Alpha 2.0
Login Register
» Authors » Elias Eulig

Elias Eulig

Explore the profile of Elias Eulig including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 8
Citations 52
Followers 0
Related Specialty
Biophysics
Top 10 Co-Authors
Marc Kachelriess
Joscha Maier
Michael Knaup
Stefan Sawall
Michael Lell
Klaus Horndler
Bjorn Ommer
Tim Voth
Adam S Wang
Eric Fournie
Published In
Med Phys
Affiliations
Soon will be listed here.
Recent Articles
1.
Reconstructing and analyzing the invariances of low-dose CT image denoising networks
Eulig E, Jager F, Maier J, Ommer B, Kachelriess M
Med Phys . 2024 Sep; 52(1):188-200. PMID: 39348044
Background: Deep learning-based methods led to significant advancements in many areas of medical imaging, most of which are concerned with the reduction of artifacts caused by motion, scatter, or noise....
2.
Benchmarking deep learning-based low-dose CT image denoising algorithms
Eulig E, Ommer B, Kachelriess M
Med Phys . 2024 Sep; 51(12):8776-8788. PMID: 39287517
Background: Long-lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography (CT) acquisitions without severe deterioration of image quality....
3.
Training of a deep learning based digital subtraction angiography method using synthetic data
Duan L, Eulig E, Knaup M, Adamus R, Lell M, Kachelriess M
Med Phys . 2024 Feb; 51(7):4793-4810. PMID: 38353632
Background: Digital subtraction angiography (DSA) is a fluoroscopy method primarily used for the diagnosis of cardiovascular diseases (CVDs). Deep learning-based DSA (DDSA) is developed to extract DSA-like images directly from...
4.
Real-time 3D reconstruction of guidewires and stents using two update X-ray projections in a rotating imaging setup
Voth T, Koenig T, Eulig E, Knaup M, Wiesmann V, Horndler K, et al.
Med Phys . 2023 Jul; 50(9):5312-5330. PMID: 37458680
Background: Vascular diseases are often treated minimally invasively. The interventional material (stents, guidewires, etc.) used during such percutaneous interventions are visualized by some form of image guidance. Today, this image...
5.
Real-time estimation of patient-specific dose distributions for medical CT using the deep dose estimation
Maier J, Klein L, Eulig E, Sawall S, Kachelriess M
Med Phys . 2022 Feb; 49(4):2259-2269. PMID: 35107176
Purpose: With the rising number of computed tomography (CT) examinations and the trend toward personalized medicine, patient-specific dose estimates are becoming more and more important in CT imaging. However, current...
6.
Deep learning-based reconstruction of interventional tools and devices from four X-ray projections for tomographic interventional guidance
Eulig E, Maier J, Knaup M, Bennett N, Horndler K, Wang A, et al.
Med Phys . 2021 Aug; 48(10):5837-5850. PMID: 34387362
Purpose: Image guidance for minimally invasive interventions is usually performed by acquiring fluoroscopic images using a monoplanar or a biplanar C-arm system. However, the projective data provide only limited information...
7.
Deep learning-based coronary artery motion estimation and compensation for short-scan cardiac CT
Maier J, Lebedev S, Erath J, Eulig E, Sawall S, Fournie E, et al.
Med Phys . 2021 May; 48(7):3559-3571. PMID: 33959983
Purpose: During a typical cardiac short scan, the heart can move several millimeters. As a result, the corresponding CT reconstructions may be corrupted by motion artifacts. Especially the assessment of...
8.
Real-time scatter estimation for medical CT using the deep scatter estimation: Method and robustness analysis with respect to different anatomies, dose levels, tube voltages, and data truncation
Maier J, Eulig E, Voth T, Knaup M, Kuntz J, Sawall S, et al.
Med Phys . 2018 Nov; 46(1):238-249. PMID: 30390295
Purpose: X-ray scattering leads to CT images with a reduced contrast, inaccurate CT values as well as streak and cupping artifacts. Therefore, scatter correction is crucial to maintain the diagnostic...