8 citations · 17 across the 5 of their papers we have counts for
5 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…
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…
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…
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…