77 citations · 298 across the 18 of their papers we have counts for
5 papers · 1 filter
Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction
Mareike Thies, Fabian Wagner, Noah Maul +10
Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the proje…
Appearance Learning for Image-based Motion Estimation in Tomography
Alexander Preuhs, Michael Manhart, Philipp Roser +5
In tomographic imaging, anatomical structures are reconstructed by applying a pseudo-inverse forward model to acquired signals. Geometric information within this process is usually…
Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images
Yixing Huang, Alexander Preuhs, Michael Manhart +2
Image reconstruction from insufficient data is common in computed tomography (CT), e.g., image reconstruction from truncated data, limited-angle data and sparse-view data. Deep lea…
Image Quality Assessment for Rigid Motion Compensation
Alexander Preuhs, Michael Manhart, Philipp Roser +5
Diagnostic stroke imaging with C-arm cone-beam computed tomography (CBCT) enables reduction of time-to-therapy for endovascular procedures. However, the prolonged acquisition time…
Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior
Yixing Huang, Alexander Preuhs, Guenter Lauritsch +3
Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to uns…