Prof. Frédéric Leblond Profile

Prof. Frédéric Leblond

Professor

SPIE Involvement:

Conference Program Committee | Editorial Board Member: Journal of Biomedical Optics | Author | Special Event Speaker

Publications (74)

SPIE Journal Paper | 6 June 2024 Open Access

Open Access

Macroscopic inelastic scattering imaging using a hyperspectral line-scanning system identifies invasive breast cancer in lumpectomy and mastectomy specimens

Sandryne David, Hugo Tavera, Tran Trang, Frédérick Dallaire, François Daoust, Francine Tremblay, Lara Richer, Sarkis Meterissian, Frédéric Leblond

JBO, Vol. 29, Issue 06, 065004, (June 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.6.065004

KEYWORDS: Cancer, Raman spectroscopy, Hyperspectral imaging, Tissues, Surgery, Breast, Tumor growth modeling, Breast cancer, Adipose tissue, Biological samples

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Proceedings Article | 13 March 2024 Presentation

Presentation

Line-scanning Raman spectroscopy imaging system for cancer margin detection with spatial-offset depth-resolved imaging capabilities

Hugo Tavera, Sandryne David, François Daoust, Guillaume Sheehy, Trang Tran, Sarkis Meterissian, Frédéric Leblond

Proceedings Volume PC12836, PC1283609 (2024) https://doi.org/10.1117/12.3003091

KEYWORDS: Imaging systems, Raman spectroscopy, Cancer detection, Tissues, Cancer, Visible radiation, RGB color model, Near infrared, Imaging devices, Beam path

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Proceedings Article | 13 March 2024 Presentation

Presentation

Towards non-contact and real-time large field-of-view single-band Raman spectroscopy imaging in brain cancer surgery

Frédéric Leblond, Sandryne David, Samaneh Pahlavani, Nassim Ksantini, Frédérick Dallaire, Guillaume Sheehy, Costas Hadjapanayis, Kevin Petrecca, Brian Wilson

Proceedings Volume PC12825, PC1282505 (2024) https://doi.org/10.1117/12.3001248

KEYWORDS: Imaging spectroscopy, Raman spectroscopy, Cancer detection, Brain, Surgery, Brain cancer, Tumors, Imaging systems, Cancer, Vibration

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Proceedings Article | 13 March 2024 Presentation

Presentation

Novel Raman spectroscopy fiber optics system design can enable real-time intraoperative tissue probing for focal cortical dysplasia in children

Hugo Le Moël, Trang Tran, Sandryne David, Guillaume Sheehy, Roy Dudley, Frédéric Leblond

Proceedings Volume PC12831, PC1283105 (2024) https://doi.org/10.1117/12.3001084

KEYWORDS: Raman spectroscopy, Tissues, Fiber optics, Design and modelling, Tissue optics, Surgery, Epilepsy, Brain tissue, Brain, Signal processing

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Proceedings Article | 13 March 2024 Presentation

Presentation

Rapid detection of COVID-19 in liquid saliva using reagent-free Raman spectroscopy

Katherine Ember, Nassim Ksantini, Frédéric Dallaire, Guillaume Sheehy, Antoine Noé, Trang Tran, Madeleine Durand, Dominique Trudel, Frederic Leblond

Proceedings Volume PC12850, PC1285005 (2024) https://doi.org/10.1117/12.3001877

KEYWORDS: COVID 19, Artificial intelligence, Portability, Statistical modeling, Statistical analysis, Raman spectroscopy, Spectroscopy, Receivers, Point-of-care devices, Machine learning

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SPIE Journal Paper | 9 September 2023 Open Access

Open Access

Subsecond lung cancer detection within a heterogeneous background of normal and benign tissue using single-point Raman spectroscopy

Frédéric Leblond, Frédérick Dallaire, Trang Tran, Rajeev Yadav, Kelly Aubertin, Eric Goudie, Philippe Romeo, Christopher Kent, Charles Leduc, Moishe Liberman

JBO, Vol. 28, Issue 09, 090501, (September 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.9.090501

KEYWORDS: Tissues, Lung cancer, Raman spectroscopy, Cancer detection, Cancer, Tumor growth modeling, Lung, Diagnostics, Data modeling, Education and training

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SPIE Journal Paper | 31 May 2023 Open Access

Open Access

Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model

Ivan Kosik, Frédérick Dallaire, Layla Pires, Trang Tran, Frédéric Leblond, Brian Wilson

JBO, Vol. 28, Issue 05, 057003, (May 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.5.057003

KEYWORDS: Tissues, Bone, Raman spectroscopy, Data modeling, Spinal cord, In vivo imaging, Surgery, Education and training, Muscles, Cartilage

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SignificanceOrthopedic surgery is frequently performed but currently lacks consensus and availability of ideal guidance methods, resulting in high variability of outcomes. Misdirected insertion of surgical instruments can lead to weak anchorage and unreliable fixation along with risk to critical structures including the spinal cord. Current methods for surgical guidance using conventional medical imaging are indirect and time-consuming with unclear advantages.AimThe purpose of this study was to investigate the potential of intraoperative in situ near-infrared Raman spectroscopy (RS) combined with machine learning in guiding pedicular screw insertion in the spine.ApproachA portable system equipped with a hand-held RS probe was used to make fingerprint measurements on freshly excised porcine vertebrae, identifying six tissue types: bone, spinal cord, fat, cartilage, ligament, and muscle. Supervised machine learning techniques were used to train—and test on independent hold-out data subsets—a six-class model as well as two-class models engineered to distinguish bone from soft tissue. The two-class models were further tested using in vivo spectral fingerprint measurements made during intra-pedicular drilling in a porcine spine model.ResultsThe five-class model achieved >96 % accuracy in distinguish all six tissue classes when applied onto a hold-out testing data subset. The binary classifier detecting bone versus soft tissue (all soft tissue or spinal cord only) yielded 100% accuracy. When applied onto in vivo measurements performed during interpedicular drilling, the soft tissue detection models correctly detected all spinal canal breaches.ConclusionsWe provide a foundation for RS in the orthopedic surgical guidance field. It shows that RS combined with machine learning is a rapid and accurate modality capable of discriminating tissues that are typically encountered in orthopedic procedures, including pedicle screw placement. Future development of integrated RS probes and surgical instruments promises better guidance options for the orthopedic surgeon and better patient outcomes.

SPIE Journal Paper | 30 March 2023 Open Access

Open Access

In situ Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer

Sandryne David, Trang Tran, Frédérick Dallaire, Guillaume Sheehy, Feryel Azzi, Dominique Trudel, Francine Tremblay, Atilla Omeroglu, Frédéric Leblond, Sarkis Meterissian

JBO, Vol. 28, Issue 03, 036009, (March 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.3.036009

KEYWORDS: Cancer, Raman spectroscopy, Tissues, Tumor growth modeling, Machine learning, Breast, Surgery, Breast cancer, Biomolecules, Tumors

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Proceedings Article | 15 March 2023 Poster + Paper

Poster + Paper

Raman macroscopic imaging system for intraoperative brain cancer detection

Patrick Orsini, Joannie Desroches, François Daoust, Hugo Tavera, Frédérick Dallaire, Keven Savard, Jacques Bismuth, Philippe Mckoy, Israel Veilleux, Kevin Petrecca, Frédéric Leblond

Proceedings Volume 12361, 123610H (2023) https://doi.org/10.1117/12.2668370

KEYWORDS: Imaging systems, Raman spectroscopy, Adipose tissue, Tissues, Biological imaging, Tissue optics, Muscles, Design and modelling, Helium, Classification systems

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SPIE Journal Paper | 21 February 2023 Open Access

Open Access

Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids

Guillaume Sheehy, Fabien Picot, Frédérick Dallaire, Katherine Ember, Tien Nguyen, Kevin Petrecca, Dominique Trudel, Frédéric Leblond

JBO, Vol. 28, Issue 02, 025002, (February 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.2.025002

KEYWORDS: Raman spectroscopy, Data processing, Tissues, Equipment, Bubbles, Biological samples, Raman scattering, Fluorescence, Aluminum, Spectroscopy

