31 January 2017 Diffuse optical characterization of collagen absorption from 500 to 1700 nm
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Reduction in scattering, high absorption, and spectral features of tissue constituents above 1000 nm could help in gaining higher spatial resolution, penetration depth, and specificity for in vivo studies, opening possibilities of near-infrared diffuse optics in tissue diagnosis. We present the characterization of collagen absorption over a broadband range (500 to 1700 nm) and compare it with spectra presented in the literature. Measurements were performed using a time-domain diffuse optical technique. The spectrum was extracted by carefully accounting for various spectral distortion effects, due to sample and system properties. The contribution of several tissue constituents (water, lipid, collagen, oxy, and deoxy-hemoglobin) to the absorption properties of a collagen-rich in vivo bone location, such as radius distal in the 500- to 1700-nm wavelength region, is also discussed, suggesting bone diagnostics as a potential area of interest.



In recent years, diffuse optical spectroscopy (DOS) is advancing as a promising tool for noninvasive quantification of tissue constituents in pathological diagnostics.12.3.4 Traditionally, in vivo diffuse optical measurements have been performed, at most, in the 600- to 1100-nm window.56.7 This is due to high hemoglobin absorption below 600 nm and dominant water absorption combined with lack of suitable detection technology above 1100 nm. Furthermore, studies are most often performed below 900 nm, where more conventional and cheaper detectors are available. However, recent investigations emphasize the advantage of high penetration depth and enhanced constituent specificity in the short wave infrared (SWIR) 1100- to 1700-nm range.89.10 Importantly, the lack of systematic characterization of tissue constituents in the SWIR range prevents its widespread use for in vivo applications.

The structural protein collagen is an abundant key tissue constituent in soft and hard tissues. Works by Taroni et al.11 and Provenzano et al.12 showed that collagen density in breast tissues correlates with breast density and accordingly with the risk of breast cancer. Few studies on the noninvasive characterization of collagen in bone tissues13,14 revealed its future prospects in osteology to complement existing technologies, such as dual-energy x-ray absorptiometry (DXA), for bone pathological diagnosis. Enhanced contrast of collagen absorption was reported using photoacoustics at SWIR wavelengths as compared to the near-infrared region.15,16 For collagen, the high absorption in the SWIR region9 can be exploited to investigate its dynamics of growth in studies related to tissue culture or engineering.17,18

To our knowledge, the first characterization of collagen absorption in the 600- to 1100-nm range was reported by Taroni et al.19 in 2009 then followed by Nachabé et al.8 However, little information is available on the absolute absorption spectrum of collagen in the SWIR, despite all the interesting features of collagen in that range and the related potential diagnostic applications mentioned above. High scattering, fluorescence in the visible range, and flake-like nature of commercially available collagen are of hindrance to its characterization by direct spectroscopic approaches (e.g., commercial spectrophotometer). Though a recent work has partially solved the problem of scattering using an integrating sphere over the 500- to 1600-nm range,8 in the absence of rigorous validation per se this approach does not guarantee the effective elimination of distortions caused by scattering. Moreover, fluorescence in the visible region cannot be eliminated in this way. Photoacoustics may be a viable option but it requires tunable and powerful pulsed lasers over the SWIR spectral range, as well as a proper approach to quantitation. In this scenario, time-domain diffuse optical techniques can be an effective solution to characterize the optical properties of collagen, since their natural disentanglement of absorption from reduced scattering enables the assessment of a distortion-free absorption spectrum.

Aim of the current work is to present the absorption spectrum of collagen over a broadband range (500 to 1700 nm). A hybrid time-resolved DOS system (TRS) was developed by combining a clinical TRS system that works in the 500- to 1350-nm range20 with a TRS system specifically designed to work in the SWIR region10 (1100 to 1700 nm), in order to effectively cover continuously the whole range from 500 to 1700 nm. The simulation of in vivo measurements is presented for an area of the human body where bone is close to the surface (radius distal), which is rich in collagen, to better appreciate in different spectral regions the contributions of collagen as compared to the other key tissue absorbers.


