Multiscalar Machine Learning for the Integration of Quantitative in vivo MRI with ex vivo Analysis to assess Pathological Changes of the Extracellular Matrix (A07*)

• Subject Area: Radiology
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 372486779

Currently, quantitative in vivo MRI and ex vivo data are analyzed separately using individual analysis techniques. For correlation analysis, single parameters are extracted and related to each other. Deriving and integrating all information from in vivo imaging and ex vivo analysis has the potential to improve the identification of novel predictive biomarkers. This project aims to integrate advanced multiscalar imaging and machine learning (ML) techniques, enabling the identification of the most promising quantitative imaging biomarkers and ex vivo parameters for the prediction of disease-progression and response-to-therapy in the disease models investigated within CRC 1340.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1340: In vivo Visualization of Extracellular Matrix Pathology „Matrix in Vision“
• Applicant Institution: shared FU Berlin and HU Berlin through:; Charité - Universitätsmedizin Berlin
• Project Heads: Professor Dr. Marcus R. Makowski; Professor Daniel Rückert, Ph.D.