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Innovative Training Network towards raising and supporting the next generation of creative and entrepreneurial cross-speciality imaging experts

Modern medicine seeks to personalize diagnosis and treatment to the needs of individual patients. Personalized medicine approaches use non-invasive imaging to detect and characterize disease. While anatomical and molecular imaging systems have become a commodity, these systems have also grown complex. Complexity is further increased by the introduction of combined (hybrid) imaging systems, such as PET/CT, SPECT/CT or PET/MR. Within the context of hybrid imaging, these systems provide multi-parametric image information that has proven promising in rendering diagnoses and subsequent therapeutic management of patients more effective. The growing complexity of diagnostic and therapeutic regimens in light of the drive to integrate multiple layers of biomarker information no longer supports the operation of hybrid imaging modalities by singular specialists, but instead requires a new generation of open-minded, technology-fluent and applications-oriented experts.

HYBRID will help educate future imaging experts in adopting and disseminating such a cross-specialty approach. Our idea for this innovative training network is built on our personal experience and understanding of the importance of quantitative multi-parametric biomarkers in the years to come. We propose a consortium of world-leading imaging experts and partners that present with extended experience in advanced biomarker utilization, from the development of multi-parametric imaging methods and the design and construction of hybrid imaging systems to sourcing non-image based biomarker information. We are supported by vendors of diagnostic hardware and software systems who engage in this multi-disciplinary and cross-specialty effort, as well as by scientific associations and non-governmental entities. Thus, HYBRID provides an exceptional platform to young, ambitious and talented researchers who like to engage in supporting the concept of personalized medicine through multi-parametric data.

Datum:1 okt 2017 →  30 sep 2021
Disciplines:Medische beeldvorming en therapie