# Simulated Near Infrared Database for Fluorescence Diffuse Optical Tomography

Martin Rodriguez-Vega, Pascal Nguyen, Anabela Da Silva, Julien Wojak, Veronique Josserand, Maxime Henry

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Martin Rodriguez-Vega, Pascal Nguyen, Anabela Da Silva, Julien Wojak, Veronique Josserand, et al. Simulated Near Infrared Database for Fluorescence Diffuse Optical Tomography. European Conference on Biomedical Optics, OPTICA/SPIE, Jun 2025, Munich (Allemagne), Germany. hal-05194772

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Simulated Near Infrared Database for
Fluorescence Diffuse Optical Tomography

Martin Rodriguez-Vega¹,*, Pascal Nguyen¹,², Anabela da Silva¹, Julien Wojak¹,
Veronique Josserand³ and Maxime Henry³

¹ Aix Marseille Univ, CNRS, Centrale Med, Institut Fresnel, Marseille, France

² Kaer Labs, Nantes, France

³ Institute for Advanced Biosciences Université Grenoble Alpes, 38700 La Tronche, France

*rodriguez-vega@fresnel.fr

**Abstract:** This paper presents a methodology for constructing a simulated fluorescence diffuse optical tomography database. It details dataset generation, including sample geometry, optical properties, forward solvers, and data processing. The database ensures reproducibility and supports AI applications in biomedical imaging. © 2025 The Author(s)

# 1. Introduction

Fluorescence Diffuse Optical Tomography (fDOT) is a valuable tool for non-invasive imaging of biological samples [1]. While most research has focused on the Near-Infrared (NIR) window between 700 and 900 nm (NIR-I) due to lower absorption, recent advances have shifted the researchers' attention toward the NIR-II, wavelengths between 900 and 1800 nm, due to lower scattering and therefore higher penetration depth and spatial resolution [2]. However, many challenges remain, such as the development of data collection/processing techniques and mathematical reconstruction methods. Open databases and reproducible simulations are invaluable for research, as they allow the evaluation of different proposed methods and ideas within the same constraints, helping to reduce bias. Furthermore, Artificial Intelligence (AI) has been recognized to be an important emerging area in fDOT applications [3], which requires large amounts of data for training and validation.

In this work, we present the methodology for the construction of a large simulated database of fDOT experiments, with a special interest in the NIR-I towards NIR-II windows. The code for generating the database will become available to the scientific community (https://gitlab.fresnel.fr/rodriguez-vega/tomography_forward) to ensure reproducibility of the results, and that different teams could contribute their work as a communal effort. The database framework and fundamental works are designed to be easily expanded, due to a modular design and standardized data formats.

# 2. Methodology

The dataset is designed to replicate the physical setup shown in Fig. 1, where a laser positioned at the bottom moves in the xy plane, illuminating a sample, and a detector (camera) at the top measures emitted light at different wavelengths. Optical filters separate laser excitation and fluorescence signals. Data is organized into experiments

<figure><img src="image_2.png" /><figcaption>Fig. 1. Simulated tomograph setup, with sample laser configuration, and simulated image at detector.</figcaption></figure>
involving a sample. Each experiment consists of laser positions $(x_i, y_i)$, distributed within a Region of Interest (ROI) defining the probing domain. For each laser position, one or more acquisitions are performed which can be of two types: 1) *excitation*, the light source is the laser; 2) *fluorescence*, the light source are the fluorophores. Fluorescence acquisitions require a prior excitation to determine the emissivity of fluorophore regions.

The database is generated through independent modules, each defining specific parameters of the fDOT system, which are described in the following subsections. For each module, metadata is generated which is then linked to the simulation outputs.

## 2.1. Geometry

The sample is discretized into small volume elements called voxels, forming a 3D matrix of integer assigning a tissue label, where the value 0 corresponds to air. We use as a base geometry the Digimouse mouse atlas [4], which has a homogeneous voxel size of 0.1mm, and has 21 tissue labels including: skin, skeleton, heart, brain, bladder, testis, stomach, spleen, pancreas, liver, kidneys, lungs, and others. Fluorophores are introduced by modifying the label of specific sets of voxels. Several fluorophore configurations are modeled including introduced capillaries (rectal, pulmonary, ventral subcutaneous), and tumors placed in organs such as the liver and lungs. For extra variability, tumor scenarios were divided into the cases of 1) single large tumor in the interior, 2) several small tumors in the interior, and 3) one tumor in the surface.

