By Alessandro Torricelli – Politecnico di Milano – Dipartimento di Fisica, Milan (Italy)

Can you imagine fNIRS in a smartphone? With existing steady state or continuous wave (CW) fNIRS technologies this could be just science fiction since miniaturization of a setup is not enough. If we want NIR light to reach brain cortex, at an average depth of 15 mm from the scalp, we would need in fact to place light source and photodetectors at a relative distance of at least 35 mm. This is ruled by the Physics of photon diffusion as presented in Martelli et al. 2016.

Actually, the size of existing smartphone is becoming bigger and bigger, and we can foresee sooner or later a smartphone equipped with a CW fNIRS sensor. However, to get rid of unwanted confounding signals from the scalp, CW fNIRS sensors working at short and long distance are essential, therefore increasing the complexity of the apparatus. Of course, flexible display smartphones could even help in fitting head curvature, but indeed this might be too complicated or not viable.

Starting more than 10 years ago in Milan we have been pursuing what in the beginning was just a crazy idea: fNIRS at null source detector distance (Torricelli et al. 2005). The concept is simple: to use short (picosecond) pulses of light to explore tissue in depth by exploiting a kind of echo effect. The longer photon travel in the tissue, the deeper they can go. If we are able to measure the time of flight of photons (i.e. the time they spend in the tissue before reaching the detector) with nanosecond (10^-9 s) resolution, we can discriminate signals coming from superficial (extra cerebral) layer (i.e. scalp) from deep cortical signals. In time domain (TD) fNIRS this can be achieved also at null source detector separation, paving the way to very small and miniaturized fNIRS systems. The first demonstration of this concept at laboratory level dates back to 2009 and it is based on the use of time-gated SPAD detectors (Pifferi et al. 2008). More sophisticated lab setup can be designed and used to study the Physics of null distance TD fNIRS (Di Sieno et al. 2019). Improvement in light source (i.e. using VCSEL) and timing and acquisition electronics led to the first steps toward miniaturization of the null distance TD fNIRS, still with SPAD detectors (Dalla Mora et al. 2015a).

Indeed, the drawback of SPAD is the narrow detection area (<0.01 mm²) that prevents collecting a reasonable signal-to-noise ratio (SNR). To overcome this problem, the solution we have been exploring in Milan is the use of array of SPAD or SiPM (silicon photomultipliers). Large area (i.e. 1 mm²) SiPM can be efficiently used for TD fNIRS applications (Dalla Mora et al. 2015b). The implementation of the null distance approach requires fast gating of large area detectors, dedicated microelectronics for high throughput photon counting and timing. In the ongoing H2020 project SOLUS – “Smart optical and ultrasound diagnostics of breast cancer” (www.solus-project.eu), funded by the European Commission, we are developing such technologies, actually for application to breast imaging, but exploitation to brain and fNIRS can be immediate, as the SOLUS probe relies on stand-alone units capable to perform multi-wavelength TD fNIRS measurements even at null distance.

The steps towards a TD fNIRS smartphone comprises also the development of compact TD fNIRS system (Buttafava et al. 2017) to break the barrier of bulky TD fNIRS system (Torricelli et al. 2014) while maintaining top level performances. We have recently presented at the ECBO conference the first TD fNIRS signals from the motor cortex during cycling in human (Lacerenza et al. 2019).

Waiting for the TD fNIRS smartphone, that might take a while, classical research on TD fNIRS and related clinical applications (Giacalone et al. 2019, Re et al. 2019) is going on in Milan, also encompassing multimodality approaches like integrating TD fNIRS with diffuse correlation spectroscopy (DCS), as recently done in the in BabyLux project in neonatology (Giovannella et al. 2019a, De Carli et al. 2019), and with DCS and ultrasonography for thyroid screening (luca-project.eu).

