You will work on an ERC Starting Grant funded project which aims to enable the detection and characterisation of liver micrometastases development in cancer, based on integrating advanced MRI techniques with computational histology.

Your project specifically is focused on developing and applying a histology-informed simulation framework for multi parametric MRI to create innovative biomarkers for liver micrometastases. By using synthetic histology meshes generated based on deep learning approaches, you will build MR signal dictionaries, which in turn can be used to estimate tissue cellular properties from in vivo MRI data, in a non-invasive way.

Applying this approach to preclinical and clinical MRI data, and comparing the results with histological ground truths, you will provide histologically-meaningful descriptors that outperform traditional models, advancing precision oncology and early metastasis detection.

You will work on an ERC Starting Grant funded project which aims to enable the detection and characterisation of liver micrometastases development in cancer, based on integrating advanced MRI techniques with computational histology.

Your project specifically is focused on developing and applying a histology-informed simulation framework for multi parametric MRI to create innovative biomarkers for liver micrometastases. By using synthetic histology meshes generated based on deep learning approaches, you will build MR signal dictionaries, which in turn can be used to estimate tissue cellular properties from in vivo MRI data, in a non-invasive way.

Applying this approach to preclinical and clinical MRI data, and comparing the results with histological ground truths, you will provide histologically-meaningful descriptors that outperform traditional models, advancing precision oncology and early metastasis detection.