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SPIE Journal Paper | 9 September 2022 Open Access

Open Access

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 2: in-vivo tumor-targeting using a classification model combining spectral and MRI-radiomics features

David Grajales , Fabien Picot, Roozbeh Shams, Frédérick Dallaire, Guillaume Sheehy, Stephanie Alley, Maroie Barkati, Guila Delouya, Jean-Francois Carrier, Mirela Birlea, Dominique Trudel, Frédéric Leblond, Cynthia Ménard, Samuel Kadoury

JBO, Vol. 27, Issue 09, 095004, (September 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.9.095004

KEYWORDS: Raman spectroscopy, Navigation systems, Remote sensing, Tumors, Image segmentation, Data modeling, Principal component analysis, In vivo imaging, Biopsy, Tissue optics

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Significance: The diagnosis and treatment of prostate cancer (PCa) are limited by a lack of intraoperative information to accurately target tumors with needles for biopsy and brachytherapy. An innovative image-guidance technique using optical devices could improve the diagnostic yield of biopsy and efficacy of radiotherapy.Aim: To evaluate the performance of multimodal PCa detection using biomolecular features from in-situ Raman spectroscopy (RS) combined with image-based (radiomics) features from multiparametric magnetic resonance images (mpMRI).Approach: In a prospective pilot clinical study, 18 patients were recruited and underwent high-dose-rate brachytherapy. Multimodality image fusion (preoperative mpMRI with intraoperative transrectal ultrasound) combined with electromagnetic tracking was used to navigate an RS needle in the prostate prior to brachytherapy. This resulting dataset consisted of Raman spectra and co-located radiomics features from mpMRI. Feature selection was performed with the constraint that no more than 10 features were retained overall from a combination of inelastic scattering spectra and radiomics. These features were used to train support vector machine classifiers for PCa detection based on leave-one-patient-out cross-validation.Results: RS along with biopsy samples were acquired from 47 sites along the insertion trajectory of the fiber-optics needle: 26 were confirmed as benign or grade group = 1, and 21 as grade group >1, according to histopathological reports. The combination of the fingerprint region of the RS and radiomics showed an accuracy of 83% (sensitivity = 81 % and a specificity = 85 % ), outperforming by more than 9% models trained with either spectroscopic or mpMRI data alone. An optimal number of features was identified between 6 and 8 features, which have good potential for discriminating grade group ≥1 / grade group <1 (accuracy = 87 % ) or grade group >1 / grade group ≤1 (accuracy = 91 % ).Conclusions: In-situ Raman spectroscopy combined with mpMRI radiomics features can lead to highly accurate PCa detection for improved in-vivo targeting of biopsy sample collection and radiotherapy seed placement.

SPIE Journal Paper | 1 September 2022 Open Access

Open Access

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 1: Raman spectroscopy fiber-optics system and in situ tissue characterization

Fabien Picot, Roozbeh Shams, Frédérick Dallaire, Guillaume Sheehy, Tran Trang, David Grajales, Mirela Birlea, Dominique Trudel, Cynthia Ménard, Samuel Kadoury, Frédéric Leblond

JBO, Vol. 27, Issue 09, 095003, (September 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.9.095003

KEYWORDS: Raman spectroscopy, Prostate, Cancer, In vivo imaging, Tissues, Principal component analysis, Biopsy, Remote sensing, Navigation systems, Tumors

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Significance: The diagnosis of prostate cancer (PCa) and focal treatment by brachytherapy are limited by the lack of precise intraoperative information to target tumors during biopsy collection and radiation seed placement. Image-guidance techniques could improve the safety and diagnostic yield of biopsy collection as well as increase the efficacy of radiotherapy.Aim: To estimate the accuracy of PCa detection using in situ Raman spectroscopy (RS) in a pilot in-human clinical study and assess biochemical differences between in vivo and ex vivo measurements.Approach: A new miniature RS fiber-optics system equipped with an electromagnetic (EM) tracker was guided by trans-rectal ultrasound-guided imaging, fused with preoperative magnetic resonance imaging to acquire 49 spectra in situ (in vivo) from 18 PCa patients. In addition, 179 spectra were acquired ex vivo in fresh prostate samples from 14 patients who underwent radical prostatectomy. Two machine-learning models were trained to discriminate cancer from normal prostate tissue from both in situ and ex vivo datasets.Results: A support vector machine (SVM) model was trained on the in situ dataset and its performance was evaluated using leave-one-patient-out cross validation from 28 normal prostate measurements and 21 in-tumor measurements. The model performed at 86% sensitivity and 72% specificity. Similarly, an SVM model was trained with the ex vivo dataset from 152 normal prostate measurements and 27 tumor measurements showing reduced cancer detection performance mostly attributable to spatial registration inaccuracies between probe measurements and histology assessment. A qualitative comparison between in situ and ex vivo measurements demonstrated a one-to-one correspondence and similar ratios between the main Raman bands (e.g., amide I-II bands, phenylalanine).Conclusions: PCa detection can be achieved using RS and machine learning models for image-guidance applications using in situ measurements during prostate biopsy procedures.

SPIE Journal Paper | 9 February 2022 Open Access

Open Access

Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning

Katherine Ember, François Daoust, Myriam Mahfoud, Frédérick Dallaire, Esmat Zamani, Trang Tran, Arthur Plante, Mame-Kany Diop, Tien Nguyen, Amélie St-Georges-Robillard, Nassim Ksantini, Julie Lanthier, Antoine Filiatrault, Guillaume Sheehy, Gabriel Beaudoin, Caroline Quach, Dominique Trudel, Frédéric Leblond

JBO, Vol. 27, Issue 02, 025002, (February 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.2.025002

KEYWORDS: Raman spectroscopy, Crystals, Proteins, Statistical modeling, Spectroscopy, Data modeling, Molecules, Statistical analysis, Feature selection, Aluminum

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SPIE Journal Paper | 6 November 2021 Open Access

Open Access

Dimensional reduction based on peak fitting of Raman micro spectroscopy data improves detection of prostate cancer in tissue specimens

Arthur Plante, Frédérick Dallaire, Andrée-Anne Grosset, Tien Nguyen, Mirela Birlea, Jahg Wong, François Daoust, Noémi Roy, André Kougioumoutzakis, Feryel Azzi, Kelly Aubertin, Samuel Kadoury, Mathieu Latour, Roula Albadine, Susan Prendeville, Paul Boutros, Michael Fraser, Rob Bristow, Theodorus Van Der Kwast, Michele Orain, Hervé Brisson, Nazim Benzerdjeb, Hélène Hovington, Alain Bergeron, Yves Fradet, Bernard Têtu, Fred Saad, Dominique Trudel, Frédéric Leblond

JBO, Vol. 26, Issue 11, 116501, (November 2021) https://doi.org/10.1117/12.10.1117/1.JBO.26.11.116501

KEYWORDS: Raman spectroscopy, Cancer, Tissues, Proteins, Tumor growth modeling, Spectroscopy, Micro raman spectroscopy, Prostate, Prostate cancer, Machine learning

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Significance: Prostate cancer is the most common cancer among men. An accurate diagnosis of its severity at detection plays a major role in improving their survival. Recently, machine learning models using biomarkers identified from Raman micro-spectroscopy discriminated intraductal carcinoma of the prostate (IDC-P) from cancer tissue with a ≥85 % detection accuracy and differentiated high-grade prostatic intraepithelial neoplasia (HGPIN) from IDC-P with a ≥97.8 % accuracy. Aim: To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique. Approach: A radial basis function (RBF) kernel support vector machine (SVM) model was trained on Raman spectra of prostate tissue from a 272-patient cohort (Centre hospitalier de l’Université de Montréal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Québec-Université Laval, CHUQc-UL). Two types of engineered features were used. Individual intensity features, i.e., Raman signal intensity measured at particular wavelengths and novel Raman spectra fitted peak features consisting of peak heights and widths. Results: Combining engineered features improved classification performance for the three aforementioned classification tasks. The improvements for IDC-P/cancer classification for the UHN and CHUQc-UL testing sets in accuracy, sensitivity, specificity, and area under the curve (AUC) are (numbers in parenthesis are associated with the CHUQc-UL testing set): +4 % (+8 % ), +7 % (+9 % ), +2 % (6%), +9 (+9) with respect to the current best models. Discrimination between HGPIN and IDC-P was also improved in both testing cohorts: +2.2 % (+1.7 % ), +4.5 % (+3.6 % ), +0 % (+0 % ), +2.3 (+0). While no global improvements were obtained for the normal versus cancer classification task [+0 % (−2 % ), +0 % (−3 % ), +2 % (−2 % ), +4 (+3)], the AUC was improved in both testing sets. Conclusions: Combining individual intensity features and novel Raman fitted peak features, improved the classification performance on two independent and multicenter testing sets in comparison to using only individual intensity features.

