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Andreas Hauptmann
Researcher at University of Oulu
Publications - 80
Citations - 1488
Andreas Hauptmann is an academic researcher from University of Oulu. The author has contributed to research in topics: Iterative reconstruction & Computer science. The author has an hindex of 14, co-authored 66 publications receiving 954 citations. Previous affiliations of Andreas Hauptmann include University of Helsinki & University College London.
Papers
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Journal ArticleDOI
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,Sebastien Ourselin,Simon R. Arridge +8 more
TL;DR: A deep neural network is presented that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artifacts.
Journal ArticleDOI
Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks
Sarah Hamilton,Andreas Hauptmann +1 more
TL;DR: In this article, D-bar methods are used to provide robust direct reconstructions by using a low-pass filtering of the associated nonlinear Fourier data for electrical impedance tomography (EIT) images.
Journal ArticleDOI
Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease
Andreas Hauptmann,Simon R. Arridge,Felix Lucka,Felix Lucka,Vivek Muthurangu,Jennifer A. Steeden +5 more
TL;DR: In this article, a 3D (2D plus time) CNN architecture was developed and trained using synthetic training data created from previously acquired breath hold cine images from 250 CHD patients.
Journal ArticleDOI
Deep learning in photoacoustic tomography: current approaches and future directions
TL;DR: In this paper, a review of learned image reconstruction is presented, summarizing the current trends and explaining how these approaches fit within, and to some extent have arisen from, a framework that encompasses classical reconstruction methods.
Journal ArticleDOI
Model based learning for accelerated, limited-view 3D photoacoustic tomography
Andreas Hauptmann,Felix Lucka,Marta M. Betcke,Nam Huynh,Jonas Adler,Ben T. Cox,Paul C. Beard,Sebastien Ourselin,Simon R. Arridge +8 more
TL;DR: In this article, a deep neural network is designed to provide high resolution 3D images from restricted photoacoustic measurements, and the resulting network is trained and tested on a set of segmented vessels from lung CT scans and then applied to in-vivo photo-acoustic measurement data.