8 papers
Neighbor2Inverse: Self-Supervised Denoising for Low-Dose Region-of-Interest Phase Contrast CT
Johannes B. Thalhammer, Lorenzo D'Amico, Lucy Costello +11
Propagation-based X-ray phase-contrast imaging (PBI) enables high-contrast visualization of lung structures and holds strong medical potential. However, safe translation to the cli…
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…
Beam Hardening Correction in Clinical X-ray Dark-Field Chest Radiography using Deep Learning-Based Bone Segmentation
Lennard Kaster, Maximilian E. Lochschmidt, Anne M. Bauer +5
Dark-field radiography is a novel X-ray imaging modality that enables complementary diagnostic information by visualizing the microstructural properties of lung tissue. Implemented…
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…