Materials and Methods


Time-Domain Diffuse Optical Spectrometer

Figure 1 depicts the schematic of the system. A broadband (450 to 1750 nm) supercontinuum fiber laser along with a Pellin Broca prism allows wavelength selective coupling of the 60-MHz laser pulses into a 50-μm core fiber. The maximum power ranges between 0.5 and 6 mW, depending on the selected wavelength. Due to prism dispersion, the bandwidth at the selected wavelength varies between 4 and 10 nm. A strategy for the elimination of temporal drift and system fluctuations was implemented by the acquisition of a reference signal. To effectively cover the desired broadband range (500 to 1700 nm), two detectors were employed: (i) at 500 to 1100 nm, a silicon photomultiplier (SiPM, Excelitas Technologies, C30742-11-050-T1) with home-made front-end electronics21 that guarantees a narrow temporal response [about 100 ps full-width at half maximum (FWHM)] and a good detection efficiency (peak of 40% at 500 nm and 10% at 800 nm) and (ii) at 1100 to 1700 nm, an InGaAs/InP SPAD detection module,22 with a good temporal resolution (<200  ps FWHM), good detection efficiency (peak of 42% at 1100 nm and still 15% at 1600 nm), and moderate dark count rate and afterpulsing (15 kcps and 5%, respectively, when operated in gated mode at T=230  K). A detailed description on various aspects of the system can be found in Refs. 10, 20, and 23.

Fig. 1

Schematic of experimental setup with SiPM (500 to 1100 nm) and InGaAs/InP SPAD (1100 to 1700 nm). The inset on the right shows the customized transmittance probe.



Sample and Measurement Protocol

Type I collagen from bovine achilles tendon (C9879) was purchased from Sigma-Aldrich. Collagen flakes were placed in a customized cylinder of a wide diameter (105 mm), which minimizes contamination of the absorption spectrum arising from photons reflected at the cylinder boundaries. The sample was tightly packed (density=0.206  g/cm3) to minimize air gap between the flakes. Measurements were performed in transmittance geometry, with 6.8 mm as sample thickness. The wavelength was tuned in 5-nm steps over the range 500 to 1700 nm. Data were collected four times for each wavelength. In the visible range (500 to 650 nm), to eliminate fluorescence, bandpass filters (BP filters) of 10-nm bandwidth were placed in front of the detector at each measured wavelength.


Data Analysis

The absorption coefficient of collagen at each wavelength was extracted by fitting the measured temporal curves to a solution of the diffusion equation24 with extrapolated boundary conditions25 for an infinite slab model. The fitting range for residual minimization between theoretical and measured curves extended from 80% of the rising edge to 1% of the falling edge of the temporal curves, effectively using the entire time window for residual minimization.

At long wavelengths (1100 to 1700 nm), a fitting procedure based on Monte Carlo (MC) simulations was used to extract the absorption spectrum.10,26 In particular, a library of temporal point spread functions (TPSFs) was created at different scattering values and null absorption. Later, the absorption was added by multiplying the TPSF by the Lambert–Beer exponential term exp(μavt), where μa is the absorption coefficient, v is the light speed in the medium, and t is the photon time-of-flight. A linear interpolation was used to obtain the TPSF for scattering values different from the simulated TPSFs scattering.10

Apart from the above mentioned routinely used fitting range, analysis was also performed for different fitting ranges (80% to 50%, 80% to 10%, and 80% to 1%) to understand the effect of the laser bandwidth on the estimated absorption. As reported in the literature,27 large source bandwidth distorts the recovered absorption values. This is due to the contamination of the tail part of the temporal profile by less absorbed photons in the source bandwidth, which happens under the conditions of sharp absorption variation. In our case, a bandwidth test is needed in the SWIR region, where our system has a large bandwidth (5 to 10 nm) and collagen shows sharp absorption variations. In the visible region, in the presence of fluorescence, we expect an absorption underestimation for extended fitting ranges, such as 80% to 1%, due to increased late fluorescence photons. Therefore, we tested for the effect of fluorescence and took actions to eliminate it (as described above and discussed in the following).


Results and Discussion

The absorption and reduced scattering spectra of collagen measured by our system are shown in Fig. 2 (black and red lines, respectively). As expected, the absorption in the SWIR region is higher than in the visible region. Major peaks of collagen are observed, such as the second overtone of CH2 stretching16 around 1200 nm, the peak around 1500 nm corresponding to a combination of CH2 stretching and nonstretching modes,15 and the shoulder around 1700 nm due to the first overtone of CH3 stretching.16 Comparing peak intensities, a factor of 5 is seen between the 1200- and 1000-nm peaks, whereas the shoulder at 1700 nm and the peak around 1500 nm are found to be 25 and 38 times stronger than the absorption peaks at 1000 and 930 nm, respectively.