## 2.2. Optical properties

To model realistic optical properties across a wide wavelength range, we used the framework proposed in [5]. The scattering coefficient is derived using Rayleigh and Mie theory with fitted parameters, while the absorption coefficient $\mu_a$ is computed based on the tissue's primary constituents. We currently consider six main constituents: water, oxy- and deoxyhemoglobin [6], fat [7], bile [6], and bone [8, 9]. The composition of organ tissue were obtained from [5, 10], or given by the average body values if no data was available. Figure 2 shows the optical properties for a selection of tissues. This data serves as a base for constructing sets of tissue properties. To account for the uncertainties of the substance spectra and tissue composition – and to increase database variability – each tissue's base optical properties are randomly offset by up to 20%.

## 2.3. Forward problem solvers

The database metadata includes information about the light propagation model, solver type, and solver parameters. The primary model used is the Radiative Transfer Equation (RTE) [11], solved using the Monte Carlo eXtreme (MCX) [12] open-source photon simulation software, which outputs the energy density for each voxel, and the position, direction, and energy of individual photons that cross the sample's surface. Additionally, we developed a parallelized finite-difference implementation of the Diffusion Approximation (DA) [11]. This solver offers advantages in speed and noise reduction but is only valid in highly scattering media ($\mu_s \gg \mu_a$). Future work aims to incorporate finite-volume solvers for the RTE.

## 2.4. Data processing and storage

Two types of results are stored: raw outputs and detector data. The former are the solutions given by the forward problem solver to the light propagation model in the 3D object, which typically includes the energy density at each voxel, and photon statistics at the sample's boundary. This type of data contains a lot of information but is unmeasurable in practice. On the other hand, detector data consists of images that simulate the signal that would

<figure><img src="image_3.png" /><figcaption>Fig. 2. Optical properties in the NIR for selected mouse tissues.</figcaption></figure>
actually be measured by the device. To generate this type of data, it is necessary to process the raw outputs using
an imaging model, that only uses the information at the surface of the sample.

The detector model is independent of the sample's geometry, and is parametrized by its distance from the sample, and its half-angle aperture (view cone). Two methods are proposed to generate the images depending of the type of raw data. If the energy density is used, the intensity of each pixel is determined by the contributions of the surface elements inside of the view cone, weighted by the surface's normal vector and the distance to the pixel. If photon information is used, each one is projected towards the detector plane using its direction, and then the intensity of a pixel is determined by integrating all photons that are within its boundaries, and direction constraints.

Results are stored in standardized formats with annotated metadata, while an SQL database facilitates experi-
ment retrieval and data selection.

3. Preliminary results and perspectives

For the first simulation round, 75 samples were generated using 19 geometries and 6 sets of optical properties. Each experiments includes seven acquisitions, one excitation at 808 nm, and six fluorescence measurements at 900, 1050, 1100, 1200, 1300, and 1400 nm. ROIs are centered at the centroid of fluorophore distributions, with at least a 1 cm buffer, yielding scan areas of approximately 3 × 3 cm. Source positions were distributed in a uniform grid with a spacing of 2 mm. This configuration mimics an existing experimental setup, enabling simulation validation with real data.

As of January 2025 and since October 2024, approximately 45% of samples in the first simulation round have been completed, amounting to 16 TB of data. This dataset has already proven useful for the development of fluorophore location reconstruction algorithms, with results to be published in future work. Further simulations will incorporate additional geometries, including simple shapes for calibration and quantitative validation.

4. Conclusion

We have introduced a modular framework for generating a large-scale simulated fDOT dataset, with a focus on NIR-I and NIR-II imaging. By ensuring reproducibility and accessibility, this initiative aims to support the broader research community in advancing fDOT techniques, particularly in AI-driven applications.

Acknowledgments

This work received support from the french government under the France 2030 investment plan, as part of the Ini-
tiative d'Excellence d'Aix-Marseille Université – A*MIDEX (AMX-20-TRA-053). This work was partly funded
by France Life Imaging (grant ANR-11-INBS-0006).

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