Mona-Elisabeth Revheim's research group: Functional and Molecular Imaging

Mona-Elisabeth Revheim
Group leader

Background

Expanded knowledge of the pathogenesis behind diseases has led to the development of novel treatment strategies. Functional and molecular imaging modalities are promising for non-invasive diagnosis and evaluation of treatment response at an earlier time point than morphological imaging. The goals are to individualize diagnostic work-up and treatment, in order to optimize treatment outcome and minimize toxicity. The group focuses on:

  • clinical- and translational studies
  • how to optimize image quality and to improve accuracy of disease detection
  • in vivo tissue characterization and depiction of disease process
  • treatment response assessment
  • multimodality imaging
  • radionuclide therapies
  • theranostic medicine
  • radiomics and artificial intelligence

Projects

  • Multimodal imaging in the characterization of tumors to assess tumor aggressiveness, diagnostic work-up and treatment response evaluation in prostate cancer, cervical cancer, breast cancer, sarcomas, spinal metastases, neuroendocrine tumors, GISTs, colorectal cancers, liver malignancies, multiple myeloma and lymphoma
  • Characterization of vascular inflammation inclusive vulnerability in atherosclerosis, treatment selection and treatment response evaluation using PET, MRI and US
  • Detection of inflammatory disease and treatment response evaluations using different imaging modalities and PET tracers
  • Characterize pituritary microadenomas, remnant adenomas following surgery and hypophysitis by the use of different MRI and PET techniques
  • Radio-immunotherapy for treatment of relapsed CD37+ non-Hodgkin lymphoma, novel α-emitting radionuclide therapies and Selective Internal Radiation Therapy (SIRT) with SIR-Spheres in liver malignancies
  • Amyloid imaging by 18F-Flutemetamol PET/CT  –clinical value and quantitative reference levels
  • Ultrasound-guided percutaneous ethanol injection treatment in patients with metastatic lymph nodes from thyroid carcinoma
  • Development of new biomarkers using radiomics and artificial intelligence (AI) for the characterization of tumor heterogeneity and disease patterns