Fig. 2

Absorption (squares) and reduced scattering (triangles) spectrum of type I collagen.


To provide the best possible assessment of collagen optical properties, care was taken both in the measurement setup and data analysis.

Residual collagen fluorescence was observed, which elongates the tail of experimental time-resolved curves especially at short wavelengths, thereby distorting the estimated absorption spectrum. Therefore, the undesired contribution of fluorescence was effectively suppressed by means of filters in the 500- to 650-nm region. As discussed in Sec. 2.3, we also cross checked this in the analysis process, which confirmed the absence of significant fluorescence contributions to the collected data.

Moreover, the possible effect of the finite sample size on the estimated absorption coefficient was investigated. Specifically, tests for boundary influence were performed by fitting experimental data with a brick model28 and comparing the results with what was obtained with the infinite slab model. The tests proved that no boundary effects were present, as a result of the small source–detector distance (6.8 mm) and large diameter of the cylindrical sample holder (105 mm).

At long wavelengths, increased absorption posed challenges on the validity of the diffusion equation, as scattering and absorption become comparable. To overcome this problem, the MC technique was used to analyze data ranging from 1100 to 1700 nm.

Finally, as described in Sec. 2.3, tests were performed to understand the effect of the laser bandwidth on our measurements. To this end, we fitted the temporal-resolved curves over different fitting ranges (80% to 50%, 80% to 10%, and 80% to 1%). Since negligible differences were observed upon changing the fitting range, we can exclude possible bandwidth distortion, which are more pronounced for wider fitting ranges on the trailing edge of the photon temporal distribution.27

The instrument performances were rigorously validated elsewhere over the two spectral ranges R1=500 to 1100 nm and R2=1100 to 1700 nm.10,20 In particular, the accuracy and linearity tests were performed applying the MEDPHOT protocol,29 achieving an agreement within 3% in the R1 range up to μa=1  cm1 and for μs5  cm1. For the R2 range, using MC fitting, good linearity is observed up to μa=2.6  cm1 with μs=8  cm1. Furthermore, as a comprehensive spectral validation, very good agreement was obtained on the recovery of the spectrum of water for R1 and of lipids for R2. The noise in the retrieved absorption (coefficient of variation of the fitted values) is 2% under standard operating conditions (106 photon counts, μa=0.1  cm1, μs=10  cm1). The temporal stability over 1 h is 3%, and the day-to-day reproducibility is within 4%.

To further add confidence on our results, we present the scattering spectrum of collagen in Fig. 2. The scattering of collagen ranges between 8 and 29  cm1, which is well within the validated range of our system. The scattering spectrum is almost flat in the visible range (500 to 1100 nm) while fluctuations are observed in the SWIR range. However, such fluctuations are instrument response function dependent and have minimal effect on the estimate of the absorption properties in the absorption range of 0.1 to 2.6  cm1,10 which is well within the collagen absorption range. Importantly, to further avoid these random fluctuations from affecting the absorption spectrum, we smoothened the scattering spectrum and fixed its value when fitting to estimate the absorption spectrum, as described in detail elsewhere.30,31

We compared our data with collagen absorption spectra reported in the literature,8,19,32,33 as displayed in Fig. 3. The main peaks are in good agreement for all the reported studies. Differences corresponding to a multiplying factor among the spectra can be attributed to the difference in density of the collagen samples analyzed. The sample used by Tsai et al.32 and Biggs33 is gelatin, which is a hydrolyzed form of collagen. Gelatin has translucent optical properties and could raise some challenges for conventional spectrophotometers, which are typically designed to work with transparent samples. This seems to affect the measurement in the form of an offset, possibly more marked at short wavelengths, which would be in agreement with the contribution of residual scattering. However, the general trend of gelatin matches all other reported spectra. Nachabé et al.8 used the same type I collagen as in the present study, but they exploited an integrating sphere on collagen tightly packed in cuvette. The spectrum trend seems to be in line with our spectrum up to 1400 nm. At longer wavelengths, it differs in trend and seems to underestimate the marked absorption around 1500 nm, compared both to our spectrum and Ref. 33. At short wavelengths, the presence of fluorescence can distort the absorption spectrum unless it is eliminated, as in our case.

