
Applicability of these methods to large scale inverse problems is however limited by the available memory for training and extensive training times, the latter due to computationally expensive forward models.
no code implementations • 29 Nov 2018 • Simon Arridge, Andreas Hauptmann
By design, we obtain a nonlinear network architecture that is well suited for diffusion related problems in imaging.
no code implementations • 9 Jul 2018 • Andreas Hauptmann, Ben Cox, Felix Lucka, Nam Huynh, Marta Betcke, Paul Beard, Simon Arridge
We present a framework for accelerated iterative reconstructions using a fast and approximate forward model that is based on k-space methods for photoacoustic tomography.
no code implementations • 14 Mar 2018 • Andreas Hauptmann, Simon Arridge, Felix Lucka, Vivek Muthurangu, Jennifer A. Steeden
In this study we investigated the effect of different radial sampling patterns on the accuracy of a CNN.
no code implementations • 8 Nov 2017 • Sarah Jane Hamilton, Andreas Hauptmann
The mathematical problem for Electrical Impedance Tomography (EIT) is a highly nonlinear ill-posed inverse problem requiring carefully designed reconstruction procedures to ensure reliable image generation.
no code implementations • 31 Aug 2017 • Andreas Hauptmann, Felix Lucka, Marta Betcke, Nam Huynh, Jonas Adler, Ben Cox, Paul Beard, Sebastien Ourselin, Simon Arridge
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up.