Our services are grouped into six main categories:
- Light Transport and Modeling
- Spectroscopy and Imaging
- Data analysis and Machine Learning
- Pathology Analysis
- Biofluidic Spectroscopy
- Phantom Design and Characterization
If you or your team are interested in one or more our services, you are welcome to contact us!
Our hourly rates for each service are tabulated here.
If your requested service(s) is part of a specific industrial or technology-transfer project taking place over a substantial period of time, the associated costs can be affected in form of an industrial funding upon agreement.
- Hyperspectral Monte Carlo simulation
a) Simulating elastic and inelastic light transport
b) Depth-resolved analysis of light transport in tissues and phantoms
- Optical design and modeling
a) Sequential and nonsequential ray tracing of optical systems
b) Finite element and finite difference time domain (FDTD) simulation of light propagation and its interaction with photonic structures
c) Optical waveguide design and simulation
d) Optical analysis of periodic structures and plasmonic systems elastic
Our related articles:
 A. Akbarzadeh et al., “Experimental validation of a spectroscopic Monte Carlo light transport simulation technique and Raman scattering depth sensing analysis in biological tissue”, accepted for publication on J. Biomed. Opt. (2020).
 F. Dallaire et al., “Method for quantitative spectral quality assessment applied to Raman spectroscopy measurements in surgical applications”, Journal of Biomedical Optics, 25, 4, 040502 (2020).
- Raman Spectroscopy
a) Raman mesoscopic spectroscopy
b) Wide-field macroscopic line-scanning Raman spectroscopy
c) Confocal Raman microscopy
d) Spatially offset Raman spectroscopy (SORS)
e) Surface enhanced Raman spectroscopy (SERS)
- Wide-field spectroscopy
a) Fluorescence spatial frequency domain imaging (SFDI)
b) Diffuse reflectance spectroscopy
c) Phase shifting profilometry
Our related articles:
 K. Aubertin et al., “Mesoscopic characterization of prostate cancer using Raman spectroscopy: Potential for diagnostics and therapeutics”, British Journal of Urology – International 122 (2), 326-336 (2018).
 M. Jermyn et al., “A review of Raman spectroscopy advances with an emphasis on clinical translation challenges in oncology,” Phys. Med. Biol. 61 (23), R370 (2016).
 J. Desroches et al., “Raman spectroscopy in microsurgery: impact of operating microscope illumination sources on data quality and tissue classification,” Analyst, 142, 1185 (2017).
 J. Desroches et al., “A new method using Raman spectroscopy for in vivo targeted brain cancer tissue biopsy,” Sci. Rep. 8, 1792 (2018).
 A. St-Georges-Robillard et al., “Fluorescence hyperspectral imaging for live monitoring of multiple spheroids in microfluidic chips,” Analyst 143(16), 3829 (2018).
 E. Beaulieu et al., “Wide-field optical spectroscopy system integrating reflectance and spatial frequency domain imaging to measure attenuation-corrected intrinsic tissue fluorescence in radical prostatectomy specimens,” Biomed. Opt. Express, 11 (4), 2052 (2020).
 D. DePaoli et al., “Rise of Raman spectroscopy in neurosurgery: a review,” J. Biomed. Opt. 25(5), 050901 (2020).
- Machine learning, deep learning, and multivariate analysis
a) Data pattern recognition (dependence and interdependence statistical analysis)
b) Feature engineering and Bayesian statistical analysis
c) Training and validation of predictive classification/regression models
d) Computer vision and feature extraction
- Spectroscopic processing
a) Processing of raw fluorescence and Raman spectra
b) Instrument response correction
c) Cosmic ray removal
d) Fluorescence baseline removal
e) Signal normalization
Our related articles:
 E. Lemoine, “Feature engineering applied to intraoperative Raman spectroscopy sheds light on molecular processes in brain cancer: a retrospective study of 65 patients,” Analyst, 144 (22), 6517-6532 (2019).
 A.-A. Grosset et al., “Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case–control study with multicohort validation,” PLOS Med. 17(8), e1003281 (2020).
- Histological Analysis
a) Sample preparation
b) Interpretation of histological slides from all systems and organs
c) Precise localization of Raman acquisition on samples
d) Biological tissue identification (tissue type, cancer vs healthy)
e) Surgical margin assessment
- Biofluidic spectroscopy
a) Sample preparation (filteration, centrifugation, nanoparticle addition)
b) Spectral acquisition using a cuvette system (bulk biofluid)
c) Spectral acquisition using a microscope (dried samples)
- Sample preparation
a) Determining appropriate absorbent, scattering, fluorescence, and Raman agents as ingredients
b) Recipe planning for generating a phantom with relevant optical properties
c) Phantom fabrication: Mixing the agents and bases, molding, 3D printing, and spin coating
- Phantom characterization
a) Validating dimensions with a customized optical coherence tomography (OCT) system
b) Measuring absorption and scattering coefficients
c) Raman and fluorescence spectral measurements
d) Raman and fluorescence spectral separation using mathematical procedures