Fig. 3

Spectra of collagen reported in the literature along with our measured spectrum (triangles).


To understand the impact of collagen absorption in a diffuse reflectance measurement on biological tissues, we have calculated the contribution of key tissue constituents on the absorption spectrum of a bone prominence location. The radius distal location was chosen, as it has thin superficial muscle, giving easy access to bone probing in an in vivo measurement.14 Importantly, radius distal is one among the locations considered for bone mineral assessment by DXA scans and is rich of collagen, which can be of interest for diagnosing bone pathologies.34 Figure 4 depicts the calculated broadband (500 to 1700 nm) weighted spectra of major tissue constituents, namely oxy-hemoglobin (HbO2, 16  μM), deoxy-hemoglobin (Hb, 6  μM), lipid (750  mg/cm3), water (300  mg/cm3), and collagen (120  mg/cm3) for a simulated in vivo measurement on the forearm at radius distal location. The data required for this simulation were derived from in vivo measurements reported in Ref. 35. From Fig. 4, collagen dominates the other constituents around 700 nm, where the absorption of water and lipid is minimal, and also Hb and HbO2 contributions are small. Still, the flat spectral shape of collagen in this region hampers its robust estimate. Local minima of water and local maxima of collagen around 1050 nm make it a suitable spectral window for collagen detection. However, the competing lipid peak and limited penetration depth of light due to high scattering challenge the detection of deep tissue collagen (bone). Finally, around 1700 nm, the absorption of collagen is 5 times stronger than lipid absorption, compared to only 1.5 times around 1100 nm, and water shows a local minimum. These features could make the spectral range around 1700 nm more specific for in vivo collagen detection from deep tissues, provided that the decrease in penetration depth and signal due to increased absorption is compensated by the penetration depth and signal gained by decrease in scattering at 1700 nm.36 Apart from this specific example, due to its specificity and high absorption, we predict that the detection of collagen peak at 1700 nm may have potential applications in the intraoperative and in vitro study of collagen-rich regions.

Fig. 4

Simulation of in vivo constituent spectra for a measurement performed at radius distal location. Average constituent concentrations are extracted from Ref. 35.




We have presented the first absorption characterization of collagen flakes in the 500- to 1700-nm range performed using time-domain diffuse optics. Few other spectra, obtained with different techniques and/or on other collagen forms, are reported in the literature. Apart from amplitude differences due to the different form of collagen (gelatin) and density/concentration, the main spectral features in our spectrum are similar to the previously published results. In our measurement and analysis, we paid specific attention to eliminate/suppress florescence, boundary effects, bandwidth effects, and absorption coupling with scattering by thorough setup of the experimental apparatus (large sample size, small source–detector separation, and use of BP filters) and use of MC simulations to fit collected data. An insightful simulation of measurements performed at radius distal location was presented, investigating the potential for in vivo studies in the 1100- to 1700-nm range. Improving instrument sophistication (e.g., integrated circuit solutions for InGaAs/InP SPAD under development37) will guarantee strong cost and size reduction in the near future, making miniature handheld technologies available,5 and we see point of care devices as one of the possible beneficiary to use these long-wavelength spectral features at in situ conditions.


No conflicts of interest, financial or otherwise, are declared by the authors.


This work was supported by Optical Imaging and Laser Techniques for Biomedical Applications (OILTEBIA) under Grant No. 317526. Sanathana Konugolu Venkata Sekar is a Marie Curie fellow funded by the OILTEBIA Project. The authors wish to thank Provincia Autonoma di Bolzano for financial support in developing the InGaAs/InP SPAD module.


