Medical Imaging is a manifold domain ranging from the use of ionizing radiation (such as X-rays) to mechanical waves (as in Ultrasound) for medical diagnosis and health research purposes. The development of new diagnostic tools (more accurate and reproducible) for improved clinical practice demands the translation of multidisciplinary knowledge from diverse fields into clinical applications. In the last years, IBEB has become a national reference on research for improving the accuracy of medical imaging methods, such as in the MRI study of the brain, and developing new imaging devices such as the Clear-PEM system, among other examples.
The scientific expertise of IBEB relies on people with Physics and Biomedical Engineering and Technology backgrounds and is complemented by a strong connection with medical doctors who use imaging as a tool. These collaborations are key to identify new state-of-the-art questions aimed at improving the quality of diagnostic imaging techniques. In addition, these synergies can contribute to: i) increase the success of therapies relying on imaging data and ii) trigger ideas for creating new devices and acquisition strategies.
This thematic line of scientific research is significantly enhanced by strategic partnerships allowing: a. Direct access to reference R&D Centers that feed IBEB with state-of-the-art data (PET-MRI, MRI, PET-CT) – e.g. the University College London, the Julich Research Center, King’s College London or Champalimaud Foundation) and use IBEB’s image processing and analysis skills. b. Direct access to specialized medical doctors and to medical data (CT, MRI, PET) – e.g. Hospital da Luz, Hospital de Santa Maria, Champalimaud Foundation. c. Cooperation with local and international industry (e.g. Siemens Medical Solutions, PETSys, Phillips, Agilent technologies, MITOS Medical Systems, Micrima, Cubresa).
Our main scientific goals for the near future are:
- Constructing clinically useful image-based models of the aggressiveness of low-grade brain gliomas using PET-MRI simultaneous data.
- Characterizing the effects of breast chemotherapy in the brain.
- Constructing fully operational image reconstruction and quantification algorithms for Si-PM PET systems.
- Improving the diagnostic accuracy of X-ray tomosynthesis of the breast using statistically based image reconstruction algorithms, CAD and augmented reality technology.
- Quantifying functional and mechanical characteristics of breast cancer using the Clear-PEM Sonic prototype.
- Developing automatic methodology for discriminating between benign and malignant breast tumors in a simulation scenario for a UWB Radar system. Assessing and diagnosing lymph nodes in the axilla region.
- Constructing and testing of a UWB radar adaptable to a PEM system for breast cancer diagnosis.
- Studying novel imaging biomarkers for breast cancer using MRI techniques and imaging genetics. Optimization and application of Diffusion Kurtosis Imaging for lesion subtype discrimination. Development of machine-learning algorithms for lesion classification based on the use of imaging as a phenotype: correlation between -omic (genomic, proteomic, etc) and histological information with MRI data.
- Quantifying water diffusion in the human brain employing Magnetic Resonance Fingerprinting.
- Constructing a one-end prototype for Magnetic Particle Imaging to image humans.
- Developing new tools for the optical detection of sentinel lymph nodes.
- Developing an fNIR-based imaging system to characterize in vivo breast cancer tissue.
Ultimately, we aim to contribute to more accurate, reliable, earlier and less invasive diagnosis or evaluation of therapy response.