Mr. Dennis Hein Profile

Dennis Hein

at KTH Royal Institute of Technology

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Author

Publications (5)

Proceedings Article | 1 April 2024 Poster + Paper

Impact of deep-learning CT image denoising on the accuracy of radiomics parameter estimation

Pontus Pandurevic, Alex Back, Dennis Hein, Mats Persson

Proceedings Volume 12925, 129252C (2024) https://doi.org/10.1117/12.3006732

KEYWORDS: Radiomics, Tumors, Feature extraction, Computed tomography, Deep learning, Image processing, 3D printing

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Proceedings Article | 1 April 2024 Poster + Paper

Proton-stopping power ratio prediction using photon-counting computed tomography and deep learning

Karin Larsson, Dennis Hein, Ruihan Huang, Daniel Collin, Jonas Andersson, Mats Persson

Proceedings Volume 12925, 129252P (2024) https://doi.org/10.1117/12.3006363

KEYWORDS: Computed tomography, Deep learning, Optical computing, Radiotherapy, Proton therapy, Neural networks, Body composition, Virtual reality, Tumors, Tissues

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Proceedings Article | 7 April 2023 Poster + Paper

Model-based deep learning to achieve interpretable spectral CT denoising

Alma Eguizabal, Dennis Hein, Bruno Sandrini, Mats Persson

Proceedings Volume 12463, 124633S (2023) https://doi.org/10.1117/12.2654355

KEYWORDS: Deep learning, Model-based design, Denoising, Education and training, Matrices, Databases, X-ray computed tomography, Statistical analysis, Image restoration, Data modeling

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Proceedings Article | 7 April 2023 Poster + Paper

Spectral CT denoising using a conditional Wasserstein generative adversarial network

Dennis Hein, Mats Persson

Proceedings Volume 12463, 124633A (2023) https://doi.org/10.1117/12.2654186

KEYWORDS: Image processing, X-ray computed tomography, Image restoration, Denoising, Image quality, Medical image reconstruction, Deep learning

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Proceedings Article | 18 October 2022 Open Access

Deep learning ring artifact correction in photon-counting spectral CT with perceptual loss

Dennis Hein, Konstantinos Liappis, Alma Eguizabal, Mats Persson

Proceedings Volume 12304, 123042Q (2022) https://doi.org/10.1117/12.2647089

KEYWORDS: Sensors, X-ray computed tomography, Neural networks, Data modeling, Signal attenuation, Photons, Photodetectors, Image processing, Bone, Tissues

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