SPIE Journal Paper | 10 May 2021 Open Access

Open Access

Special Series Guest Editorial: Artificial Intelligence and Machine Learning in Biomedical Optics

Behrouz Shabestari, Mark Anastasio, Baowei Fei, Frédéric Leblond

JBO, Vol. 26, Issue 05, 052901, (May 2021) https://doi.org/10.1117/12.10.1117/1.JBO.26.5.052901

KEYWORDS: Artificial intelligence, Machine learning, Biomedical optics, Tissues, Raman spectroscopy, Image segmentation, Tissue optics, Imaging spectroscopy, Optical coherence tomography, Integrated optics

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SPIE Journal Paper | 12 February 2021 Open Access

Open Access

Handheld macroscopic Raman spectroscopy imaging instrument for machine-learning-based molecular tissue margins characterization

François Daoust, Tien Nguyen, Patrick Orsini, Jacques Bismuth, Marie-Maude de Denus-Baillargeon, Israel Veilleux, Alexandre Wetter, Philippe McKoy, Isabelle Dicaire, Maroun Massabki, Kevin Petrecca, Frédéric Leblond

JBO, Vol. 26, Issue 02, 022911, (February 2021) https://doi.org/10.1117/12.10.1117/1.JBO.26.2.022911

KEYWORDS: Tissues, Raman spectroscopy, Imaging systems, Imaging spectroscopy, Spatial resolution, Cancer, Surgery, Tumors, Tissue optics, Fiber lasers

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SPIE Journal Paper | 11 November 2020 Open Access

Open Access

Multispectral diffuse reflectance can discriminate blood vessels and bleeding during neurosurgery based on low-frequency hemodynamics

Audrey Laurence, Alain Bouthillier, Manon Robert, Dang Khoa Nguyen, Frédéric Leblond

JBO, Vol. 25, Issue 11, 116003, (November 2020) https://doi.org/10.1117/12.10.1117/1.JBO.25.11.116003

KEYWORDS: Blood vessels, Blood, Image segmentation, Hemodynamics, Reflectivity, Diffuse reflectance spectroscopy, Brain, Absorption, Tissues, Tissue optics

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Significance: The practicality of optical methods detecting tissue optical contrast (absorption, elastic and inelastic scattering, fluorescence) for surgical guidance is limited by interferences from blood pooling and the resulting partial or complete inability to interrogate cortex and blood vessels. Aim: A multispectral diffuse reflectance technique was developed for intraoperative brain imaging of hemodynamic activity to automatically discriminate blood vessels, cortex, and bleeding at the brain surface. Approach: A manual segmentation of blood pooling, cortex, and vessels allowed the identification of a frequency range in hemoglobin concentration variations associated with high optical signal in blood vessels and cortex but not in bleeding. Reflectance spectra were then used to automatically segment areas with and without hemodynamic activity as well as to discriminate blood from cortical areas. Results: The frequency range associated with low-frequency hemodynamics and respiratory rate (0.03 to 0.3 Hz) exhibits the largest differences in signal amplitudes for bleeding, blood vessels, and cortex. A segmentation technique based on simulated reflectance spectra initially allowed discrimination of blood (bleeding and vessels) from cortical tissue. Then, a threshold applied to the low-frequency components from deoxyhemoglobin allowed the segmentation of bleeding from vessels. A study on the minimum acquisition time needed to discriminate all three components determined that ∼25 s was necessary to detect changes in the low-frequency range. Other frequency ranges such as heartbeat (1 to 1.7 Hz) can be used to reduce the acquisition time to few seconds but would necessitate optimizing instrumentation to ensure larger signal-to-noise ratios are achieved. Conclusions: A method based on multispectral reflectance signals and low-frequency hemoglobin concentration changes can be used to distinguish bleeding, blood vessels, and cortex. This could be integrated into fiber optic probes to enhance signal specificity by providing users an indication of whether measurements are corrupted by blood pooling, an important confounding factor in biomedical optics applied to surgery.

SPIE Journal Paper | 27 October 2020 Open Access

Open Access

Experimental validation of a spectroscopic Monte Carlo light transport simulation technique and Raman scattering depth sensing analysis in biological tissue

Alireza Akbarzadeh, Ehsan Edjlali, Guillaume Sheehy, Juliette Selb, Rajeev Agarwal, Jessie Weber, Frédéric Leblond

JBO, Vol. 25, Issue 10, 105002, (October 2020) https://doi.org/10.1117/12.10.1117/1.JBO.25.10.105002

KEYWORDS: Raman spectroscopy, Raman scattering, Scattering, Photons, Absorption, Tissue optics, Luminescence, Monte Carlo methods, Optical properties, Light scattering

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Significance: Raman spectroscopy (RS) applied to surgical guidance is attracting attention among scientists in biomedical optics. Offering a computational platform for studying depth-resolved RS and probing molecular specificity of different tissue layers is of crucial importance to increase the precision of these techniques and facilitate their clinical adoption. Aim: The aim of this work was to present a rigorous analysis of inelastic scattering depth sampling and elucidate the relationship between sensing depth of the Raman effect and optical properties of the tissue under interrogation. Approach: A new Monte Carlo (MC) package was developed to simulate absorption, fluorescence, elastic, and inelastic scattering of light in tissue. The validity of the MC algorithm was demonstrated by comparison with experimental Raman spectra in phantoms of known optical properties using nylon and polydimethylsiloxane as Raman-active compounds. A series of MC simulations were performed to study the effects of optical properties on Raman sensing depth for an imaging geometry consistent with single-point detection using a handheld fiber optics probe system. Results: The MC code was used to estimate the Raman sensing depth of a handheld fiber optics system. For absorption and reduced scattering coefficients of 0.001 and 1 mm − 1, the sensing depth varied from 105 to 225 μm for a range of Raman probabilities from 10 − 6 to 10 − 3. Further, for a realistic Raman probability of 10 − 6, the sensing depth ranged between 10 and 600 μm for the range of absorption coefficients 0.001 to 1.4 mm − 1 and reduced scattering coefficients of 0.5 to 30 mm − 1. Conclusions: A spectroscopic MC light transport simulation platform was developed and validated against experimental measurements in tissue phantoms and used to predict depth sensing in tissue. It is hoped that the current package and reported results provide the research community with an effective simulating tool to improve the development of clinical applications of RS.

