A game changer for biomedical imaging!

About CONcISE research

The ambition of the CONcISE research network is to overcome traditional limitations of the current biomedical instrumentation, which focus on the maximisation 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.
To solve this issue, the CONcISE network brings together complementary computational and experimental expertise in the field of biomedical imaging, and follows a two-pillar approach:

Information Bandwidth

The acquisition and detection strategies will be based on the “information bandwidth” instead of the “Fourier bandwidth” to enable compression and sparse sampling without information loss. Thanks to spatial light modulators, the CONcISE network will encode images in a variety of basis – Wavelet, Fourier, Hadamard, etc. – allowing compression at the measurement stage using a single-pixel detector. Such a device enables the implementation of detection frameworks like “compressed sensing”, “basis scan”, “adaptive basis scan”, “matrix completion” which, making use of the sparsity, can reach lossless compression rates of above 90%. A shrunk spatial scene into a single detector, opens the possibility of exploring other dimensions such as spectrum and time with performances not achievable using a parallel detector.

Adaptivity, Data-Driven

The acquisition and detection will be piloted by on-line analysis/decision in a data-driven, adaptive way, until the required information is eventually reached. Online analysis and decision making are essential to guide the choice of sampling and detection strategies. To exemplify, adaptive strategies can act by dynamically focusing to best capture some features of the sample, such as the details of a specific area of interest in the field-of-view, after identification with a proper data-driven algorithm. This can be achieved, for example, by a combination of deformable optics and spatial light modulators. Online analysis is feasible nowadays thanks to the great advances in computational power promoted by massive-parallel computation strategies – GPU, cluster, cloud computing, etc. – that in turn have boosted the implementation of advanced image science and Artificial Intelligence (AI) reconstructions algorithms.

CONcISE expected outcomes

Based on the approach, Doctoral Candidates (DCs) will work on three main groups to develop three novel systems to address relevant paradigmatic problems related to biomedical imaging


will be able to simultaneously map 3D absorption and scattering (Diffuse Optical Tomography) at different wavelengths of thick biological tissues. Pulsed light together with time-resolved detection on the ps scale will be implemented to separate the absorption from the scattering contribution and to gain depth sensitivity. The development of such a system paves the way to a new generation of tissue imaging systems, e.g. optical mammography and brain functional imaging.


a more clinically-oriented system, will be able to perform multispectral fluorescence imaging, absorption and scattering using endoscopes and structured light illumination. The system will enable quantitative fluorescence endoscopy imaging, making multidimensional fluorescence measurements repeatable and interpretable in real-time through an endoscope. The system aims to provide the physician with a fast-imaging tool to be used during guided-surgery with exogenous contrast agents.


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.