activity
20232026
most citedImproving image quality of sparse-view lung tumor CT images with U-Net

8 citations · 15 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2026

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…

cs.CV2026

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…

cs.LG2026

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…

cs.CV2025

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…

physics.med-ph2025

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…

physics.med-ph2025★ 1 cited

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…

eess.IV2025

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…

physics.med-ph2024

Influence of Medical Foreign Bodies on Dark-Field Chest Radiographs: First experiences

Lennard Kaster, Henriette Klein, Alexander W. Marka +7

Objectives: Evaluating the effects and artifacts introduced by medical foreign bodies in clinical dark-field chest radiographs and assessing their influence on the evaluation of pu…

cs.CV2023★ 8 cited

Improving image quality of sparse-view lung tumor CT images with U-Net

Annika Ries, Tina Dorosti, Johannes Thalhammer +8

Background: We aimed at improving image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determining the best tradeoff be…

eess.IV2023★ 6 cited

Improving Automated Hemorrhage Detection in Sparse-view Computed Tomography via Deep Convolutional Neural Network based Artifact Reduction

Johannes Thalhammer, Manuel Schultheiss, Tina Dorosti +4

This is a preprint. The latest version has been published here: https://pubs.rsna.org/doi/10.1148/ryai.230275 Purpose: Sparse-view computed tomography (CT) is an effective way to r…