SPIE Journal Paper | 1 May 2020 Open Access

Open Access

Rise of Raman spectroscopy in neurosurgery: a review

Damon DePaoli, Émile Lemoine, Katherine Ember, Martin Parent, Michel Prud’homme, Léo Cantin, Kevin Petrecca, Frédéric Leblond, Daniel Côté

JBO, Vol. 25, Issue 05, 050901, (May 2020) https://doi.org/10.1117/12.10.1117/1.JBO.25.5.050901

KEYWORDS: Raman spectroscopy, Tissues, Brain, Tissue optics, Remote sensing, Imaging systems, Chromium, Tumors, Signal processing, Biopsy

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SPIE Journal Paper | 21 April 2020 Open Access

Open Access

Quantitative spectral quality assessment technique validated using intraoperative in vivo Raman spectroscopy measurements

Frédérick Dallaire, Fabien Picot, Jean-Philippe Tremblay, Guillaume Sheehy, Émile Lemoine, Rajeev Agarwal, Samuel Kadoury, Dominique Trudel, Frédéric Lesage, Kevin Petrecca, Frédéric Leblond

JBO, Vol. 25, Issue 04, 040501, (April 2020) https://doi.org/10.1117/12.10.1117/1.JBO.25.4.040501

KEYWORDS: Raman spectroscopy, Tissues, Signal to noise ratio, In vivo imaging, Cancer, Brain, Data acquisition, Luminescence, Tissue optics, Visualization

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Proceedings Article | 1 April 2020 Presentation

Presentation

Data compatibility across point probe Raman spectroscopy systems and its impact on statistical models portability (Conference Presentation)

Fabien Picot, Guillaume Sheehy, Frédéric Dallaire, Jean-Phillipe Tremblay, François Daoust, Émile Lemoine, Layal Chaikho, Théophile Bégin, Kevin Petrecca, Samuel Kadoury, Frédéric Leblond

Proceedings Volume 11362, 113620F (2020) https://doi.org/10.1117/12.2555848

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Proceedings Article | 10 March 2020 Presentation

Presentation

In situ molecular characterization and brain cancer detection using macroscopic spontaneous Raman spectroscopy imaging (Conference Presentation)

Frédéric Leblond

Proceedings Volume 11253, 1125304 (2020) https://doi.org/10.1117/12.2550584

KEYWORDS: Raman spectroscopy, Imaging spectroscopy, Data modeling, Brain cancer, Molecular spectroscopy, Brain, Systems modeling, Tissues, Cancer, Spatial resolution

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Proceedings Article | 9 March 2020 Presentation

Presentation

Large field of view macroscopic Raman line-scanning imaging system for neuro-oncology applications (Conference Presentation)

Francois Daoust, Patrick Orsini, Jacques Bismuth, Marie-Maude de Denus-Baillargeon, Israel Veilleux, Alexandre Wetter, Philippe McKoy, Isabelle Dicaire, Maroun Massabki, Kevin Petrecca, Frédéric Leblond

Proceedings Volume 11236, 112360E (2020) https://doi.org/10.1117/12.2543047

KEYWORDS: Imaging systems, Raman spectroscopy, Neuro-oncology, Spatial resolution, In vivo imaging, Real time imaging, Coherence imaging, Endoscopes, RGB color model, Cameras

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Proceedings Article | 11 July 2019 Paper

Paper

Multimodal imaging platform for surgical guidance during epilepsy surgery

Audrey Laurence, Emile Beaulieu, Dang Nguyen, Alain Bouthillier, Dominique Trudel, Frederic Leblond

Proceedings Volume 11074, 1107425 (2019) https://doi.org/10.1117/12.2527232

KEYWORDS: Epilepsy, Auto-fluorescence imaging, Imaging systems, Surgery, Hemodynamics, Microscopes, Multimodal imaging, Hyperspectral imaging, Optical properties, Luminescence

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SPIE Journal Paper | 23 April 2019 Open Access

Open Access

Interstitial imaging with multiple diffusive reflectance spectroscopy projections for in vivo blood vessels detection during brain needle biopsy procedures

Fabien Picot, Andréanne Goyette, Sami Obaid, Joannie Desroches, Simon Lessard, Marie-André Tremblay, Mathias Strupler, Brian Wilson, Kevin Petrecca, Gilles Soulez, Frédéric Leblond

NPh, Vol. 6, Issue 02, 025003, (April 2019) https://doi.org/10.1117/12.10.1117/1.NPh.6.2.025003

KEYWORDS: Biopsy, Brain, Blood vessels, Neuroimaging, Tissue optics, In vivo imaging, Computed tomography, Tissues, Absorption, Imaging systems

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Proceedings Article | 4 March 2019 Presentation

Presentation

Post-prostatectomy spatial frequency domain imaging for positive margins identification using endogenous tissue fluorescence, absorption and scattering (Conference Presentation)

Emile Beaulieu, Audrey Laurence, Mathieu Latour, Roula Albadine, Dominique Trudel, Frédéric Leblond

Proceedings Volume 10871, 108710B (2019) https://doi.org/10.1117/12.2510067

KEYWORDS: Luminescence, Tissues, Absorption, Scattering, Prostate, Imaging systems, Cancer, Light scattering, Laser scattering, Laser tissue interaction

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Proceedings Article | 4 March 2019 Presentation

Presentation

Wide-field intraoperative Raman imaging technique to guide cytoreductive surgery for advanced stages of ovarian cancer (Conference Presentation)

Sandryne David, Guillaume Sheehy, Kayla Simeone, Jean-Phillipe Tremblay, Fred Saad, Anne-Marie Mes-Masson, Daniel Côté, Frédéric Leblond

Proceedings Volume 10890, 108901O (2019) https://doi.org/10.1117/12.2509773

KEYWORDS: Raman spectroscopy, Ovarian cancer, Surgery, Cancer, Tissues, Imaging systems, Tissue optics, Tumor growth modeling, Remote sensing, Optical inspection

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SPIE Journal Paper | 14 February 2019 Open Access

Open Access

Integration of a Raman spectroscopy system to a robotic-assisted surgical system for real-time tissue characterization during radical prostatectomy procedures

Michael Pinto, Kevin Zorn, Jean-Philippe Tremblay, Joannie Desroches, Frédérick Dallaire, Kelly Aubertin, Eric Marple, Chris Kent, Frederic Leblond, Dominique Trudel, Frederic Lesage

JBO, Vol. 24, Issue 02, 025001, (February 2019) https://doi.org/10.1117/12.10.1117/1.JBO.24.2.025001

KEYWORDS: Raman spectroscopy, Prostate, Robotic systems, Tissue optics, Tissues, Surgery, Spectroscopy, In vivo imaging, Robotics, Prostate cancer

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Proceedings Article | 24 May 2018 Presentation

Presentation

Quantitative subsurface fluorescence imaging enabled by spatial frequency domain imaging for enhanced fluorescence-guided surgery (Conference Presentation)

Mira Sibai, Dennis Wirth, Frédéric Leblond, David Roberts, Brian Wilson

Proceedings Volume 10685, 1068517 (2018) https://doi.org/10.1117/12.2307010

KEYWORDS: Luminescence, Tumors, Tissues, Surgery, Tissue optics, Imaging systems, Optical properties, Hyperspectral imaging, Fluorescence spectroscopy, Modulation

Read Abstract +

Intra-operative fluorescence-guided resection (FGR) enables maximum safe resection of glioma by providing real-time tumor contrast. In its most widely used form, FGR is mediated by the preferential overproduction of the fluorophore protoporphyrinIX (PpIX) in malignant tissue after an oral dose of its precursor 5-Aminolevulinic Acid (ALA)1. ALA-PpIX-FGR has been shown to significantly increase completeness of tumor resection. However, the subjective visual assessment and the variable intrinsic optical attenuation of tissue limit this technique to delineating only high-grade tumors that display strong fluorescence residing at the tissue surface. We have shown that wide-field quantitative assessment by extracting 2D maps of PpIX concentration in the tissue, [PpIX], significantly improves the accuracy in detecting diffuse tumors, thereby potentially extending FGR to patients with low-grade tumors. In this approach, hyperspectral fluorescence imaging is coupled to a custom-built spatial frequency domain imaging (SFDI) system. SFDI enables the recovery of tissue optical properties maps, μ_a and μ_s^'. These are used to correct the fluorescence images. The corrected hyperspectral fluorescence images are then spectrally unmixed to separate true PpIX fluorescence from that of its photoproducts and from autofluorescence. Quantitative fluorescence imaging was validated against the clinically used spectroscopic probe by comparing the recovered optical properties and [PpIX] in vivo of a rat brain tumor model. This quantitative approach was also applied to a near infrared fluorophore ZW-800 on tissue-simulating phantoms. ALA-PpIX-FGR, as it is currently implemented, is inaccessible to infiltrative residuals lying beyond the resection cavity because of the limited penetration depth of the blue excitation light used. This is problematic as these infiltrative tumors are the main cause of reccurence. Enhanced sub-surface tumor detection was shown feasible by exciting PpIX’s secondary absorption peak of 635 nm intra-operatively on patients with various intra-cranial pathologies. However, resolving strong fluorescence of a deep-seated tumor from weak fluorescence of a shallow tumor was not possible. That is because the detected fluorescence intensity is heavily dependent on fluorophore concentration, depth, and fluorophore distribution, while also being convolved with tissue turbidity. The aim of this work, therefore, is to extend quantitative ALA-PpIX-FGR to identify sub-surface tumors by resolving tumor depth from fluorophore concentration. This should assist the surgeon in making an informed decision as for whether to further resect or not. A new quantitative depth imaging method was developed by exploiting SFDI’s depth-encoding capabilities in fluorescence mode. The result is a series of spatially modulated fluorescence images, where the modulation amplitude decays with increasing spatial frequency at a rate dependent on fluorophore depth. After recovering depth, a diffusion-based fluorescent light transport model is applied to extract fluorophore concentration. The algorithm was validated using tissue-simulating phantoms and an ex vivo tissue model indicating that the maximum depth recovered is highly dependent on fluorophore concentration as well as on tissue turbidity. For the [PpIX] and optical property maps relevant for glioma tissue, our quantitative depth fluorescence technique can predict depths up to 9 mm ± 0.4 mm, while recovering [PpIX] with an accuracy of 15% for concentrations as low as 2.5 µg/ml.

