Fluorescence molecular tomography (FMT), as well as mesoscopic FMT (MFMT) is widely employed to investigate molecular level processes ex vivo or in vivo. However, acquiring depth-localized and less blurry reconstruction still remains challenging, especially when fluorophore (dye) is located within large scattering coefficient media. Herein, a two-stage deep learning-based three-dimensional (3-D) reconstruction algorithm is proposed. The key point for the proposed algorithm is to employ a 3-D convolutional neural network to correctly predict the boundary of reconstructions, leading refined results. Compared with conventional algorithm, in silico experiments show that relative volume and absolute centroid error reduce over ∼50 % whereas intersection over union increases over 15% for most situations. These results preliminarily indicate the promising future of appropriately applying machine learning (deep learning)-based methods in MFMT.
Upconversion nanoparticles (UCNPs) have the unique ability to emit multiple colors upon excitation by near-infrared (NIR) light. Herein, we investigate the potential use of UCNPs as contrast agents for dental optical tomography, with a focus on monitoring the status of fillings after dental restoration. The potential of performing tomographic imaging using UCNP emission of visible or NIR light is established. This in silico and ex vivo study paves the way toward employing UCNPs as theranostic agents for dental applications.
Optical tomography has a wide range of biomedical applications. Accurate prediction of photon transport in media is critical, as it directly affects the accuracy of the reconstructions. The radiative transfer equation (RTE) is the most accurate deterministic forward model, yet it has not been widely employed in practice due to the challenges in robust and efficient numerical implementations in high dimensions. Herein, we propose a method that combines the discrete ordinate method (DOM) with a streamline diffusion modified continuous Galerkin method to numerically solve RTE. Additionally, a phase function normalization technique was employed to dramatically reduce the instability of the DOM with fewer discrete angular points. To illustrate the accuracy and robustness of our method, the computed solutions to RTE were compared with Monte Carlo (MC) simulations when two types of sources (ideal pencil beam and Gaussian beam) and multiple optical properties were tested. Results show that with standard optical properties of human tissue, photon densities obtained using RTE are, on average, around 5% of those predicted by MC simulations in the entire/deeper region. These results suggest that this implementation of the finite element method-RTE is an accurate forward model for optical tomography in human tissues.
Dental lesions located in the pulp are quite difficult to identify based on anatomical contrast, and, hence, to diagnose
using traditional imaging methods such as dental CT. However, such lesions could lead to functional and/or molecular
optical contrast. Herein, we report on the preliminary investigation of using Laminar Optical Tomography (LOT) to
image the pulp and root canals in teeth. LOT is a non-contact, high resolution, molecular and functional mesoscopic
optical imaging modality. To investigate the potential of LOT for dental imaging, we injected an optical dye into ex vivo
teeth samples and imaged them using LOT and micro-CT simultaneously. A rigid image registration between the LOT
and micro-CT reconstruction was obtained, validating the potential of LOT to image molecular optical contrast deep in
the teeth with accuracy, non-invasively. We demonstrate that LOT can retrieve the 3D bio-distribution of molecular
probes at depths up to 2mm with a resolution of several hundred microns in teeth.