1. T. Durduran et al., “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010). http://dx.doi.org/10.1088/0034-4885/73/7/076701 Google Scholar

2. G. Strangman et al., “A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation,” NeuroImage 17(2), 719–731 (2002).NEIMEF1053-8119 http://dx.doi.org/10.1006/nimg.2002.1227 Google Scholar

3. P. Taroni et al., “Clinical trial of time-resolved scanning optical mammography at 4 wavelengths between 683 and 975 nm,” J. Biomed. Opt. 9(3), 464–473 (2004).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.1695561 Google Scholar

4. A. Cerussi et al., “Predicting response to breast cancer neoadjuvant chemotherapy using diffuse optical spectroscopy,” Proc. Natl. Acad. Sci. U. S. A. 104(10), 4014–4019 (2007). http://dx.doi.org/10.1073/pnas.0611058104 Google Scholar

5. A. Pifferi et al., “New frontiers in time-domain diffuse optics, a review,” J. Biomed. Opt. 21(9), 091310 (2016).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.JBO.21.9.091310 Google Scholar

6. D. Khoptyar et al., “Broadband photon time-of-flight spectroscopy of pharmaceuticals and highly scattering plastics in the VIS and close NIR spectral ranges,” Opt. Express 21, 20941–20953 (2013).OPEXFF1094-4087 http://dx.doi.org/10.1364/OE.21.020941 Google Scholar

7. A. Pifferi et al., “Fully automated time domain spectrometer for the absorption and scattering characterization of diffusive media,” Rev. Sci. Instrum. 78(5), 053103 (2007).RSINAK0034-6748 http://dx.doi.org/10.1063/1.2735567 Google Scholar

8. R. Nachabé et al., “Diagnosis of breast cancer using diffuse optical spectroscopy from 500 to 1600 nm: comparison of classification methods,” J. Biomed. Opt. 16(8), 087010 (2011).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.3611010 Google Scholar

9. R. H. Wilson et al., “Review of short-wave infrared spectroscopy and imaging methods for biological tissue characterization,” J. Biomed. Opt. 20(3), 030901 (2015).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.JBO.20.3.030901 Google Scholar

10. I. Bargigia et al., “Time-resolved diffuse optical spectroscopy up to 1700 nm by means of a time-gated InGaAs/InP single-photon avalanche diode,” Appl. Spectrosc. 66(8), 944–950 (2012).APSPA40003-7028 http://dx.doi.org/10.1366/11-06461 Google Scholar

11. P. Taroni et al., “Optical identification of subjects at high risk for developing breast cancer,” J. Biomed. Opt. 18(6), 060507 (2013).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.JBO.18.6.060507 Google Scholar

12. P. P. Provenzano et al., “Collagen density promotes mammary tumor initiation and progression,” BMC Med. 6(1), 1–15 (2008). http://dx.doi.org/10.1186/1741-7015-6-11 Google Scholar

13. A. Pifferi et al., “Optical biopsy of bone tissue: a step toward the diagnosis of bone pathologies,” J. Biomed. Opt. 9(3), 474–480 (2004).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.1691029 Google Scholar

14. S. K. V. Sekar et al., “Time-resolved diffused optical characterization of key tissue constituents of human bony prominence locations,” Proc. SPIE 9538, 95380X (2015).PSISDG0277-786X http://dx.doi.org/10.1117/12.2183724 Google Scholar

15. P. Wang et al., “Bond-selective imaging of deep tissue through the optical window between 1600 and 1850 nm,” J. Biophotonics 5(1), 25–32 (2012). http://dx.doi.org/10.1002/jbio.v5.1 Google Scholar

16. P. Wang, “Mapping lipid and collagen by multispectral photoacoustic imaging of chemical bond vibration,” J. Biomed. Opt. 17(9), 096010 (2012).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.JBO.17.9.096010 Google Scholar

17. N. D. Kirkpatrick et al., “In vitro model for endogenous optical signatures of collagen,” J. Biomed. Opt. 11(5), 054021 (2006).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.2360516 Google Scholar

18. G. Vunjak-Novakovic et al., “Dynamic cell seeding of polymer scaffolds for cartilage tissue engineering,” Biotechnol. Prog. 14(2), 193–202 (1998). http://dx.doi.org/10.1021/bp970120j Google Scholar

19. P. Taroni et al., “Diffuse optical spectroscopy of breast tissue extended to 1100 nm,” J. Biomed. Opt. 14(5), 054030 (2009).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.3251051 Google Scholar

20. S. K. V. Sekar et al., “Broadband (600–1350 nm) time-resolved diffuse optical spectrometer for clinical use,” IEEE J. Sel. Top. Quantum Electron. 22(3), 7100609 (2016).IJSQEN1077-260X http://dx.doi.org/10.1109/JSTQE.2015.2506613 Google Scholar

