Accurate quantification of fluorescent targets within turbid media based on a decoupled fluorescence Monte Carlo model

First-order approximation of fluorescence excitation-transmission to accelerate fluorescence molecular tomography image reconstruction

We present a method to accelerate image reconstruction in fluorescence molecular tomography based on the historical path fluorescence Monte Carlo model. The method exploits a first-order approximation expression during the fluorescence excitation-transmission process to merge the path and state information of the photon in a voxel. The experiments show that our method not only greatly reduces the amount of data required for storage in the hard disk and accelerates image reconstruction, but also maintains the quantitative and positioning accuracy of the conventional method.

A fast forward solver of fluorescence diffuse optical tomography based on the lattice Boltzmann method

Fluorescence diffuse optical tomography (FDOT) is a new molecular imaging technology, which uses near-infrared light to excite the fluorophore in tissues. According to the measurements detected on the surface of imaged object, the fluorescent quantum yield as well as lifetime of the fluorescence can be reconstructed. However, the reconstruction of FDOT remains a challenging problem because the conventional forward solvers are time consuming. Thus, a forward model solver that would enable the fast imaging is necessary. This paper describes a new forward solver to simulate the propagation of photons in tissues based on the lattice Boltzmann method (LBM). This is accomplished by propagation photons in tissues guided by the LBM. To evaluate the performance of the proposed LBM, based on the numerical simulation, we compared the light distribution generated by the LBM with the diffusion equation implemented by COMSOL in four different cases. The experimental results indicate that compared to diffusion equation, the LBM can reduce the computation time for the forward solver of FDOT while preserving the similar accuracy.

High-dynamic-range fluorescence molecular tomography for imaging of fluorescent targets with large concentration differences

When CCD-based free-space fluorescence molecular tomography (FMT) is used for imaging of fluorescent targets with a large concentration difference, the limited dynamic range of the CCD diminishes the localization and quantitative accuracy of FMT. To overcome this, we present a high-dynamic-range FMT (HDR-FMT) method. Under the multiple-exposure scheme, HDR fluorescence projection images are constructed using the recovered CCD response curve. Image reconstruction is implemented using iterative reweighted L1 regularization which can reduce artifacts by using fewer HDR fluorescence projection images. Phantom and in vivo animal studies indicate that localization of fluorescent targets with a large concentration difference is effectively improved with HDR-FMT and with good quantitative accuracy.

Accelerating fDOT image reconstruction based on path-history fluorescence Monte Carlo model by using three-level parallel architecture

The excessive time required by fluorescence diffuse optical tomography (fDOT) image reconstruction based on path-history fluorescence Monte Carlo model is its primary limiting factor. Herein, we present a method that accelerates fDOT image reconstruction. We employ three-level parallel architecture including multiple nodes in cluster, multiple cores in central processing unit (CPU), and multiple streaming multiprocessors in graphics processing unit (GPU). Different GPU memories are selectively used, the data-writing time is effectively eliminated, and the data transport per iteration is minimized. Simulation experiments demonstrated that this method can utilize general-purpose computing platforms to efficiently implement and accelerate fDOT image reconstruction, thus providing a practical means of using path-history-based fluorescence Monte Carlo model for fDOT imaging.