Biomedical imaging and spectroscopy

Raman microscopy for biomedical applications

Team: Renzo Vanna (IFN-CNR), Marco Ventura (IFN-CNR), Victor Alcolea-Rodriguez (IFN-CNR),  Dario Polli (Politecnico di Milano), Giuseppe Antonacci (Specto Photonics), 

This research activity started in 2020 and it is coordinated by Dr. Renzo Vanna (Milan).  Raman spectroscopy and Raman imaging studies are carried out using a home-built setup equipped with a  laser centered at 660nm (Cobolt, 300 mW) entering into the back port of an inverted microscope (Olympus IX73) coupled with motorised stage (x, y) and focus (z). Raman photons enter into a Isoplane 160 spectrometer (Princeton Intruments) equipped with two gratings (600 and 1200 l/mm) and Raman signals are detected by a front illuminated CCD (Pixis 256).


Raman microscope


The main research activities are focused on the study of tissue, cells and biofluids from patients and animal models, without excluding specific characterisation of other type of samples (materials, food, microplastics). The main research topics are the study of bone diseases,  senescence processes and leukemia.

In 2022, thanks to the collaboration with Giuseppe Antonacci (CEO and co-founder of Specto Photonics) the Raman microscope has been coupled with a compact and innovative Brillouin spectrometer thus allowing the simultaneous co-registration of Raman and Brillouin data and images on the same sample, thus allowing to investigate both chemical and mechanical properties of biological (or non-biological) samples.


RamApp: In the context of our research we developed a new open-source and web-based tool for the processing of Raman maps, called “RamApp“.


CRIMSON” (H2020 ICT-36);
“TROPHY” (Horizon EIC Pathfinder);
CHARM” (Horizon EIC Transition)
“OPTIMA” (Italian Ministry of Research, PRIN call 2022)


Wide-field Raman and Hyperspectral Imaging

This research activity started in 2019 and is coordinated by Dr. Cristian Manzoni (Milan). It deals with the development of novel imaging devices for Hyperspectral Imaging. In this class of imaging, one collects, for each element (or pixel) of the image of a scene the complete spectral information, giving rise to a 3D datacube in which two dimensions are the spatial (x,y) coordinates, and the third dimension is frequency or wavelength.

Hyperspectral datacube, with (x,y,w) information

Our innovative hyperspectral imaging system is based on Fourier-transform spectroscopy and on the use of an ultrastable common-path birefringent interferometer (TWINS). By coupling the interferometer to an optical microscope, we can acquire wide-field hyperspectral images of various observables: transmission, reflection, fluorescence and Raman.  The exceptional delay stability and reproducibility  of the birefringent interferometer enables rapid acquisition of Raman maps (∼ 30 minutes for a 250 000 pixel image) with high spatial (<1 μm) and spectral (∼ 23/cm) resolution. The time-domain detection allows us to disentangle fluorescence and Raman signals, which can both be measured separately. The system enables the study of 2D materials, biological samples, nanoplastics, and more. The microscope is also configurable to provide images in the k-space, enabling the novel hyperspectral k-space imaging.


“TROPHY” (Horizon EIC Pathfinder)


Time-Domain Near Infrared Spectroscopy (TD-NIRS) in Diffuse Media

Team: Lorenzo Spinelli (IFN-CNR),  Alessandro Torricelli, Davide Contini, Rebecca Re (Politecnico di Milano)

Optical radiation in the visible and near-infrared spectral region (600-1100 mm, NIR) can be use to probe non-invasively biological tissues because:

  • biological tissues are relative transparent in this spectral range (named “therapeutic window”) allowing the optical radiation to penetrate for a few centimeters and to have access also to deep structures (such as muscle or cerebral cortex) operating from the outside in a completely non-invasive way;
  • the propagation of light in diffusive media has the characteristics of a chaotic process (named “photon migration”), mainly ruled by the absorption and diffusion phenomena, and can be described by suitable mathematical models that allows to retrieve the optical properties, i.e. the absorption and reduced scattering coefficient, of the probed tissues;
  • the absorption spectra of the chromophores present in biological tissues (such as oxygenated and deoxygenated hemoglobin, lipid, water, collagen, etc., in case of human tissues, or chlorophyll, carotenoids, anthocyanins, water, etc. in case of vegetables) exhibit different characteristics that a multispectral approach can exploit to determine their concentrations;
  • The scattering spectra are related to the microstructures of the probed tissue and give access in particular to the density and dimensions of the scattering centers.

