PhD student (d/f/m) Horizon Europe MSCA-Doctoral Network DM.255.24

Overview

The project for sought-after candidates “Multidimensional elastography using autonomous scanning probe microscopy” is carried out in the Elastography Group of the Radiology Clinic of the Charité - Universitätsmedizin Berlin under the direction of Prof. Ingolf Sack. The focus of the elastography laboratory is the imaging measurement of the viscoelastic properties of soft tissue for clinical diagnosis and biomedical research using acoustic waves in magnetic resonance elastography (MRE), ultrasound elastography and optical elastography across scales, types and entities. Activities include research and development in all major aspects of elastography technology and applications to body tissues in health and disease. The overarching goal is to translate the understanding of the critical role of physical tissue properties into clinical biomarkers, e.g. in cardiovascular disease and cancer. Further information can be found on the Elastography website (https://elastography.charite.de/en/).

In this project, the doctoral candidate will develop diagnostic biomarkers based on the stiffness properties of cancer as measured by in vivo MRE and ex vivo atomic force microscopy (AFM). Aim is to quantify the differences in tumor stiffness between in vivo and ex vivo samples and infer from difference values the solid mechanical stress in vivo. A liver tumor aggressiveness score should be developed that indicates pre-stressed niche properties associated with higher tumor aggressiveness and poor treatment response. Furthermore, chemotherapy-treated lesions will be investigated with in vivo MRE and multiparametric quantitative MRI (qMRI) prior to resection and ex vivo MRE, qMRI and AFM. The main objectives are to develop 1) multiscale profiles of tumor mechanical and biophysical properties, 2) multiscale mechanics-based tumor aggressiveness markers for translation into clinical oncology and staging of liver tumors and metastases and 3) a ground-truth for in vivo mechanical data for neural network training and benchmarking. The methods used in the project are MRE, qMRI and AFM.