Development of tomographic reconstruction for three-dimensional optical imaging:From the inversion of light propagation to artificial intelligence

Optical molecular tomography(OMT)is an imaging modality which uses an optical signal,especially near-infrared light,to reconstruct the three-dimensional information of the light source in biological tissue.With the advantages of being low-cost,noninvasive and having high sensitivity,OMT has been applied in preclinical and clinical research.However,due to its serious ill-posedness and illcondition,the solution of OMT requires heavy data analysis and the reconstruction quality is limited.Recently,the artificial intelligence(commonly known as AI)-based methods have been proposed to provide a different tool to solve the OMT problem.In this paper,we review the progress on OMT algorithms,from conventional methods to AI-based methods,and we also give a prospective towards future developments in this domain.

An efficient regulation approach for tomographic reconstruction in combustion diagnostics based on TDLAS method

For tomographic reconstruction in combustion diagnostics,it is usually necessary to solve a rank-deficient problem,where the number of non-linear dependent equations is smaller than the number of unknowns.In some reconstructions,there are grids without rays passing through.This produces artifacts during the reconstruction.In this paper,the weight of the regulation equation is modified with the number of the rays crossing the grid cells.The effect of the neighboring grid values as well as the number of rays crossing the grid cells is considered in the new regulation method.Numerical simulation results show that the new regulation method suppresses the reconstruction error of the no rays crossing grid and successfully restrains the corner distortion in four projection angles.The effects of the weight coefficient and the smoothing factor on the reconstruction are examined through a numerical study.Finally,a combustion experiment demonstrates that the new regulation method can significantly reduce the reconstructed error,especially for the nonray crossing condition,and the results are compared with thermocouple measurements and reconstructions without modified regulation.

Reconstruction of Tomographic Images from a Few Views by New Maximum Entropy Approach

A new algorithm for the reconstruction of tomographic images from a few views is presented. A variable metric method is used to solve the unconstrained optimization problem which resulted from the analysis by use of the maximum ent ropy formalism. The numerical simulation is used to study the reconstruction eff ect on the different asymmetric functions. The results show that the reconstruct ion accuracy is satisfactory.

3D Reconstruction Technique for Tomographic PIV

Tomographic particle image velocimetry(Tomo-PIV) is a state-of-the-art experimental technique based on a method of optical tomography to achieve the three-dimensional(3D) reconstruction for threedimensional three-component(3D-3C) flow velocity measurements. 3D reconstruction for Tomo-PIV is carried out herein. Meanwhile, a 3D simplified tomographic reconstruction model reduced from a 3D volume light intensity field with 2D projection images into a 2D Tomo-slice plane with 1D projecting lines, i.e., simplifying this 3D reconstruction into a problem of 2D Tomo-slice plane reconstruction, is applied thereafter. Two kinds of the most well-known algebraic reconstruction techniques, algebraic reconstruction technique(ART) and multiple algebraic reconstruction technique(MART), are compared as well. The principles of the two reconstruction algorithms are discussed in detail, which has been performed by a series of simulation images, yielding the corresponding reconstruction images that show different features between the ART and MART algorithm, and then their advantages and disadvantages are discussed. Further discussions are made for the standard particle image reconstruction when the background noise of the pre-initial particle image has been removed. Results show that the particle image reconstruction has been greatly improved. The MART algorithm is much better than the ART. Furthermore, the computational analyses of two parameters(the particle density and the number of cameras), are performed to study their effects on the reconstruction. Lastly, the 3D volume particle field is reconstructed by using the improved algorithm based on the simplified 3D tomographic reconstruction model, which proves that the algorithm simplification is feasible and it can be applied to the reconstruction of 3D volume particle field in a Tomo-PIV system.

Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors

The state-of-the-art approaches for image reconstruction using under-sampled k-space data are compressed sensing based.They are iterative algorithms that optimize objective functions with spatial and/or temporal constraints.This paper proposes a non-iterative algorithm to estimate the un-measured data and then to reconstruct the image with the efficient filtered backprojection algorithm.The feasibility of the proposed method is demonstrated with a patient magnetic resonance imaging study.The proposed method is also compared with the state-of-the-art iterative compressed-sensing image reconstruction method using the total-variation optimization norm.

Geometric matrix research for nuclear waste drum tomographic gamma scanning transmission image reconstruction

A geometric matrix model of nuclear waste drums is proposed for transmission image reconstruction from tomographic gamma scans（TGS）. The model assumes that rays are conical, with intensity uniformly distributed within the cone. The attenuation coefficients are centered on the voxel（cube） of the geometric center. The proposed model is verified using the EM algorithm and compared to previously reported models. The calculated results show that the model can obtain good reconstruction results even when the sample models are highly heterogeneous.

OCTOPOD:single-bunch tomography for angular-spectral characterization of laser-driven protons

Laser-plasma accelerated(LPA)proton bunches are now applied for research fields ranging from ultra-high-dose-rate radiobiology to material science.Yet,the capabilities to characterize the spectrally and angularly broad LPA bunches lag behind the rapidly evolving applications.The OCTOPOD translates the angularly resolved spectral characterization of LPA proton bunches into the spatially resolved detection of the volumetric dose distribution deposited in a liquid scintillator.Up to 24 multi-pinhole arrays record projections of the scintillation light distribution and allow for tomographic reconstruction of the volumetric dose deposition pattern,from which proton spectra may be retrieved.Applying the OCTOPOD at a cyclotron,we show the reliable retrieval of various spatial dose deposition patterns and detector sensitivity over a broad dose range.Moreover,the OCTOPOD was installed at an LPA proton source,providing real-time data on proton acceleration performance and attesting the system optimal performance in the harsh laser-plasma environment.