Different approaches to NIRS are possible, i.e. continuous wave, frequency-domain, and time-domain. We adopted the time-domain approach, that has the advantage of increasing the spatial resolution in depth, and of a better decoupling of absorption and scattering phenomena, allowing an accurate estimation of tissue optical properties.

TD-NIRS measurement and analysis scheme: IRF, Instrument Response Function; DTOF, Distribution of Time-of-Fly; model, mathematical model describing photon migration.

In this framework, we are working to two main applications.

Monitoring of the hemodynamics and oxidative metabolism

Portable and compact instruments for TD-NIRS are developed in our laboratories with the aim of non-invasively monitoring the concentration of oxy- and deoxy hemoglobin in the tissues (skeletal muscle, brain cortex). These instruments have been successfully applied to both adults and children, during measurement protocols involving external stimuli, or as continuous monitor also at the bedside.

More recently TD-fNIRS instrumentation has been integrated with a new developed instrument based on the Diffusion Correlation Spectroscopy (DCS). This technique relies on the light intensity decorrelation determined by red blood cells movement in the tissue, allowing to have an estimation of the bool flow. The combination of the hemoglobin concentrations and blood flow allows to give an estimation of the metabolic rate of oxygen consumption.

Non-destructive assessment of fruit quality

A TD-NIRS instrument, working at 14 wavelengths in the spectral range 540-1064 nm, has been successfully used in various measurement campaigns, both directly in the field and in the laboratory. The main problems addressed have been: the post-harvest management of different types of fruit, such as apples, pears, peaches, nectarines, kiwis, plums and mangoes; the non-destructive detection of defects in fruit; the prediction of the quality of transformed products.


ESPERA: Economia circolare e sostenibilità della filiera della pera IGP del Mantovano, Regione Lombardia


Multidimensional Imaging of biological samples

Team: Andrea Farina (IFN-CNR),  Cosimo D’Andrea (Politecnico di Milano), Alberto Ghezzi (Politecnico di Milano)

This research activity aims at developing novel, unconventional techniques for multi-dimensional biomedical optical imaging to detect and map absorption, scattering, and fluorescence in biological tissues using visible and near-infrared light.

The possibility of detecting other dimensions like time and wavelength, together with space, dramatically improves the imaging and diagnostic power of biomedical devices.

Under a classical, conventional approach to imaging, more dimensions mean more and more data to acquire but…more and more data does not necessarily mean more information! This is why we aim to optimize the sensing strategy to acquire less data as possible without losing information.

Our research is based on  a Compuational imaging approach: the final image is not directly measured but obtained by hard computational processing where different techniques, such as single-pixel imaging, compressed sensing, data fusion,  and others, can be combined together. Hereafter is a short description.

Single-pixel imaging

Developed at the RICE University, single-pixel imaging allows imaging through a single-element detector (also called “bucket detector”) taking advantage of spatial light modulators, such as a Digital Micromirror Device (DMD). A series of spatial patterns are overlapped to the target image and the resulting light is focused on a single-element detector. After a reconstruction phase that can range from a simple matrix inversion to a more complex constrained minimization, the target image is eventually reconstructed. The fact that we can compress the spatial dimension into a single-element detector opens the way to exploring other dimensions (such as lifetime, spectrum, polarization, and depth) or other spectral ranges where cameras are not available.

Compressed sensing

This computational framework allows the recovery of an image which has a sparse representation by a number of measurements that is much less than the number of pixels. This can be done by a constrained minimization enforcing sparsity in different domains (e.g. pixel, gradient, Wavelet, etc.).

