Mr. Navid Ibtehaj Nizam Profile

Navid Ibtehaj Nizam

at Rensselaer Polytechnic Institute

SPIE Involvement:

Author

Publications (7)

Proceedings Article | 13 March 2024 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 | 13 March 2024 Presentation

3D fluorescence molecular tomography utilizing a novel SPAD camera

Navid Ibtehaj Nizam, Vikas Pandey, Ismail Erbas, Arin Ulku, Claudio Bruschini, Edoardo Charbon, Xavier Michalet, Xavier Intes

Proceedings Volume PC12834, PC1283407 (2024) https://doi.org/10.1117/12.2692977

KEYWORDS: Fluorescence, Cameras, Fluorescence tomography, Stereoscopic cameras, Imaging systems, Structured light, Fluorescence imaging, Tomography, Reflectivity, Near infrared

Read Abstract +

Proceedings Article | 13 March 2024 Presentation

Transformer based deep learning model for fluorescence lifetime parameters estimation using pixelwise instrument response function

Ismail Erbas, Vikas Pandey, Navid Ibtehaj Nizam, Nanxue Yuan, Xavier Intes

Proceedings Volume PC12834, PC128340C (2024) https://doi.org/10.1117/12.3001941

KEYWORDS: Fluorescence, Deep learning, Transformers, Statistical modeling, Instrument modeling, Equipment, Performance modeling, Mathematical modeling, In vivo imaging, Molecular interactions

Read Abstract +

Proceedings Article | 27 March 2023 Poster

End-to-end reconstruction for mesoscopic fluorescence molecular tomography via deep learning

Shan Gao, Mengzhou Li, Navid Ibtehaj Nizam, Intes Xavier

Proceedings Volume PC12371, PC123710H (2023) https://doi.org/10.1117/12.2650648

KEYWORDS: Luminescence, Tomography, Fluorescence tomography, Inverse problems, 3D modeling, Spatial resolution, Monte Carlo methods, Biomedical optics, 3D image processing, 3D acquisition

Read Abstract +

Proceedings Article | 6 March 2023 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

Read Abstract +

SPIE Journal Paper | 28 April 2022 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

Read Abstract +

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.

DOWNLOAD PAPER

Proceedings Article | 5 March 2021 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

Read Abstract +

Showing 5 of 7 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years