Dr. Jason T. Smith Profile

Dr. Jason T. Smith

SETA at ARPA-H

SPIE Involvement:

Author

Area of Expertise:

biomedical signal processing , molecular imaging , applied machine learning , data simulation , physics , pattern recognition

Websites:

Company Website

Publications (24)

Proceedings Article | 13 March 2024 Poster

Poster

Fluorescence lifetime tomography via deep learning

Navid Ibtehaj Nizam, Vikas Pandey, Jason Smith, Ismail Erbas, Xavier Intes

Proceedings Volume PC12834, PC128340H (2024) https://doi.org/10.1117/12.3000108

KEYWORDS: Fluorescence, Fluorescence tomography, Deep learning, Molecular biology, Fluorescence imaging, Cancer, 3D modeling, Visualization, Tissues, Neural networks

Proceedings Article | 25 April 2023 Paper

Paper

Near infrared fluorescence lifetime FRET microscopy to evaluate antibody drug binding in various HER2 positive cancer cell lines

Catherine Sherry, Amit Verma, Jason Smith, Vikas Pandey, Tynan Young, John Williams, Xavier Michalet, Xavier Intes, Margarida Barroso

Proceedings Volume 12384, 123840P (2023) https://doi.org/10.1117/12.2654748

KEYWORDS: Fluorescence resonance energy transfer, Near infrared, Fluorescence, Fluorescence lifetime imaging, In vitro testing, Fluorophores, Dyes, Cancer, Molecules, Microscopy

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Proceedings Article | 17 March 2023 Presentation

Presentation

FRET in small animal optical imaging: Intensity versus lifetime-based approaches

Jason Smith, Nattawut Sinsuebphon, Xavier Michalet, Xavier Intes, Margarida Barroso

Proceedings Volume 12357, 1235707 (2023) https://doi.org/10.1117/12.2650586

KEYWORDS: Fluorescence resonance energy transfer, In vivo imaging, Optical imaging, Imaging systems, Luminescence, Calibration, Receptors

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Proceedings Article | 17 March 2023 Presentation

Presentation

Impact of tumor microenvironment in antibody-HER2 binding using macroscopy fluorescence lifetime FRET in vivo imaging

Amit Verma, Vikas Pandey, Jason Smith, Xavier Intes, Margarida Barroso

Proceedings Volume 12357, 123570D (2023) https://doi.org/10.1117/12.2650382

KEYWORDS: Tumors, Fluorescence resonance energy transfer, In vivo imaging, Luminescence, Collagen, Resonance energy transfer, Receptors, Ovarian cancer, Near infrared, Multiplexing

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Proceedings Article | 6 March 2023 Presentation + Paper

Presentation + Paper

Micro-CT guided deep neural network for 3D reconstructions in widefield diffuse optical tomography

Navid Ibtehaj Nizam, Marien Ochoa, Jason Smith, Xavier Intes

Proceedings Volume 12371, 1237105 (2023) https://doi.org/10.1117/12.2650090

KEYWORDS: 3D modeling, Diffuse optical tomography, Neural networks, Light sources and illumination, Inverse problems, Spatial resolution, Optical properties, Optical phantoms

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SPIE Journal Paper | 28 April 2022 Open Access

Open Access

Monte Carlo-based data generation for efficient deep learning reconstruction of macroscopic diffuse optical tomography and topography applications

Navid Ibtehaj Nizam, Marien Ochoa, Jason T. Smith, Shan Gao, Xavier Intes

JBO, Vol. 27, Issue 08, 083016, (April 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.8.083016

KEYWORDS: Monte Carlo methods, Data modeling, Luminescence, Computer simulations, Reflectivity, Sensors, In vivo imaging, Absorption, Diffuse optical tomography, 3D modeling