The single-pixel camera is the enabling technology of compressed sensing: in fact, we can directly compress during the acquisition phase by acquiring a small set of measurements.

Data fusion

Developed in collaboration with the Photonics Research Group (GROC) @ UJI (University Jaume I) this technique allows us to fuse together datasets obtained with different detectors. In particular, we have recently applied and published this technique to recover a high-resolution 4D hypercube (space, time on the ps, spectrum) by fusing a low-resolution multi-dimensional microscopy dataset derived bt a low-resolution single-pixel camera and a high-resolution image obtained by a CMOS camera.

Experimental Setups

We mainly work on two setups: one dedicated to imaging and tomography in diffusive media on the macro scale, and another working on the microscopy scale.

Time-resolved Diffuse Optical Tomography (TR-DOT)

The system allows the 3D mapping of absorption and scattering in diffusive media. Making use of the time-resolved technique it is possible to discriminate absorption and scattering. The system embeds two DMDs for both structured illumination and single-pixel detection.


Multi-spectral Fluorescence Lifetime Microscopy (λ-FLIM)

The system allows for the multidimensional mapping of the fluorescence emission from biological samples on the microscopy scale. The system embeds a DMD for structured illumination and different output paths for exploring both multi-spectral and high-throughput time-resolved single-pixel imaging in collaboration with the Department of Electronics @ Politecnico di Milano.


CONcISE” (HE – MSCA – DN – 2021)


  • Ghezzi, A., Lenz, A. J. M., Soldevila, F., Tajahuerce, E., Vurro, V., Bassi, A., Valentini, G., Farina, A., & D’Andrea, C. (2023). Computational based time-resolved multispectral fluorescence microscopy. APL Photonics, 8(4), 046110 1-7.
  • Farina, S., Labanca, I., Acconcia, G., Ghezzi, A., Farina, A., D’Andrea, C., & Rech, I. (2022). Above pile-up fluorescence microscopy with a 32 Mc/s single-channel time-resolved SPAD system. Optics Letters, 47(1), 82.
  • Calisesi, G., Ghezzi, A., Ancora, D., D’Andrea, C., Valentini, G., Farina, A., & Bassi, A. (2022). Compressed sensing in fluorescence microscopy. Progress in Biophysics and Molecular Biology, 168.
  • Soldevila, F., Lenz, A. J. M., Ghezzi, A., Farina, A., D’Andrea, C., & Tajahuerce, E. (2021). Giga-voxel multidimensional fluorescence imaging combining single-pixel detection and data fusion. Optics Letters, 46(17), 4312–4315.
  • Ghezzi, A., Farina, A., Bassi, A., Valentini, G., Labanca, I., Acconcia, G., Rech, I., & D’Andrea, C. (2021). Multispectral compressive fluorescence lifetime imaging microscopy with a SPAD array detector. Optics Letters, 46(6), 1353.
  • Farina, A., Betcke, M., Bassi, A., Valentini, G., Arridge, S., & D’Andrea, C. (2019). An adaptive scheme for diffuse-optical tomography based on combined structured-light illumination and single-pixel camera detection. In H. Dehghani & H. Wabnitz (Eds.), Diffuse Optical Spectroscopy and Imaging VII (Vol. 11074, p. 89). SPIE.
  • Farina, A., Candeo, A., Dalla Mora, A., Bassi, A., Lussana, R., Villa, F., Valentini, G., Arridge, S., & D’Andrea, C. (2019). Novel time-resolved camera based on compressed sensing. Optics Express, 27(22), 31889.
  • Farina, A., Betcke, M., di Sieno, L., Bassi, A., Ducros, N., Pifferi, A., Valentini, G., Arridge, S., & D’Andrea, C. (2017). Multiple-view diffuse optical tomography system based on time-domain compressive measurements. Optics Letters, 42(14), 2822.



People involved:

Cristian Manzoni
Lorenzo Spinelli
Andrea Farina
Renzo Vanna


Research units