Proceedings Article | 5 April 2018 Presentation

Presentation

Integrated Raman biopsy probe for high yield targeted brain cancer biopsies (Conference Presentation)

Joannie Desroches, Michael Jermyn, Eric Marple, Kirk Urmey, Gilles Soulez, Marie-Christine Guiot, Brian Wilson, Kevin Petrecca, Frédéric Leblond

Proceedings Volume 10484, 104840E (2018) https://doi.org/10.1117/12.2290189

KEYWORDS: Biopsy, Tissue optics, Brain, Cancer, Raman spectroscopy, Brain cancer, Tumors, Remote sensing, Computed tomography, Magnetic resonance imaging

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Proceedings Article | 14 March 2018 Presentation

Presentation

Label-free optical spectroscopy techniques for targeted, real time and informed in situ cancer diagnosis (Conference Presentation)

Frédéric Leblond

Proceedings Volume 10478, 1047808 (2018) https://doi.org/10.1117/12.2287890

KEYWORDS: Cancer, Surgery, Oncology, Tissue optics, Luminescence, Optical spectroscopy, Raman spectroscopy, Brain cancer, Prostate cancer, Fiber optics

Read Abstract +

SPIE Journal Paper | 6 March 2018 Open Access

Open Access

Challenges and opportunities in clinical translation of biomedical optical spectroscopy and imaging

Brian Wilson, Michael Jermyn, Frédéric Leblond

JBO, Vol. 23, Issue 03, 030901, (March 2018) https://doi.org/10.1117/12.10.1117/1.JBO.23.3.030901

KEYWORDS: Tissue optics, Clinical trials, Biomedical optics, Tumors, Prototyping, Luminescence, Imaging spectroscopy, Raman spectroscopy, Biopsy, Surgery

Read Abstract +

SPIE Journal Paper | 12 October 2017

Dimension reduction technique using a multilayered descriptor for high-precision classification of ovarian cancer tissue using optical coherence tomography: a feasibility study

Catherine St. Pierre, Wendy-Julie Madore, Étienne De Montigny, Dominique Trudel, Caroline Boudoux, Nicolas Godbout, Anne-Marie Mes-Masson, Kurosh Rahimi, Frédéric Leblond

JMI, Vol. 4, Issue 04, 041306, (October 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.4.041306

KEYWORDS: Optical coherence tomography, Tissues, Image classification, Cancer, Ovarian cancer, Tissue optics, Multilayers, Binary data, Dimension reduction, Image segmentation

Read Abstract +

SPIE Journal Paper | 11 July 2017 Open Access

Open Access

Preclinical evaluation of spatial frequency domain-enabled wide-field quantitative imaging for enhanced glioma resection

Mira Sibai, Carl Fisher, Israel Veilleux, Jonathan Elliott, Frederic Leblond, David Roberts, Brian Wilson

JBO, Vol. 22, Issue 07, 076007, (July 2017) https://doi.org/10.1117/12.10.1117/1.JBO.22.7.076007

KEYWORDS: Tumors, Tissue optics, Spatial frequencies, Fiber optics tests, Fiber optics, Neuroimaging, Brain, Visualization, Visual optics, Signal attenuation

Read Abstract +

Proceedings Article | 2 May 2017 Presentation

Presentation

Raman spectroscopy for prostate cancer detection and characterization (Conference Presentation)

Kelly Aubertin, Quoc-Huy Trinh, Michael Jermyn, Catherine St-Pierre, Maria-Claudia Vladoiu, Andrée-Anne Grosset, Fred Saad, Dominique Trudel, Frédéric Leblond

Proceedings Volume 10038, 100380K (2017) https://doi.org/10.1117/12.2253117

KEYWORDS: Tumors, Laser therapeutics, Prostate cancer, Raman spectroscopy, Prostate, Tissues, Cancer, Microscopes, Machine learning, Collagen

Read Abstract +

Proceedings Article | 1 May 2017 Presentation

Presentation

Taking label-free optical spectroscopy techniques into the operating theatre: biopsy needles and surgical guidance probes (Conference Presentation)

Frédéric Leblond

Proceedings Volume 10060, 100600B (2017) https://doi.org/10.1117/12.2256742

KEYWORDS: Cancer, Biopsy, Optical spectroscopy, Surgery, Raman spectroscopy, Oncology, Tissues, Brain, Prostate, Colorectal cancer

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SPIE Journal Paper | 12 October 2016 Open Access

Open Access

Intraoperative video-rate hemodynamic response assessment in human cortex using snapshot hyperspectral optical imaging

Julien Pichette, Audrey Laurence, Leticia Angulo, Frederic Lesage, Alain Bouthillier, Dang Khoa Nguyen, Frédéric Leblond

NPh, Vol. 3, Issue 04, 045003, (October 2016) https://doi.org/10.1117/12.10.1117/1.NPh.3.4.045003

KEYWORDS: Hemodynamics, Tissue optics, Brain, Absorption, Optical imaging, Cameras, Hyperspectral imaging, Imaging systems, Calibration, Microscopes

Read Abstract +

SPIE Journal Paper | 8 September 2016 Open Access

Open Access

Neural networks improve brain cancer detection with Raman spectroscopy in the presence of operating room light artifacts

Michael Jermyn, Joannie Desroches, Jeanne Mercier, Marie-Andrée Tremblay, Karl St-Arnaud, Marie-Christine Guiot, Kevin Petrecca, Frédéric Leblond

JBO, Vol. 21, Issue 09, 094002, (September 2016) https://doi.org/10.1117/12.10.1117/1.JBO.21.9.094002

KEYWORDS: Raman spectroscopy, Light, Brain, Cancer, Tissue optics, Neurons, Brain cancer, Microscopes, Tissues, Light sources

Read Abstract +

Proceedings Article | 27 April 2016 Presentation

Presentation

Multimodal optical biopsy probe to improve the safety and diagnostic yield of brain needle biopsies (Conference Presentation)

Joannie Desroches, Julien Pichette, Andréanne Goyette, Marie-Andrée Tremblay, Michael Jermyn, Kevin Petrecca, Frédéric Leblond

Proceedings Volume 9698, 96980U (2016) https://doi.org/10.1117/12.2209569

KEYWORDS: Biopsy, Tissue optics, Raman spectroscopy, Brain, Blood vessels, Tissues, Integrated optics, Diagnostics, Yield improvement, Safety

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Proceedings Article | 26 April 2016 Presentation

Presentation

An intraoperative spectroscopic imaging system for quantification of Protoporphyrin IX during glioma surgery (Conference Presentation)

Leticia Angulo-Rodríguez, Audrey Laurence, Michael Jermyn, Guillaume Sheehy, Mira Sibai, Kevin Petrecca, David Roberts, Keith Paulsen, Brian Wilson, Frédéric Leblond

Proceedings Volume 9690, 96900F (2016) https://doi.org/10.1117/12.2213367

KEYWORDS: Imaging systems, Luminescence, Tissue optics, Imaging spectroscopy, Surgery, Cancer, Tissues, Spectroscopy, Brain, Tumors