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Significance: Deep learning (DL) models are being increasingly developed to map sensor data to the image domain directly. However, DL methodologies are data-driven and require large and diverse data sets to provide robust and accurate image formation performances. For research modalities such as 2D/3D diffuse optical imaging, the lack of large publicly available data sets and the wide variety of instrumentation designs, data types, and applications leads to unique challenges in obtaining well-controlled data sets for training and validation. Meanwhile, great efforts over the last four decades have focused on developing accurate and computationally efficient light propagation models that are flexible enough to simulate a wide variety of experimental conditions. Aim: Recent developments in Monte Carlo (MC)-based modeling offer the unique advantage of simulating accurately light propagation spatially, temporally, and over an extensive range of optical parameters, including minimally to highly scattering tissue within a computationally efficient platform. Herein, we demonstrate how such MC platforms, namely “Monte Carlo eXtreme” and “Mesh-based Monte Carlo,” can be leveraged to generate large and representative data sets for training the DL model efficiently. Approach: We propose data generator pipeline strategies using these platforms and demonstrate their potential in fluorescence optical topography, fluorescence optical tomography, and single-pixel diffuse optical tomography. These applications represent a large variety in instrumentation design, sample properties, and contrast function. Results: DL models trained using the MC-based in silico datasets, validated further with experimental data not used during training, show accurate and promising results. Conclusion: Overall, these MC-based data generation pipelines are expected to support the development of DL models for rapid, robust, and user-friendly image formation in a wide variety of applications.

Proceedings Article | 7 March 2022 Presentation

Presentation

Multiplexing drug-target engagement and metabolic imaging in HER2-positive tumor xenograft models

Alena Rudkouskaya, Jason Smith, Xavier Intes, Margarida Barroso

Proceedings Volume PC11938, PC1193809 (2022) https://doi.org/10.1117/12.2607308

KEYWORDS: Tumors, Tumor growth modeling, Multiplexing, Fluorescence resonance energy transfer, Systems modeling, Signal detection, Luminescence, Mode conditioning cables, In vivo imaging, Therapeutic antibodies

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Proceedings Article | 7 March 2022 Presentation

Presentation

Antibody-target binding in living tumors using macroscopy fluorescence lifetime imaging

Margarida Barroso, Alena Rudkouskaya, Jason Smith, John Williams, Xavier Intes

Proceedings Volume PC11965, PC119650C (2022) https://doi.org/10.1117/12.2609024

KEYWORDS: Tumors, Fluorescence resonance energy transfer, Near infrared, Fluorescence lifetime imaging, Signal detection, Resonance energy transfer, Microscopy, Imaging systems

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Proceedings Article | 7 March 2022 Presentation

Presentation

Characterization of a large gated SPAD array for in vivo fluorescence lifetime imaging of drug target engagement

Jason Smith, Alena Rudkouskaya, Shan Gao, Arin Ulku, Claudio Bruschini, Edoardo Charbon, Shimon Weiss, Margarida Barroso, Xavier Intes, Xavier Michalet

Proceedings Volume PC11964, PC1196408 (2022) https://doi.org/10.1117/12.2609888

KEYWORDS: In vivo imaging, Fluorescence lifetime imaging, Sensors, In vitro testing, Visible radiation, Tumors, Near infrared, Luminescence, Fluorescence resonance energy transfer, Detector development

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Proceedings Article | 7 March 2022 Presentation

Presentation

Kidney cancer diagnosis using deep learning

Andrew Cheng, Michael Icaza, Nicholas Judd, Jason Smith, Sushmita Mukherjee, Manu Jain, Binlin Wu

Proceedings Volume PC11954, PC119540E (2022) https://doi.org/10.1117/12.2610117

KEYWORDS: Kidney, Tumors, Cancer, Image classification, Visual process modeling, Tumor growth modeling, Data modeling, Receivers, Performance modeling, Multiphoton microscopy

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Proceedings Article | 4 March 2022 Presentation + Paper