21. E. Martinenghi et al., “Time-resolved single-photon detection module based on silicon photomultiplier: a novel building block for time-correlated measurement systems,” Rev. Sci. Instrum. 87(87), 073101 (2016). http://dx.doi.org/10.1063/1.4954968 Google Scholar

22. A. Tosi et al., “Fully programmable single-photon detection module for InGaAsInP single-photon avalanche diodes with clean and sub-nanosecond gating transitions,” Rev. Sci. Instrum. 83(1), 013104 (2012).RSINAK0034-6748 http://dx.doi.org/10.1063/1.3675579 Google Scholar

23. S. K. V. Sekar et al., “Broadband time-resolved diffuse optical spectrometer for clinical diagnostics: characterization and in-vivo measurements in the 600–1350 nm spectral range,” Proc. SPIE 9538, 95380R (2015). http://dx.doi.org/10.1117/12.2183835 Google Scholar

24. D. Contini, F. Martelli and G. Zaccanti, “Photon migration through a turbid slab described by a model based on diffusion approximation. I. Theory,” Appl. Opt. 36(19), 4587–4599 (1997).APOPAI0003-6935 http://dx.doi.org/10.1364/AO.36.004587 Google Scholar

25. R. C. Haskell et al., “Boundary conditions for the diffusion equation in radiative transfer,” J. Opt. Soc. Am. A 11(10), 2727–2741 (1994). http://dx.doi.org/10.1364/JOSAA.11.002727 Google Scholar

26. E. Alerstam, T. Svensson and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13(6), 060504 (2008).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.3041496 Google Scholar

27. A. Farina et al., “Bandpass effects in time-resolved diffuse spectroscopy,” Appl. Spectrosc. 63(1), 48–56 (2009).APSPA40003-7028 http://dx.doi.org/10.1366/000370209787169795 Google Scholar

28. F. Martelli et al., Light Propagation through Biological Tissue and Other Diffusive Media: Theory, Solutions and Software, SPIE Press, Bellingham, Washington (2009). Google Scholar

29. A. Pifferi et al., “Performance assessment of photon migration instruments: the MEDPHOT protocol,” Appl. Opt. 44(11), 2104–2114 (2005).APOPAI0003-6935 http://dx.doi.org/10.1364/AO.44.002104 Google Scholar

30. R. Cubeddu et al., “Experimental test of theoretical models for time-resolved reflectance,” Med. Phys. 23(9), 1625–1633 (1996).MPHYA60094-2405 http://dx.doi.org/10.1118/1.597739 Google Scholar

31. I. Bargigia et al., “Diffuse optical techniques applied to wood characterisation,” J. Near Infrared Spectrosc. 21(4), 259–268 (2013). http://dx.doi.org/10.1255/jnirs.1068 Google Scholar

32. C. L. Tsai, J. C. Chen and W. J. Wang, “Near-infrared absorption property of biological soft tissue constituents,” J. Med. Biol. Eng. 21(1), 7–14 (2001).IYSEAK0021-3292 Google Scholar

33. K. M. Biggs, “A physical model of human skin and its application for search and rescue,” Dissertation, pp. 25–27, Air Force Institute of Technology (2009). Google Scholar

34. G. M. Blake and I. Fogelman, “The role of DXA bone density scans in the diagnosis and treatment of osteoporosis,” Postgrad. Med. J. 83(982), 509–517 (2007). http://dx.doi.org/10.1136/pgmj.2007.057505 Google Scholar

35. S. K. V. Sekar et al., “In vivo time domain broadband (600–1200 nm) diffuse optical characterization of human bone,” in Biomedical Optics 2016, JTu3A.32, (2016). http://dx.doi.org/10.1364/CANCER.2016.JTu3A.32 Google Scholar

36. F. Martelli et al., “There’s plenty of light at the bottom: statistics of photon penetration depth in random media,” Sci. Rep. 6, 27057 (2016). http://dx.doi.org/10.1038/srep27057 Google Scholar

37. A. Ruggeri et al., “Integrated circuit for subnanosecond gating of InGaAs/InP SPAD,” IEEE J. Quantum Electron. 51(7), 1–7 (2015).IEJQA70018-9197 http://dx.doi.org/10.1109/JQE.2015.2438436 Google Scholar


Sanathana Konugolu Venkata Sekar received his master’s degree in physics (gold medalist) with photonics specialization from Sri Sathya Sai Institute of Higher Learning, India, in 2013. He is currently pursuing his PhD at Politecnico di Milano, Milan, Italy, under a Marie Curie innovative training network fellowship (Optical Imaging and Laser Techniques for Biomedical Applications). His current research interests include diffuse optical spectroscopy (DOS), DOS applied to in vivo clinical studies, DOS and Raman instrumentation, and time-domain Raman spectroscopy of diffusive media.

