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

Abstract

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.

Incomplete angle reconstruction algorithm with the sparse optimization and the image optimal criterions

“…The coefficient matrix is constructed according to the reference. 21 To verify the reconstruction performance of the SO-IOC algorithm proposed in this article, under the influence of incomplete projection angle and the white Gaussian noise, the reconstruction results of the ART algorithm, the Split Bregman (SpBr) algorithm, and the SO-IOC algorithm are compared. The ART algorithm is a classical algorithm in the incomplete angle reconstruction, in which the reconstruction results are obtained by line-by-line correction.…”

Towards high-speed optical measurement of shape and deformation

“…A full domain decomposition (DD) was proposed and parallelized using a hybrid CPU-GPU architecture to accelerate DOT applications in large-sized tissues[170]. Similarly, multiple nodes composed of CPUs and GPUs were used to accelerate the fDOT image reconstruction based on the path-history fluorescence Monte Carlo model[171].Optical diffraction tomography (ODT).The wavy nature of light is used for 3D reconstruction in ODT [172]. Kim et al realized a real-time 3D refractive index (RI) distribution reconstruction algorithm based on ODT by computing the sparse angle illumination on the GPU [173].…”