In collaboration with the Medical Optics group at ICFO leaded by Turgut Durduran, we have recently started chasing “coherent” photons. The recent proposal of TD DCS in fact further exploits the capability of the time domain approach to extract depth information and – in combination with TD fNIRS – leads to a more quantitative assessment of hemodynamics. Similar technologies can be adopted in both modalities, exploiting recent advancements in photonics components. (Pagliazzi et al. 2017, Pagliazzi et al. 2018).

During the last decade playing with fNIRS and Biomedical Optics tools, we have become aware that a concerted action on performance assessment (Wabnitz et al. 2014a, Wabnitz et al. 2014b) and standardization (SfNIRS Newsletter June 2018/) is needed to ground fNIRS on solid basis for comparison of studies and reliability of results, similarly to other clinical modalities now well established in the clinical ward. Open Data as well as machine learning/artificial intelligence will mostly benefit from standardization, starting from the interoperability of data format (see for instance the SfNIRS data format (SfNIRS Newsletter March 2019) up to traceability of measurements. On this line, the BITMAP project (www.bitmap-itn.eu) is running an unprecedented exercise which will lead to the comparison of over 28 instruments (fNIRS, DCS, TD DCS) all over Europe on 3 common protocols. If your next smartphone is equipped with a fNIRS sensor, very likely it will be a TD fNIRS one. Come and visit us in Milan if you want to see your very next future tool sprouting from lab into everyday life.

References & Links

• Martelli F et al., Scientific Reports 6:27057, (2016) https://doi.org/10.1038/srep27057
• Torricelli A et al., Phys. Rev. Lett. 95, 078101, 2005) https://doi.org/10.1103/PhysRevLett.95.078101
• Pifferi A et al., Phys. Rev. Lett. 100, 138101 (2008) https://doi.org/10.1103/PhysRevLett.100.138101
• Di Sieno L et al., Applied Science 9(11), 2366 (2019) https://doi.org/10.3390/app9112366
• Dalla Mora A et al. Biomed Opt Express 6(5), 1749-1760 (2015a) https://doi.org/10.1364/BOE.6.001749
• Dalla Mora A et al., Opt Express 23(11), 13937-13946 (2015b) https://doi.org/10.1364/OE.23.013937
• http://www.solus-project.eu
• Buttafava M. et al., IEEE Photonics Journal 9(1), 1-14 (2017) https://doi.org//10.1109/JPHOT.2016.2632061
• Torricelli A et al., Neuroimage 85, 28 (2104) https://doi.org/10.1016/j.neuroimage.2013.05.106
• Lacerenza M et al., ECBO 2019 Paper 11074-3 https://spie.org/EBO/conferencedetails/diffuse-optical-imaging
• Giacalone G et al., Neurophotonics 6(1), 015003 (2019). https://doi.org/10.1117/1.NPh.6.1.015003
• Re R et al., ECBO 2019 Paper Paper 11074-54 https://spie.org/EBO/conferencedetails/diffuse-optical-imaging
• Giovannella M et al., Neurophotonics, 6(2), 025007 (2019a) https://doi.org/10.1117/1.NPh.6.2.025007
• De Carli A et al., Archives of Disease in Childhood – Fetal and Neonatal Edition (2019). https://doi.org/10.1136/archdischild-2018-316400
• http://luca-project.eu
• Pagliazzi M et al., Biomed Opt Express 8(11), 5311-5325 (2017) https://doi.org/10.1364/BOE.8.005311
• Pagliazzi M et al., Optics Letters 43(11), 2450-2453 (2018) https://doi.org/10.1364/OL.43.002450
• Pifferi et al., Appl Opt 44(11), 2104-2114 (2005). https://doi.org/10.1364/AO.44.002104
• Wabnitz H et al., J Biomed Opt 19(8): 19(8):086010 (2014a) https://doi.org/10.1117/1.JBO.19.8.086010
• Wabnitz H et al., J Biomed Opt 19(8):086012 (2014b) https://doi.org/10.1117/1.JBO.19.8.086012
• https://fnirs.org/2018/06/sfnirs-newsletter-june-2018/
• https://fnirs.org/2019/03/shared-near-infrared-snirf-file-format-advance/
• http://www.bitmap-itn.eu