Read Abstract +

Proceedings Article | 26 April 2016 Presentation

Presentation

Intraoperative brain hemodynamic response assessment with real-time hyperspectral optical imaging (Conference Presentation)

Audrey Laurence, Julien Pichette, Leticia Angulo-Rodríguez, Catherine Saint Pierre, Frédéric Lesage, Alain Bouthillier, Dang Khoa Nguyen, Frédéric Leblond

Proceedings Volume 9690, 96900E (2016) https://doi.org/10.1117/12.2213384

KEYWORDS: Hemodynamics, Brain, Hyperspectral imaging, Imaging systems, Neuroimaging, Optical imaging, Surgery, Mosaic detectors, Brain mapping, Cerebral blood flow

Read Abstract +

Proceedings Article | 9 March 2016 Presentation + Paper

Presentation + Paper

Neural networks improve brain cancer detection with Raman spectroscopy in the presence of light artifacts

Michael Jermyn, Joannie Desroches, Jeanne Mercier, Karl St-Arnaud, Marie-Christine Guiot, Kevin Petrecca, Frederic Leblond

Proceedings Volume 9690, 96900B (2016) https://doi.org/10.1117/12.2208892

KEYWORDS: Cancer, Brain cancer, Raman spectroscopy, Surgery, Neural networks, Brain, Tissue optics, Machine learning, Light, Neurons

Read Abstract +

Proceedings Article | 29 February 2016 Paper

Paper

Spectroscopic imaging system for high-throughput viability assessment of ovarian spheroids or microdissected tumor tissues (MDTs) in a microfluidic chip

A. St-Georges-Robillard, M. Masse, J. Kendall-Dupont, M. Strupler, B. Patra, M. Jermyn, A.-M. Mes-Masson, F. Leblond, T. Gervais

Proceedings Volume 9689, 96894E (2016) https://doi.org/10.1117/12.2211159

KEYWORDS: Medicine, Imaging spectroscopy, Microfluidics, Ovarian cancer, Cancer, Fluorescence spectroscopy, Tumors, Fluorescent markers, Spectral calibration, Tunable filters

Read Abstract +

SPIE Journal Paper | 20 March 2015 Open Access

Open Access

Macroscopic-imaging technique for subsurface quantification of near-infrared markers during surgery

Michael Jermyn, Kolbein Kolste, Julien Pichette, Guillaume Sheehy, Leticia Angulo-Rodríguez, Keith Paulsen, Brian Wilson, Kevin Petrecca, Frederic Leblond

JBO, Vol. 20, Issue 03, 036014, (March 2015) https://doi.org/10.1117/12.10.1117/1.JBO.20.3.036014

KEYWORDS: Luminescence, Tissue optics, Tumors, Tissues, Natural surfaces, Surgery, Reconstruction algorithms, Microscopes, Fluorescence spectroscopy, Fluorescent markers

Read Abstract +

Proceedings Article | 5 March 2015 Paper

Paper

Intraoperative detection of glioma invasion beyond MRI enhancement with Raman spectroscopy in humans

Michael Jermyn, Kelvin Mok, Jeanne Mercier, Joannie Desroches, Julien Pichette, Karl Saint-Arnaud, Marie-Christine Guiot, Kevin Petrecca, Frédéric Leblond

Proceedings Volume 9318, 93180D (2015) https://doi.org/10.1117/12.2075660

KEYWORDS: Cancer, Magnetic resonance imaging, Raman spectroscopy, Tumors, Brain, Tissues, Brain cancer, Surgery, Visible radiation, Visualization

Read Abstract +

Proceedings Article | 4 March 2015 Paper

Paper

Quantitative fluorescence imaging enabled by spatial frequency domain optical-property mapping in the sub-diffusive regime for surgical guidance

Mira Sibai, Israel Veilleux, Jonathan Elliott, Frederic Leblond, David Roberts, Brian Wilson

Proceedings Volume 9311, 93110C (2015) https://doi.org/10.1117/12.2080205

KEYWORDS: Luminescence, Tissue optics, Optical properties, Tumors, Absorption, Diffuse reflectance spectroscopy, Scattering, Tissues, Reflectivity, Spatial frequencies

Read Abstract +

SPIE Journal Paper | 4 February 2015 Open Access

Open Access

Macroscopic optical imaging technique for wide-field estimation of fluorescence depth in optically turbid media for application in brain tumor surgical guidance

Kolbein Kolste, Stephen Kanick, Pablo Valdés, Michael Jermyn, Brian Wilson, David Roberts, Keith Paulsen, Frederic Leblond

JBO, Vol. 20, Issue 02, 026002, (February 2015) https://doi.org/10.1117/12.10.1117/1.JBO.20.2.026002

KEYWORDS: Luminescence, Optical properties, Tissue optics, Error analysis, Tissues, Monte Carlo methods, Tumors, Brain, Scattering, Optical imaging

Read Abstract +

Proceedings Article | 20 March 2014 Paper

Paper

Quantitative spectrally resolved intraoperative fluorescence imaging for neurosurgical guidance in brain tumor surgery: pre-clinical and clinical results

Pablo Valdés, Valerie Jacobs, Frederic Leblond, Brian Wilson, Keith Paulsen, David Roberts

Proceedings Volume 8928, 892809 (2014) https://doi.org/10.1117/12.2039090

KEYWORDS: Luminescence, Tumors, Imaging systems, Tissue optics, Microscopes, Surgery, Signal detection, Brain, Signal attenuation, Tissues

Read Abstract +

Proceedings Article | 25 March 2013 Paper

Paper

An overview of time-domain diffuse fluorescence imaging: instrumentation and applications

Kenneth Tichauer, Robert Holt, Frederic Leblond, Brian Pogue

Proceedings Volume 8578, 857828 (2013) https://doi.org/10.1117/12.2005188

KEYWORDS: Luminescence, Photons, Sensors, Imaging systems, Tissue optics, Light sources, Charge-coupled devices, Dispersion, Signal detection, Pulsed laser operation

Read Abstract +

Proceedings Article | 25 March 2013 Paper

Paper

Optimized methodology for low-contrast fluorescence recovery using a new approach for reference tracer normalization

Robert Holt, Kenneth Tichauer, Fadi El-Ghussein, Jason Gunn, Frederic Leblond, Brian Pogue

Proceedings Volume 8578, 85781W (2013) https://doi.org/10.1117/12.2004829

KEYWORDS: Luminescence, Sensors, Fluorescence tomography, Tumors, Signal detection, Tomography, Image restoration, Liver, Reconstruction algorithms, Data modeling

Read Abstract +

Proceedings Article | 13 March 2013 Paper

Paper

Toward ideal imaging geometry for recovery independence in fluorescence molecular tomography

Robert Holt, Frederic Leblond, Brian Pogue

Proceedings Volume 8574, 857403 (2013) https://doi.org/10.1117/12.2004870

KEYWORDS: Sensors, Fluorescence tomography, Imaging systems, Tomography, Projection systems, Modulation, Luminescence, Optical testing, Fourier transforms, Charge-coupled devices

Read Abstract +

SPIE Journal Paper | 4 January 2013 Open Access

Open Access

Dual-tracer background subtraction approach for fluorescent molecular tomography

Kenneth Tichauer, Robert Holt, Fadi El-Ghussein, Scott Davis, Kimberley Samkoe, Jason Gunn, Frederic Leblond, Brian Pogue

JBO, Vol. 18, Issue 01, 016003, (January 2013) https://doi.org/10.1117/12.10.1117/1.JBO.18.1.016003

KEYWORDS: Luminescence, Tumors, Fourier transforms, Tomography, Image restoration, Imaging systems, In vivo imaging, Optical properties, Data modeling, Tissues

Read Abstract +

SPIE Journal Paper | 4 May 2012 Open Access

Open Access

Spatial frequency domain tomography of protoporphyrin IX fluorescence in preclinical glioma models

Soren Konecky, Tyler Rice, Bruce Tromberg, Chris Owen, Pablo Valdes, David Roberts, Kolbein Kolste, Frederic Leblond, Keith Paulsen, Brian Wilson