Presentation + Paper

NIR fluorescence lifetime macroscopic imaging with a time-gated SPAD camera

X. Michalet, A. Ulku, J. Smith, C. Bruschini, S. Weiss, E. Charbon, X. Intes

Proceedings Volume 11965, 1196507 (2022) https://doi.org/10.1117/12.2607833

KEYWORDS: Signal to noise ratio, Near infrared, Sensors, Fluorescence lifetime imaging, Cameras, Pulsed laser operation, Luminescence, Laser scattering, Data acquisition, Picosecond phenomena

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SPIE Journal Paper | 25 February 2022 Open Access

Open Access

Deep learning in macroscopic diffuse optical imaging

Jason Smith, Marien Ochoa, Denzel Faulkner, Grant Haskins, Xavier Intes

JBO, Vol. 27, Issue 02, 020901, (February 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.2.020901

KEYWORDS: Diffuse optical imaging, Luminescence, Data modeling, Neural networks, Tissue optics, Network architectures, Monte Carlo methods, Tissues, Photons, Tumors

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Proceedings Article | 12 March 2021 Presentation + Paper

Presentation + Paper

Monitoring receptor heterodimerization along intracellular trafficking pathways using anti-HER2 therapeutic antibodies

Alena Rudkouskaya, Jason Smith, Xavier Intes, Margarida Barroso

Proceedings Volume 11624, 116240F (2021) https://doi.org/10.1117/12.2578402

KEYWORDS: Receptors, Therapeutic antibodies, Fluorescence resonance energy transfer, Cancer, Modulation, Luminescence, Switches, Resonance energy transfer, Resistance

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Proceedings Article | 5 March 2021 Presentation

Presentation

Single-pixel hyperspectral macroscopic autofluorescence lifetime imaging and unmixing with deep learning

Marien Ochoa Mendoza, Jason Smith, Xavier Intes

Proceedings Volume 11655, 1165505 (2021) https://doi.org/10.1117/12.2577898

KEYWORDS: In vivo imaging, Auto-fluorescence imaging, Tissues, In vitro testing, Visible radiation, Structured light, Spectrophotometry, Single photon, Sensors, Near infrared

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Proceedings Article | 5 March 2021 Presentation

Presentation

K-space fluorescence tomography in reflectance: a comparison between deep learning and iterative solvers

Navid Ibtehaj Nizam, Marien Ochoa, Jason Smith, Xavier Intes

Proceedings Volume 11634, 116340D (2021) https://doi.org/10.1117/12.2578613

KEYWORDS: Fluorescence tomography, Reflectivity, Diffuse optical tomography, Tumors, Tomography, Resolution enhancement technologies, Reconstruction algorithms, Optical tomography, Monte Carlo methods, Luminescence

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Proceedings Article | 5 March 2021 Presentation

Presentation

Depth-resolved macroscopic fluorescence lifetime imaging improved though spatial frequency domain imaging

Jason Smith, Enagnon Aguénounon, Sylvain Gioux, Xavier Intes

Proceedings Volume 11625, 116250A (2021) https://doi.org/10.1117/12.2578495

KEYWORDS: Fluorescence lifetime imaging, Monte Carlo methods, Scattering, Process modeling, Near infrared, Luminescence, Absorption, Tissues, Photon transport

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Proceedings Article | 9 March 2020 Presentation

Presentation

Monitoring receptor-ligand interactions using fluorescence lifetime FRET imaging via deep learning (Conference Presentation)

Margarida Barroso, Alena Rudkouskaya, Jason Smith, Xavier Intes

Proceedings Volume 11244, 1124419 (2020) https://doi.org/10.1117/12.2546342

KEYWORDS: Luminescence, Fluorescence resonance energy transfer, Near infrared, Visible radiation, Resonance energy transfer, In vitro testing, In vivo imaging, 3D acquisition, 3D image processing, 3D metrology

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Proceedings Article | 19 February 2020 Presentation + Paper

Presentation + Paper

Quantification of anti-HER2 drug uptake into human breast cancer cells using fluorescence lifetime FRET imaging

