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.

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.