JBO, Vol. 17, Issue 5, 056008, (May 2012) https://doi.org/10.1117/12.10.1117/1.JBO.17.5.056008

KEYWORDS: Luminescence, Tumors, Tomography, Tissue optics, Scattering, Skull, Tissues, In vivo imaging, Brain, Optical properties

Read Abstract +

SPIE Journal Paper | 1 November 2011 Open Access

Open Access

Combined fluorescence and reflectance spectroscopy for in vivo quantification of cancer biomarkers in low- and high-grade glioma surgery

Pablo Valdes, Frederic Leblond, Keith Paulsen, Anthony Kim, Brian Wilson, Olga Conde, Brent Harris, David Roberts

JBO, Vol. 16, Issue 11, 116007, (November 2011) https://doi.org/10.1117/12.10.1117/1.3646916

KEYWORDS: Tumors, Luminescence, Tissue optics, Tissues, Diagnostics, Scattering, Algorithm development, Surgery, Absorption, Brain

Read Abstract +

Proceedings Article | 1 March 2011 Paper

Paper

Application of spectral derivative data in spectral near infrared tomography

Hamid Dehghani, Frederic Leblond, Brian Pogue, Fabien Chauchard

Proceedings Volume 7896, 78960I (2011) https://doi.org/10.1117/12.873940

KEYWORDS: Sensors, Near infrared, Tissue optics, Scattering, Tomography, Tissues, Imaging spectroscopy, Calibration, Spectroscopy, Absorption

Read Abstract +

Proceedings Article | 18 February 2011 Paper

Paper

ALA-induced PpIX spectroscopy for brain tumor image-guided surgery

Pablo Valdes, Frederic Leblond, Anthony Kim, Brent Harris, Brian Wilson, Keith Paulsen, David Roberts

Proceedings Volume 7883, 78833P (2011) https://doi.org/10.1117/12.875016

KEYWORDS: Tumors, Luminescence, Tissues, Diagnostics, Brain, Tissue optics, Microscopes, Spectroscopy, Reflectivity, Surgery

Read Abstract +

Proceedings Article | 17 February 2011 Paper

Paper

Development and evaluation of a time-resolved near-infrared fluorescence finite element model

Qun Zhu, Frederic Leblond, Fadi El-Ghussein, Brian Pogue, Hamid Dehghani

Proceedings Volume 7896, 78960T (2011) https://doi.org/10.1117/12.873741

KEYWORDS: Finite element methods, Data modeling, Luminescence, Monte Carlo methods, 3D modeling, Photons, Diffusion, Sensors, Scattering, Absorption

Read Abstract +

Proceedings Article | 17 February 2011 Paper

Paper

Imaging sub-nanomolar concentrations through more than five centimeters of tissue with time-domain diffuse fluorescence tomography?

Kenneth Tichauer, Frederic Leblond, Robert Holt, Fadi El-Ghussein, Brian Pogue

Proceedings Volume 7896, 78962U (2011) https://doi.org/10.1117/12.874801

KEYWORDS: Luminescence, Imaging systems, Optical properties, Fluorescence tomography, Data modeling, Transmittance, Tissues, Fluorescence spectroscopy, Image restoration, Head

Read Abstract +

Proceedings Article | 10 February 2011 Paper

Paper

Acquisition and reconstruction of Raman and fluorescence signals for rat leg imaging

Jennifer-Lynn Demers, Brian Pogue, Frederic Leblond, Francis Esmonde-White, Paul Okagbare, Michael Morris

Proceedings Volume 7892, 789211 (2011) https://doi.org/10.1117/12.881104

KEYWORDS: Raman spectroscopy, Liquids, Bone, Tissues, Calibration, Diffusion, Data acquisition, Luminescence, Animal model studies, Data modeling

Read Abstract +

Proceedings Article | 10 February 2011 Paper

Paper

A hybrid approach combining microCT and fluorescence tomography: imaging workflow and system of coordinate registration

Robert Holt, Fadi El-Ghussein, Kenneth Tichauer, Frederic Leblond, Brian Pogue

Proceedings Volume 7892, 789213 (2011) https://doi.org/10.1117/12.881449

KEYWORDS: Imaging systems, Luminescence, Fluorescence tomography, Image registration, Finite element methods, Software development, Diffusion, Image restoration, Optical phantoms, Binary data

Read Abstract +

SPIE Journal Paper | 1 May 2009 Open Access

Open Access

Errata: Imaging of glioma tumor with endogenous fluorescence tomography

Dax Kepshire, Summer Gibbs-Strauss, Julia O'Hara, Frederic Leblond, Subhadra Srinivasan, Brian Pogue, Michael Hutchins, Niculae Mincu, Mario Khayat, Hamid Dehghani

JBO, Vol. 14, Issue 03, 039802, (May 2009) https://doi.org/10.1117/12.10.1117/1.3157198

KEYWORDS: Tumors, Fluorescence tomography, Medical research, Physics, Roads, Diagnostics, Radiology

SPIE Journal Paper | 1 May 2009 Open Access

Open Access

Imaging of glioma tumor with endogenous fluorescence tomography

Dax Kepshire, Summer Gibbs-Strauss, Julia O'Hara, Michael Hutchins, Niculae Mincu, Frederic Leblond, Mario Khayat, Hamid Dehghani, Subhadra Srinivasan, Brian Pogue

JBO, Vol. 14, Issue 03, 030501, (May 2009) https://doi.org/10.1117/12.10.1117/1.3127202

KEYWORDS: Tumors, Luminescence, Fluorescence tomography, Tomography, Imaging systems, Signal detection, Tissues, Calibration, Fluorescence spectroscopy, Single photon detectors

Read Abstract +

Proceedings Article | 13 March 2009 Paper

Paper

Brain tumor resection guided by fluorescence imaging and MRI image guidance

Pablo Valdes, Brent Harris, Frederic Leblond, Kathryn Fontaine, Songbai Ji, Brian Pogue, Alex Hartov, David Roberts, Keith Paulsen

Proceedings Volume 7261, 726103 (2009) https://doi.org/10.1117/12.813868

KEYWORDS: Magnetic resonance imaging, Luminescence, Tissues, Tumors, Microscopes, Brain, Gadolinium, Surgery, Image analysis, Image enhancement

Read Abstract +

Proceedings Article | 24 February 2009 Paper

Paper

Brain tumor resection guided by fluorescence imaging

Frederic Leblond, Kathryn Fontaine, Pablo Valdes, Songbai Ji, Brian Pogue, Alex Hartov, David Roberts, Keith Paulsen

Proceedings Volume 7164, 71640M (2009) https://doi.org/10.1117/12.808928

KEYWORDS: Tumors, Luminescence, Surgery, Microscopes, Tissues, Brain, Magnetic resonance imaging, Navigation systems, Neuroimaging, Clinical trials

Read Abstract +

Proceedings Article | 20 February 2009 Paper

Paper

Imaging protoporphyrin IX fluorescence with a time-domain FMT/microCT system

Frederic Leblond, Dax Kepshire, Julia O'Hara, Hamid Dehghani, Subha Srinivasan, N. Mincu, M. Hutchins, M. Khayat, B. Pogue

Proceedings Volume 7171, 717106 (2009) https://doi.org/10.1117/12.809267

KEYWORDS: Luminescence, Imaging systems, Signal detection, Sensors, Tomography, Quantum efficiency, Data acquisition, Diffusion, Computed tomography, Natural surfaces

Read Abstract +

Proceedings Article | 21 February 2008 Paper

Paper

Diffuse optical tomography and spectroscopy performance assessment: phantoms and methodology

Niculae Mincu, Jean Brunette, Olga Guilman, Fréderic Leblond, Salim Djeziri, Zahia Ichalalène, Guobin Ma, Mario Khayat, Anader Benyamin-Seeyar

Proceedings Volume 6870, 68700O (2008) https://doi.org/10.1117/12.763638

KEYWORDS: Absorption, Scattering, Particles, Mie scattering, Refractive index, Solids, Optical properties, Liquids, Water, Titanium dioxide

