347 citations · 987 across the 24 of their papers we have counts for
29 papers
Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs
Sebastian Springer, Aldo Glielmo, Angelina Senchukova +5
In industrial applications, it is common to scan objects on a moving conveyor belt. If slice-wise 2D computed tomography (CT) measurements of the moving object are obtained we call…
Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data
Arttu Arjas, Erwin J. Alles, Efthymios Maneas +4
Many interventional surgical procedures rely on medical imaging to visualise and track instruments. Such imaging methods not only need to be real-time capable, but also provide acc…
Unsupervised Knowledge-Transfer for Learned Image Reconstruction
Riccardo Barbano, Zeljko Kereta, Andreas Hauptmann +2
Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities. These approaches usually require a large amount o…
A model-based iterative learning approach for diffuse optical tomography
Meghdoot Mozumder, Andreas Hauptmann, Ilkka Nissilä +2
Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image…
Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems
William Herzberg, Daniel B. Rowe, Andreas Hauptmann +1
The majority of model-based learned image reconstruction methods in medical imaging have been limited to uniform domains, such as pixelated images. If the underlying model is solve…
Machine Learning in Magnetic Resonance Imaging: Image Reconstruction
Javier Montalt-Tordera, Vivek Muthurangu, Andreas Hauptmann +1
Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20…