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Computational Imaging as a training network for smart biomedical devices (CONcISE)

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https://concise-project.eu/

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Please contact with Drs. Jesús Lancis or Enrique Tajahuerce

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Computational Imaging as a training network for smart biomedical devices (CONcISE)

Funded within the framework of the Marie Skłodowska-Curie Actions (MSCA) of the European Union (EU), and coordinated by Consiglio Nazionale delle Ricerche, CONcISE is a new doctoral network set to bring to the market more data-efficient and quality-oriented techniques for biomedical optical imaging in biological tissues. The project follows an interdisciplinary approach, bringing together experts from academia and industry to develop novel, unconventional, and multi-dimensional computational imaging devices for biological tissues, where the hardware and the software are completely integrated and developed together.

research line computational imaging

The project aims to overcome traditional limitations of the current biomedical instrumentation, which focus on the maximization of the data acquired. Current devices gather data regardless of its quality, unavoidably leading to a large amount of data to manage, transfer, and analyse and thus creating data bottlenecks and gaps in measurements.

 

Concise Project

The part of the project coordinated by our research group ate University Jaume I, SMART-2PM, aims to perform high-resolution imaging of biological samples with wide-field 2-photon microscopy (also known as “non-linear microscopy”) based on light structured illumination and integrated detection. The system will combine new fs-light sources in the mid-IR, sophisticated adaptive optics systems, temporal focusing, single-pixel imaging strategies, and computational imaging approaches based on machine learning for adaptive detection. We will fix the fundamental trade-off between penetration depth, sensitivity, and imaging speed of current nonlinear microscopes for imaging biological tissues by applying, simultaneously, adaptive optics and computational imaging strategies.

 

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