Read Abstract +

Proceedings Article | 20 February 2008 Paper

Paper

Full path Monte Carlo simulation of fluorescence in non-voxelized complex heterogeneous mouse model

Jean-Francois Delorme, Guobin Ma, Frederic Lesage, Frederic Leblond, Anader Benyamin-Seyar

Proceedings Volume 6854, 68541M (2008) https://doi.org/10.1117/12.760822

KEYWORDS: Monte Carlo methods, Scattering, Luminescence, Photon transport, Reflection, 3D modeling, Computer simulations, Refraction, Refractive index, Diffusion

Read Abstract +

Proceedings Article | 12 February 2008 Paper

Paper

Fluorescence lifetime estimation of multiple near-infrared dyes in mice

Guobin Ma, Simon Fortier, Muriel Jean-Jacques, Niculae Mincu, Frederic Leblond, Zahia Ichalalene, Anader Benyamin-Seeyar, Mario Khayat

Proceedings Volume 6850, 685003 (2008) https://doi.org/10.1117/12.763544

KEYWORDS: Luminescence, Tissues, Diffusion, Optical properties, In vivo imaging, Convolution, Biological research, Liquids, Radiative transfer, Fluorescence lifetime imaging

Read Abstract +

Proceedings Article | 12 July 2007 Paper

Paper

Diffuse optical tomography forward model refinements in media with heterogeneous optical properties

Simon Fortier, Frederic Leblond

Proceedings Volume 6629, 662912 (2007) https://doi.org/10.1117/12.727935

KEYWORDS: Data modeling, Optical properties, Luminescence, Sensors, Statistical modeling, Statistical analysis, Absorption, Scattering, Diffuse optical tomography, Tomography

Read Abstract +

Proceedings Article | 27 March 2007 Paper

Paper

Sensitivity and repeatability of diffuse optical tomography: toward breast cancer neoadjuvant treatment monitoring

Niculae Mincu, Salim Djeziri, Zahia Ichalalène, Fréderic Leblond, Mario Khayat

Proceedings Volume 6434, 64341D (2007) https://doi.org/10.1117/12.701094

KEYWORDS: Breast, Absorption, Scattering, Blood, Data acquisition, Diffuse optical tomography, Reconstruction algorithms, Liquids, Breast cancer, Tumors

Read Abstract +

Proceedings Article | 20 February 2007 Paper

Paper

Diffuse optical fluorescence tomography using time-resolved data acquired in transmission

Frederic Leblond, Simon Fortier, Michael Friedlander

Proceedings Volume 6431, 643106 (2007) https://doi.org/10.1117/12.700841

KEYWORDS: Luminescence, Sensors, Tissues, Refraction, Data acquisition, Optical properties, Diffusion, Absorption, Scattering, Animal model studies

Read Abstract +

Proceedings Article | 20 February 2007 Paper

Paper

Optical tomography as adjunct to x-ray mammography: methods and results

Mario Khayat, Zahia Ichalalene, Niculae Mincu, Fredéric Leblond, Olga Guilman, Salim Djeziri

Proceedings Volume 6431, 64310F (2007) https://doi.org/10.1117/12.700960

KEYWORDS: Breast, Mammography, Tissue optics, X-rays, X-ray optics, Statistical analysis, Tissues, Optical imaging, Radio optics, Scattering

Read Abstract +

Proceedings Article | 13 February 2007 Paper

Paper

Improved quantification of fluorescence in 3-D in a realistic mouse phantom

Subhadra Srinivasan, Brian Pogue, Scott Davis, Frederic Leblond

Proceedings Volume 6434, 64340S (2007) https://doi.org/10.1117/12.698636

KEYWORDS: Luminescence, Tumors, Optical properties, Image restoration, Brain, Diffusion, Tissues, Absorption, Sensors, Scattering

Read Abstract +

Proceedings Article | 13 February 2007 Paper

Paper

Numerical optix: a time-domain simulator of fluorescent-light diffusion in turbid medium

Guobin Ma, Jean-François Delorme, Olga Guilman, Frédéric Leblond, Mario Khayat

Proceedings Volume 6434, 64342J (2007) https://doi.org/10.1117/12.701251

KEYWORDS: Luminescence, Monte Carlo methods, Diffusion, Tissues, Sensors, Computer simulations, Reflectivity, Biomedical optics, In vivo imaging, Light sources

Read Abstract +

Showing 5 of 74 publications

Conference Committee Involvement (35)

Optical Tomography and Spectroscopy of Tissue XVI

27 January 2025 | San Francisco, California, United States

Molecular-Guided Surgery: Molecules, Devices, and Applications XI

25 January 2025 | San Francisco, California, United States

Clinical and Translational Neurophotonics 2025

25 January 2025 | San Francisco, California, United States

Clinical Biophotonics III

7 April 2024 | Strasbourg, France

Molecular-Guided Surgery: Molecules, Devices, and Applications X

27 January 2024 | San Francisco, California, United States

Translational Biophotonics: Diagnostics and Therapeutics

25 June 2023 | Munich, Germany

Optical Tomography and Spectroscopy of Tissue XV

30 January 2023 | San Francisco, California, United States

Molecular-Guided Surgery: Molecules, Devices, and Applications IX

28 January 2023 | San Francisco, California, United States

Clinical and Translational Neurophotonics 2023

28 January 2023 | San Francisco, California, United States

Clinical Biophotonics II

4 April 2022 | Strasbourg, France

Molecular-Guided Surgery: Molecules, Devices, and Applications VIII

22 January 2022 | San Francisco, California, United States

Clinical and Translational Neurophotonics 2022

22 January 2022 | San Francisco, California, United States

Translational Biophotonics: Diagnostics and Therapeutics

20 June 2021 | Online Only, Germany

Clinical and Translational Neurophotonics 2021

6 March 2021 | Online Only, California, United States

Molecular-Guided Surgery: Molecules, Devices, and Applications VII

6 March 2021 | Online Only, California, United States

Optical Tomography and Spectroscopy of Tissue XIV

6 March 2021 | Online Only, California, United States

Clinical Biophotonics

6 April 2020 | Online Only, France

Molecular-Guided Surgery: Molecules, Devices, and Applications VI

1 February 2020 | San Francisco, California, United States

Clinical and Translational Neurophotonics 2020

1 February 2020 | San Francisco, California, United States

Optical Tomography and Spectroscopy of Tissue XIII

4 February 2019 | San Francisco, California, United States

Molecular-Guided Surgery: Molecules, Devices, and Applications V

2 February 2019 | San Francisco, California, United States

Clinical and Translational Neurophotonics 2019

2 February 2019 | San Francisco, California, United States

Molecular-Guided Surgery: Molecules, Devices, and Applications IV

27 January 2018 | San Francisco, California, United States

Clinical and Translational Neurophotonics 2018

27 January 2018 | San Francisco, California, United States

Optical Tomography and Spectroscopy of Tissue XII

30 January 2017 | San Francisco, California, United States

Molecular-Guided Surgery: Molecules, Devices, and Applications III

28 January 2017 | San Francisco, California, United States

Clinical and Translational Neurophotonics

28 January 2017 | San Francisco, California, United States

Clinical and Translational Neurophotonics

13 February 2016 | San Francisco, California, United States

Optical Tomography and Spectroscopy of Tissue XI

9 February 2015 | San Francisco, California, United States

Optical Techniques in Neurosurgery, Brain Imaging, and Neurobiology

7 February 2015 | San Francisco, California, United States

Optical Techniques in Neurosurgery, Brain Imaging, and Neurobiology

1 February 2014 | San Francisco, California, United States

Optical Tomography and Spectroscopy of Tissue X

3 February 2013 | San Francisco, California, United States

Optical Techniques in Neurosurgery, Brain Imaging, and Neurobiology

2 February 2013 | San Francisco, California, United States

Optical Techniques in Neurosurgery, Brain Imaging, and Neurobiology

21 January 2012 | San Francisco, California, United States

Optical Techniques in Neurosurgery, Brain Imaging, and Neurobiology

22 January 2011 | San Francisco, California, United States

Showing 5 of 35 Conference Committees

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