7 papers
Artifact Reduction in Undersampled 3D Cone-Beam CTs using a Hybrid 2D-3D CNN Framework
Johannes Thalhammer, Tina Dorosti, Sebastian Peterhansl +3
Undersampled CT volumes minimize acquisition time and radiation exposure but introduce artifacts degrading image quality and diagnostic utility. Reducing these artifacts is critica…
Uncertainty-guided Generation of Dark-field Radiographs
Lina Felsner, Henriette Bast, Tina Dorosti +4
X-ray dark-field radiography provides complementary diagnostic information to conventional attenuation imaging by visualizing microstructural tissue changes through small-angle sca…
Beam Geometry and Input Dimensionality: Impact on Sparse-Sampling Artifact Correction for Clinical CT with U-Nets
Tina Dorosti, Johannes Thalhammer, Sebastian Peterhansl +3
This study aims to investigate the effect of various beam geometries and dimensions of input data on the sparse-sampling streak artifact correction task with U-Nets for clinical CT…
Look-Up Table-Correction for Beam Hardening-Induced Signal of Clinical Dark-Field Chest Radiographs
Maximilian E. Lochschmidt, Theresa Urban, Lennard Kaster +4
Background: Material structures at the micrometer scale cause ultra-small-angle X-ray scattering, e.g., seen in lung tissue or plastic foams. In grating-based X-ray imaging, this c…
Estimating Total Lung Volume from Pixel-level Thickness Maps of Chest Radiographs Using Deep Learning
Tina Dorosti, Manuel Schultheiss, Philipp Schmette +11
Purpose: To estimate the total lung volume (TLV) from real and synthetic frontal chest radiographs (CXR) on a pixel level using lung thickness maps generated by a U-Net deep learni…
Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment
Fabian Drexel, Vasiliki Sideri-Lampretsa, Henriette Bast +6
Dark-field radiography of the human chest has been demonstrated to have promising potential for the analysis of the lung microstructure and the diagnosis of respiratory diseases. H…