Time-domain Fluorescence Molecular Tomography Research Articles

Encoder-decoder deep learning network for simultaneous reconstruction of fluorescence yield and lifetime distributions.

A time-domain fluorescence molecular tomography in reflective geometry (TD-rFMT) has been proposed to circumvent the penetration limit and reconstruct fluorescence distribution within a 2.5-cm depth regardless of the object size. In this paper, an end-to-end encoder-decoder network is proposed to further enhance the reconstruction performance of TD-rFMT. The network reconstructs both the fluorescence yield and lifetime distributions directly from the time-resolved fluorescent signals. According to the properties of TD-rFMT, proper noise was added to the simulation training data and a customized loss function was adopted for self-supervised and supervised joint training. Simulations and phantom experiments demonstrate that the proposed network can significantly improve the spatial resolution, positioning accuracy, and accuracy of lifetime values.

L(p) regularization for early gate fluorescence molecular tomography.

Time domain fluorescence molecular tomography (TD-FMT) provides a unique dataset for enhanced quantification and spatial resolution. The time-gate dataset can be divided into two temporal groups around the maximum counts gate, which are early gates and late gates. It is well established that early gates allow for improved spatial resolution and late gates are essential for fluorophore unmixing and concentration quantification. However, the inverse problem of FMT is ill-posed and typically underdetermined, which makes image reconstruction highly susceptible to data noise. More specifically, photon counts are inherently very low at early gates due to high absorption and scattering of tissue, resulting in a low signal-to-noise ratio and unstable reconstructions. In this work, an L(p) regularization-based reconstruction algorithm was developed and tested with our wide-field mesh-based Monte Carlo simulation strategy. We compared the early time-gate reconstructions obtained with the different p (p∈{1/16,1/8,1/4,1/3,1/2,1,2}) from a synthetic murine model simulating the fluorophore uptake in the kidneys and preclinical data. The results from a 3D mouse atlas and a mouse experiment show that our L(1/4) regularization methods give the best performance for early time gates reconstructions.

Three-dimensional scheme for time-domain fluorescence molecular tomography based on Laplace transforms with noise-robust factors

As a visualizing and quantitative method, Fluorescence Molecular Tomography (FMT) has many potential applications in biomedical field and its three-dimensional (3D) implementation is needed in both theory and practice. In this paper, we propose a 3D scheme for time-domain FMT within the normalized Born-ratio formulation. A finite element method solution to the Laplace transformed time-domain coupled diffusion equations is employed as the forward model, and the resultant linear inversions at two distinct transform-factors are solved with an algebraic reconstruction technique to separate fluorescent yield and lifetime images. The algorithm is validated using simulated data for 3D cylinder phantoms, and the spatial resolution and quantitativeness of the reconstruction assessed. We demonstrate that the proposed approach can accurately retrieve the positions and shapes of the targets with high spatial resolution and quantitative accuracy, and tolerate a signal-to-noise ratio down to 25dB by appropriately choosing the transform factors.

A linear, featured-data scheme for image reconstruction in time-domain fluorescence molecular tomography

Fluorescence diffuse optical tomography (DOT) has attracted many attentions from the community of biomedical imaging, since it provides effective enhancement in imaging contrast. This modality is now rapidly evolving as a potential means of monitoring molecular events in small living organisms with help of molecule-specific contrast agents, referred to as fluorescence molecular tomography (FMT). FMT could greatly promote pathogenesis research, drug development, and therapeutic intervention. Although FMT in steady-state and frequency-domain modes have been heavily investigated, the extension to time-domain scheme is imminent for its several unique advantages over the others. By extending the previously developed generalized pulse spectrum technique for time-domain DOT, we propose a linear, featured-data image reconstruction algorithm for time-domain FMT that can simultaneously reconstruct both fluorescent yield and lifetime images of multiple fluorophores, and validate the methodology with simulated data.