13 citations · 30 across the 9 of their papers we have counts for
9 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…
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…
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…