Please select the desired project time frame:

• July 2026
• January 2026
• July 2025
• January 2025
• July 2024
• January 2024
• July 2023
• January 2023
• July 2022
• January 2022
• July 2021
• January 2021
• July 2020
• January 2020
• July 2019
• January 2019
• July 2018
• January 2018
• July 2017
• January 2017
• July 2016
• January 2016
• July 2015
• January 2015
• July 2014
• January 2014
• July 2013
• January 2013
• July 2012
• January 2012
• July 2011
• January 2011
• July 2010
• January 2010
• July 2009
• January 2009
• July 2008
• January 2008
• July 2007
• January 2007
• July 2006
• January 2006
• July 2005
• January 2005
• July 2004
• January 2004
• July 2003
• January 2003
• July 2002
• January 2002
• July 2001
• January 2001

Start of funding 01.07.2023

Integration of Known Operators into a Deep Neural Network for Enhanced HR-pQCT Bone Imaging

---

This interdisciplinary research project is a collaboration between Prof. Dr. Maier's lab at Friedrich-Alexander University Erlangen-Nuremberg and Prof. Dr. Burghardt's lab at the University of California, San Francisco. The joint work focuses on the application and enhancement of high-resolution peripheral quantitative computed tomography (HR-pQCT) - a clinically relevant technology for imaging bone structures and assessing diseases such as osteoporosis. Prof. Dr. Maier's lab has developed the PYRO-NN framework that embeds known operators into Tensorflow, enabling end-to-end trainable neural networks for CT image reconstruction. In collaboration with Prof. Dr. Burghardt, this framework is being tailored and applied to HR-pQCT. A critical part of this process is the integration of the ATRACT method - a reconstruction technique for small volumes with a limited field of measurement - into PYRO- NN. The aim is to optimize image reconstruction and interpretation. The expected outcome of this research is fundamental improvements in the spatial resolution of HR-pQCT images and increased accuracy in clinical interpretation and diagnosis.