Alena Rudkouskaya, Jason Smith, Marien Ochoa, Xavier Intes, Margarida Barroso

Proceedings Volume 11219, 112190C (2020) https://doi.org/10.1117/12.2544429

KEYWORDS: Fluorescence resonance energy transfer, Luminescence, Breast cancer, Fluorescence lifetime imaging, Confocal microscopy, Cancer, Near infrared, Microscopy, Plasma, Tumors

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Proceedings Article | 4 March 2019 Presentation

Presentation

Deep learning for quantitative bi-exponential fluorescence lifetime imaging (Conference Presentation)

Jason Smith, Ruoyang Yao, Sez-Jade Chen, Nattawut Sinsuebphon, Alena Rudkouskaya, Margarida Barroso, Pingkun Yan, Xavier Intes

Proceedings Volume 10871, 108710J (2019) https://doi.org/10.1117/12.2509857

KEYWORDS: Fluorescence lifetime imaging, Biomedical optics, Super resolution, Microscopy, Whole body imaging, Resonance energy transfer, Fluorescence resonance energy transfer, Neural networks, Luminescence

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Proceedings Article | 21 February 2018 Paper

Paper

A pilot study for distinguishing chromophobe renal cell carcinoma and oncocytoma using second harmonic generation imaging and convolutional neural network analysis of collagen fibrillar structure

Nicolas Judd, Jason Smith, Manu Jain, Sushmita Mukherjee, Michael Icaza, Ryan Gallagher, Richard Szeligowski, Binlin Wu

Proceedings Volume 10489, 1048919 (2018) https://doi.org/10.1117/12.2288088

KEYWORDS: Tumors, Collagen, Image processing, Neurons, Data modeling, Kidney, Tumor growth modeling, Digital filtering, Image filtering, Second-harmonic generation

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Proceedings Article | 20 February 2018 Paper

Paper

Machine learning based analysis of human prostate cancer cell lines at different metastatic ability using native fluorescence spectroscopy with selective excitation wavelength

Jiangpeng Xue, Yang Pu, Jason Smith, Xin Gao, Binlin Wu

Proceedings Volume 10504, 105040L (2018) https://doi.org/10.1117/12.2281315

KEYWORDS: Principal component analysis, Prostate cancer, Machine learning, Fluorescence spectroscopy

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Proceedings Article | 19 February 2018 Paper

Paper

Statistical analysis and machine learning algorithms for optical biopsy

Binlin Wu, Cheng-hui Liu, Susie Boydston-White, Hugh Beckman, Vidyasagar Sriramoju, Laura Sordillo, Chunyuan Zhang, Lin Zhang, Lingyan Shi, Jason Smith, Jacob Bailin, Robert Alfano

Proceedings Volume 10489, 104890T (2018) https://doi.org/10.1117/12.2288089

KEYWORDS: Tissues, Raman spectroscopy, Machine learning, Biopsy, Principal component analysis, Neurons, Statistical analysis, Diagnostics, Skin, Cancer

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Proceedings Article | 19 February 2018 Presentation + Paper

Presentation + Paper

Characterization and discrimination of human breast cancer and normal breast tissues using resonance Raman spectroscopy

Binlin Wu, Jason Smith, Lin Zhang, Xin Gao, Robert Alfano

Proceedings Volume 10489, 104890X (2018) https://doi.org/10.1117/12.2288094

KEYWORDS: Raman spectroscopy, Tissues, Breast cancer, Principal component analysis, Breast, Diagnostics, Machine learning, Cancer, Resonance enhancement

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Proceedings Article | 13 February 2017 Paper

Paper

Optical biopsy using fluorescence spectroscopy for prostate cancer diagnosis

Binlin Wu, Xin Gao, Jason Smith, Jacob Bailin

Proceedings Volume 10038, 100380U (2017) https://doi.org/10.1117/12.2253517

KEYWORDS: Fluorescence spectroscopy, Tissues, Luminescence, Prostate cancer, Tissue optics, Cancer, Biopsy, Prostate, Collagen, Machine learning

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Showing 5 of 24 publications

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