Comparison of Monte Carlo methods for fluorescence molecular tomography-computational efficiency

Abstract

Among the three Monte Carlo methods, the mMC method is a computationally prohibitive technique that is not well suited for time-domain fluorescence tomography applications. The pMC method is advantageous over the aMC method when the early gates are employed and large number of detectors is present. Alternatively, the aMC method is the method of choice when a small number of source-detector pairs are used.

Adaptive wide-field optical tomography

“…The timegated Jacobians are computed using the perturbative MC technique, which is the most computationally efficient when large number of detectors are considered. 19 The inverse problem is solved using a least-squares solver, lsqr (MATLAB, Natick, Massachusetts). The solver was stopped after 200 iterations for all studies herein.…”

Assessing the imaging performance of light sheet microscopies in highly scattering tissues

“…To accurately and efficiently model fluorescence detection, we employed the widely used adjoint method [35][36][37], in which the illumination and collection beams are both treated as illumination beams, and the product of the simulated illumination beam irradiance profile, S i , and collection beam profile, S c , at every image voxel yields the point spread function, S i,c (PSF) of the microscope in the presence of scattering. For each simulation, we recorded the response functions in a 100 × 100 × 100 μm 3 scoring volume, with a voxel size of 0.25 × 0.25 × 0.25 μm 3 and 10 6 total rays.…”

Decoupled fluorescence Monte Carlo model for direct computation of fluorescence in turbid media

“…The pfMC model is more accurate than the afMC model in media with a high heterogeneity and scattering if there are sufficient photons for the MC simulation, because the assumption of the equivalence between the sources and detectors decreases the accuracy of the afMC model, restricting the model's application in free-space fluorescence molecular tomography. 29 The efficiency of the afMC model is also very low in fluorescence diffuse optical tomography (FDOT) if there are a large number of detectors, equal to the number of MC simulations required. 29 On the basis of integral forms of the transport equations for a fluorescence MC model, we present a decoupled fluorescence MC (dfMC) model for the direct computation of the fluorescence in turbid media.…”

“…29 The efficiency of the afMC model is also very low in fluorescence diffuse optical tomography (FDOT) if there are a large number of detectors, equal to the number of MC simulations required. 29 On the basis of integral forms of the transport equations for a fluorescence MC model, we present a decoupled fluorescence MC (dfMC) model for the direct computation of the fluorescence in turbid media. By decoupling the excitationto-emission conversion and transport process of the fluorescence from the path probability density function and associating the corresponding parameters involving the fluorescence process with the weight function, the sampling path of the fluorescence photons becomes identical to that of the excitation light, and the fluorescence statistical quantities are unbiased.…”

“…Especially for fluorescence-based in vivo tomography, changing any of the input parameters requires a new simulation, resulting in a large computational load. To overcome this problem, the adjoint fluorescence MC (afMC) 27,29 model and the perturbation fluorescence MC (pfMC) model 25,[29][30][31][32][33] are developed as fluorescence MC techniques that are more feasible for application in fluorescence-based tomography. The afMC model is built on the basis of the Born approximation and assumes that the source and detector are interchangeable, i.e., equivalent, which is difficult to satisfy for a free-space configuration because of the large difference between the light sources and photodetectors, i.e., pixels on a charge-coupled device (CCD).…”