Ilaria Bargigia received her master’s degree in physical engineering and her PhD in physics from Politecnico di Milano, Milan, Italy, in 2009 and 2013, respectively. Since January 2014, she has been a postdoctoral fellow at the Center for Nano Science and Technology at PoliMi, Istituto Italiano di Tecnologia, Milano, Italy. Her present research activity is mainly focused on time-resolved photoluminescence spectroscopy of light harvesting systems.

Alberto Dalla Mora is an assistant professor at the Department of Physics, Politecnico di Milano, Italy, since 2011. He graduated summa cum laude in electronics engineering from Politecnico di Milano in 2006, and he received his PhD summa cum laude in information and communication technology from the same university in 2010. He authored more than 35 papers in international peer-reviewed journals. Currently, his research interests include time-resolved diffuse spectroscopy instrumentation and applications for biomedical diagnosis.

Paola Taroni received her degree in nuclear engineering from Politecnico di Milano, Milan, Italy, in 1987. From 1987 to 1988, she was a visiting scientist at the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. She is a full professor of physics at Politecnico di Milano. She coauthored more than 120 scientific papers on peer-reviewed journals and 1 international patent. Her current research involves the development of laser systems for time-resolved spectroscopy and imaging and their applications in biology and medicine.

Alessandro Ruggeri received his master’s degree in electronics engineering and his PhD in information technology from the Politecnico di Milano, Milan, Italy, in 2012 and 2015, respectively. He is currently a research assistant at Politecnico di Milano. In summer 2014, he was with the IBM T. J. Watson Research Center, working on ultra large-scale integration. His main research interest regards the development of electronics for near-infrared single-photon avalanche diodes (SPADs) for biomedical and communications applications.

Alberto Tosi received his master’s degree in electronics engineering and his PhD in information technology engineering from the Politecnico di Milano, Milan, Italy, in 2001 and 2005, respectively. He was an assistant professor from 2006 to 2014. He has been an associate professor of electronics with the Politecnico di Milano since 2014. He currently works on silicon and InGaAs/InP SPADs. His research activity includes arrays of silicon SPADs for 2-D and 3-D applications and time-correlated single-photon counting electronics.

Antonio Pifferi is a full professor in the Department of Physics, Politecnico di Milano, Italy. He received his MS degree in nuclear engineering from Politecnico di Milano in 1991 and his PhD in physics from Politecnico di Torino in 1995. He was a visiting scientist at Lund Institute of Technology, Sweden, from 1995 to 1996. His research focuses on the development of new laser techniques and instrumentation for diagnosis, and on the study of light propagation in diffusive media, with applications to optical biopsy, optical mammography, and functional brain imaging.

Andrea Farina received his MS in electronics engineering in 2005 and the PhD in physics in 2009 from Politecnico di Milano, Italy. Since 2009, he has been a postdoc in the Department of Physics at Politecnico di Milano. In 2011, he became a researcher for the Italian Research National Council at the Institute of Photonics and Nanotechnologies. His research interests are devoted to the study of photon migration using time-resolved and structured light for imaging and spectroscopy.

© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Sanathana Konugolu Venkata Sekar, Sanathana Konugolu Venkata Sekar, Ilaria Bargigia, Ilaria Bargigia, Alberto Dalla Mora, Alberto Dalla Mora, Paola Taroni, Paola Taroni, Alessandro Ruggeri, Alessandro Ruggeri, Alberto Tosi, Alberto Tosi, Antonio Pifferi, Antonio Pifferi, Andrea Farina, Andrea Farina, } "Diffuse optical characterization of collagen absorption from 500 to 1700 nm," Journal of Biomedical Optics 22(1), 015006 (31 January 2017). https://doi.org/10.1117/1.JBO.22.1.015006 . Submission: Received: 22 September 2016; Accepted: 6 January 2017
Received: 22 September 2016; Accepted: 6 January 2017; Published: 31 January 2017

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