Simulations of fluorescence imaging in the oral cavity

Abstract

We describe an end-to-end image systems simulation that models a device capable of measuring fluorescence in the oral cavity. Our software includes a 3D model of the oral cavity and excitation-emission matrices of endogenous fluorophores that predict the spectral radiance of oral mucosal tissue. The predicted radiance is transformed by a model of the optics and image sensor to generate expected sensor image values. We compare simulated and real camera data from tongues in healthy individuals and show that the camera sensor chromaticity values can be used to quantify the fluorescence from porphyrins relative to the bulk fluorescence from multiple fluorophores (elastin, NADH, FAD, and collagen). Validation of the simulations supports the use of soft-prototyping in guiding system design for fluorescence imaging.

Accurate smartphone camera simulation using 3D scenes

“…Prior work had already established the accuracy of linear plus noise sensor models for planar scenes with simple lighting [5], [6]. Image system simulation validation based on 3D scenes with fluorescent materials and sensors were described in [14]. The tests here extend those validations by comparing the R, G and B digital values in simulated and measured camera images of the MCC under multiple light sources and different camera positions.…”

“…The ISETCam and ISET3d software we describe relies on physically based ray tracing that is capable creating quite complex natural scenes [8]. The software can describe radiance in any spectral band, and it can specify a wide variety material properties including fluorescence [14]. We added the capability of modeling arbitrary imaging optics specified by a ray transfer function [39] and thus compute the sensor irradiance.…”

“…For example, we used simulations to develop the L 3 (local, linear and learned) method for processing RGBW sensor data [33]- [36]. We also used simulations to design a spectrally-adaptive imaging system for underwater imaging [37] and a camera that is capable of measuring the fluorescence of oral bacteria [13], [14].…”

“…We have used three-dimensional scenes to design and evaluate imaging systems for a range of applications, including AR/VR [9], underwater imaging [10], autonomous driving [11], [12], fluorescence imaging [13], [14] and models of the human image formation [15]. In each case, we carried out certain system validations -typically by comparing some aspect of the simulation with real data.…”

Validation of image systems simulation technology using a Cornell Box

“…This enables us to calculate the spectral irradiance image of relatively complex, high-dynamic range (HDR) 3D scenes. We have used the methods to design and evaluate imaging systems for a range of applications, including AR/VR [6], underwater imaging [7], autonomous driving [8,9], fluorescence imaging [10,11] and models of the human image formation [12]. In each case, we carried out certain system validations -typically by comparing some aspect of the simulation with real data.…”