Personal page Fabio De Marco

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Fabio De Marco

Postdoctoral researcher / Assegnista di ricerca

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.

Contact
Publications
Presentations
Contact

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

Publications

  1. Taphorn, K. et al. Direct Differentiation of Pathological Changes in the Human Lung Parenchyma With Grating-Based Spectral X-ray Dark-Field Radiography. IEEE Trans. Med. Imaging 40, 1568–1578 (2021).
  2. Andrejewski, J. et al. Whole-body x-ray dark-field radiography of a human cadaver. Eur. Radiol. Exp. 5, 6 (2021).
  3. Taphorn, K. et al. Grating-based spectral X-ray dark-field imaging for correlation with structural size measures. Sci. Rep. 10, 13195 (2020).
  4. Sauter, A. P. et al. Optimization of tube voltage in X-ray dark-field chest radiography. Sci. Rep. 9, 8699 (2019).
  5. Fingerle, A. A. et al. Imaging features in post-mortem x-ray dark-field chest radiographs and correlation with conventional x-ray and CT. Eur. Radiol. Exp. 3, 25 (2019).
  6. De Marco, F. et al. Contrast-to-noise ratios and thickness-normalized, ventilation-dependent signal levels in dark-field and conventional in vivo thorax radiographs of two pigs. PLOS ONE 14, e0217858 (2019).
  7. Willer, K. et al. X-ray dark-field imaging of the human lung—a feasibility study on a deceased body. PLOS ONE 13, e0204565 (2018).
  8. Hellbach, K. et al. Depiction of pneumothoraces in a large animal model using x-ray dark-field radiography. Sci. Rep. 8, 2602 (2018).
  9. De Marco, F. et al. Analysis and correction of bias induced by phase stepping jitter in grating-based X-ray phase-contrast imaging. Opt. Express 26, 12707–12722 (2018).
  10. Schröter, T. J. et al. Large field-of-view tiled grating structures for X-ray phase-contrast imaging. Rev. Sci. Instrum. 88, 015104 (2017).
  11. Gromann, L. et al. In-vivo X-ray Dark-Field Chest Radiography of a Pig. Sci. Rep. 7, 4807 (2017).
  12. De Marco, F. et al. Improving image quality in laboratory X-ray phase-contrast imaging. Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 1013214 (2017).
  13. Braunagel, M. et al. Qualitative and Quantitative Imaging Evaluation of Renal Cell Carcinoma Subtypes with Grating-based X-ray Phase-contrast CT. Sci Rep 7, 45400 (2017).

Presentations

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