Fabio studied Physics and Biophysics at the Technical University of Munich (Germany) from 2009 until 2015. He wrote his Master thesis at the Biomedical Imaging Physics group of Prof. Dr. Franz Pfeiffer on algorithmic optimizations for a grating-based X-ray phase-contrast imaging setup for microCT applications.
He remained in the group for his Ph.D. thesis, where he co-developed a grating-based X-ray imaging setup optimized for imaging the lungs of large animals with the dark-field modality. This setup was used to produce the world-first in-vivo dark-field radiographs of a pig thorax, and thus demonstrated the viability of dark-field radiography as a clinical imaging tool.
Fabio joined the S-BaXIT project in February 2021. His main interest is in the development and improvement of image retrieval algorithms for X-ray phase contrast, in particular for speckle-based phase contrast and near-field ptychography.
Email: fabiodomenico [dot] demarco [at] units [dot] it
ORCID: https://orcid.org/0000-0002-3561-7305
ResearchGate: https://www.researchgate.net/profile/Fabio-De-Marco
LinkedIn: https://www.linkedin.com/in/f-de-marco/
Phone: (+39) 040-375-8994
IMXP 01.2019 Garmisch-Partenkirchen, DE | Imaging features of dark-field human chest X-rays | Oral Presentation |
IMXP 01.2018 Garmisch-Partenkirchen, DE | X-ray dark-field radiographies of in vivo pig and ex vivo human | Oral Presentation |
XNPIG 09.2017 Zürich, Switzerland | Systematic analysis of in-vivo dark-field signal in pig lungs | Poster |
SPIE Medical Imaging 02.2017 Orlando, FL, USA | Improving image quality in laboratory x-ray phase-contrast imaging | Oral Presentation |
DPG-Frühjahrstagung 05.2015 Wuppertal, DE | Improved spatial resolution of X-ray phase-contrast computed tomography via iterative image deconvolution | Oral Presentation |
IMXP 01.2015 Garmisch-Partenkirchen, DE | Improved spatial resolution in X-ray phase-contrast CT via iterative image deconvolution | Poster |
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This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme grant agreement n. 866026
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