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Basic and Translational Research and Imaging Methodology Development (BRIDGE)

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​​​​​​​​​​​​​​BRIDGE is a new program within the KOLFF institute, which is focussed on the development and translational application of new imaging methodologies in the UMCG with collaboration of partners at the RUG. BRIDGE will unite researchers and clinicians interested and involved in the development, translation, application and clinical implementation of improvements in imaging equipment, software and other technologies.​​​​​​

Programme Leaders  

Dr. Wiktor Szymanski (program leader), organic chemist
Dr. Romana Schirhagl (program co-leader), chemist
Dr. Andor Glaudemans (program co-leader), nuclear medicine physician

Mission  

The aim of BRIDGE is to facilitate and support imaging-related research activities, from the stage of early development all the way into clinical practice. BRIDGE will enable the sharing of knowledge and expertise (by educational activities, meetings, and dedicated IT portal that brings together medical doctors looking for technological solutions and basic scientists being able to develop those and the other way around), technology, software analysis tools and Big Data.
BRIDGE program builds on the extensive knowledge on healthcare and medical imaging (UMCG) and basic sciences (UMCG with connections to RUG). Harnessing these synergistic expertise towards clinically applied discovery seems not only a viable option, but a necessary one. BRIDGE research program is ideally positioned to coordinate efforts through idea interchange, inspiration, evaluation and application.

Description of the Programme  

BRIDGE focuses on three main themes. The themes constitute a matrix in which each element is represented by the respective methodological, basic scientists and clinical experts. Hence, the task group of each research pillar may include for example clinical imaging experts, IT experts, imaging methodology experts, basic scientists and clinicians.

New imaging biomarker development and validation (to see)

This theme covers research activities related to the development of new optical imaging agents, radiotracers, MRI contrast agents and new MRI sequences and CT techniques. For each new imaging biomarker a certain roadmap, consisting of steps from discovery to (clinical) implementation, is followed. Important aspects of the translation and implementation of new imaging biomarkers are to demonstrate their proof of mechanism, to assess their performance and robustness under various clinical conditions, to determine the optimal way of data collection and to assess its clinical value. The aim of this theme is to facilitate and support those research activities that will translate new imaging biomarkers from bench (or lab) to bed, including preclinical evaluations, first-in-human studies, repeatability and reproducibility measurements, optimization of imaging procedures and their use as diagnostic aid or as prognostic and/or predictive factors.

Quantification, standardisation and informatics (to understand)

There is an increasing interest and need for quantitative imaging methodology, analysis methods and imaging procedures that can extract (quantitatively robust) data from images. Quantification and standardisation are key issues for the (clinical) use of imaging technology. In post-processing, these two concepts are equally important. As measuring is perceived as knowing, being able to trust and understand what is measured is paramount. Methods of measurement should be standardized, validated, calibrated and tested for reproducibility and result robustness.

New and hybrid imaging technologies have the ability to generate a wealth of data. Visual inspection and conventional analysis methods and standard statistical approaches alone do not completely cover the load in analysing the generated highly complex, highly voluminous (imaging) datasets. Machine and deep learning algorithms are trained to recognize specific patterns or diagnostic information from a large set of (otherwise apparently unstructured) data. In this BRIDGE theme, medical informatics will be one of the key topics, covering besides deep learning also other medical informatics activities such as 3D printing, advanced 3D visualisations and image storage and analysis infrastructures allowing linking of images with non-imaging data. Moreover, use of advance medical informatics systems to guide therapies are considered here as well.

Image guided therapy and theranostics (to treat)

Imaging has become an integral part of the work-up in patient care and therapy, ranging from diagnosis, treatment prediction, selection and planning, response monitoring and the assessment of (healthy) tissue damage. Imaging may help in identifying patients that may more likely benefit from complex (drug, radio- or proton) treatments, thereby avoiding unnecessary/inappropriate treatments (precision medicine / personalized medicine). In particular for new therapy opportunities, such as proton therapy, careful planning and monitoring treatment as well as its efficacy is of crucial importance. Imaging can guide and optimize these therapies by accurate localisation of disease and distinguishing it from healthy tissue, by assessing their susceptibility to treatment induced damage, by non-invasively monitoring the efficacy of treatment (early on) and avoiding unintentional healthy tissue damage. Research on the use of imaging in (drug, radio- and proton) diagnosis, therapy decision making or (image guided) surgery will be one of the main topics addressed in the BRIDGE theme. Direct connections between diagnostics and therapy (theranostics) will also be explored, for example trough the development of photopharmacology.

Relevance to Healthy Ageing  

At the dawn of personalized medicine, medical imaging is becoming a key tool to diagnose, stratify and select patients for therapies. This is especially of interest for vulnerable population, including the elderly patients. In this case, not only the diagnosis, but also the decision of viable treatment becomes crucial. Here, the BRIDGE strategy may help to select the right patient for the right therapy, with the aim to stop the disease without unnecessary side effects, thereby leading to an increase in disease free survival and quality of life. Furthermore, BRIDGE may help in prevention of disease, by using Big Data to predict the onset of disease or by specific tracers to prevent the onset of disease, and by unravelling disease mechanism, all of invaluable importance for healthy ageing.