48 citations · 79 across the 12 of their papers we have counts for
12 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…
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…
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…
Towards AI Lesion Tracking in PET/CT Imaging: A Siamese-based CNN Pipeline applied on PSMA PET/CT Scans
Stefan P. Hein, Manuel Schultheiss, Andrei Gafita +7
Assessing tumor response to systemic therapies is one of the main applications of PET/CT. Routinely, only a small subset of index lesions out of multiple lesions is analyzed. Howev…
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…
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…
Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging
Tina Dorosti, Manuel Schultheiss, Felix Hofmann +7
We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by explorin…
WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer
Theodor Cheslerean-Boghiu, Felix C. Hofmann, Manuel Schultheiß +3
Deep learning based solutions are being succesfully implemented for a wide variety of applications. Most notably, clinical use-cases have gained an increased interest and have been…
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…