A new algorithm for computing electron microscopy tomograms which combines iterative methods with dual-axis geometry is presented. Initial modelling using test data shows several improvements over both the weighted back-projection (WBP) and Simultaneous Iterative Reconstruction Technique (SIRT) method, and, with increased stability and tomogram fidelity under high-noise conditions.

This is a review paper on some of the physics, modeling, and iterative algorithms in proton computed tomography (pCT) image reconstruction. The primary challenge in pCT image reconstruction lies in the degraded spatial resolution resulting from multiple Coulomb scattering within the imaged object. Analytical models such as the most likely path (MLP) have been proposed to predict the scattered t...

Abstract: This paper addresses a unique method of ISAR dipole model image by employing vector dyadic contrast function technique. The current ISAR imaging algorithms rely upon the assumption that the area under investigation consists of a superposition of infinitesimally small isotropic scatterers (i.e., the point scatterer model). This approximation fails to capture the true real-world scatter...

Computed Tomography (CT) scanner technology has progressed rapidly throughout the past decade, with major advances in CT x-ray detection, system speed, and image reconstruction that have resulted in a stillincreasing number of novel CT clinical applications. Concomitant to this increase in clinical applications, CT technology has also experienced innovation to support the realization of high CT...

Regularization methods are commonly used in X-ray CT image reconstruction. Different regularization methods reflect the characterization of different prior knowledge of images. In a recent work, a new regularization method called a low-dimensional manifold model (LDMM) is investigated to characterize the low-dimensional patch manifold structure of natural images, where the manifold dimensionali...

Algebraic iterative methods are routinely used for solving the ill-posed sparse linear systems arising in tomographic image reconstruction. Here we consider the Algebraic Reconstruction Techniques (ART) and the Simultaneous Iterative Reconstruction Techniques (SIRT), both of which rely on semi-convergence. Block versions of these methods, based on a partitioning of the linear system, are able t...

We have developed, implemented and validated a set of flexible and efficient iterative image reconstruction (IIR) and processing methods for clinical positron emission tomography (PET) static or dynamic studies. IIR techniques are shown to produce images of better contrast and signal-to-noise ratio than the conventional filtered back-projection (FBP). Their high computational cost has been a si...

Image reconstruction is a key component in many medical imaging modalities. The problem of image reconstruction can be viewed as a special inverse problem where the unknown image pixel intensities are estimated from the observed measurements. Since the measurements are usually noise contaminated, statistical reconstruction methods are preferred. In this paper we review some non-negatively const...

Objective (s): Various iterative reconstruction algorithms in nuclear medicine have been introduced in the last three decades. For each new imaging system, it is wise to select appropriate image reconstruction algorithms and evaluate their performance. In this study, three approaches of image reconstruction were developed for a novel desktop open-gantry SPECT system, PERSPECT, to assess their p...

Algebraic reconstruction techniques (ARTs), on both their successive and simultaneous formulations, have been developed since the early 1970s as efficient ‘row-action methods’for solving the image-reconstruction problem in computerized tomography. In this respect, two important development directions were concerned with, first, their extension to the inconsistent case of the reconstruction prob...