Regularized Iterative Reconstruction in Tensor Tomography Using Gradient Constraints
نویسندگان
چکیده
This paper investigates the iterative reconstruction of tensor fields in diffusion tensor magnetic resonance imaging (MRI). The gradient constraints on eigenvalue and tensor component images of the diffusion tensor were exploited. A computer-generated phantom was used in order to simulate the diffusion tensor in a cardiac MRI study with a diffusion model that depends on the fiber structure of the myocardium. Computer simulations verify that the regularized methods provide an improved reconstruction of the tensor principal directions. The reconstruction from experimentally acquired data is also presented.
منابع مشابه
Iterative Reconstruction in Diffusion Tensor Tomography Using Total Variation Regularization on Reconstructed Eigenvalue and Tensor Component Images
The regularized iterative reconstruction of the tensor field in diffusion tensor tomography magnetic resonance imaging was investigated. The total variation constraints on eigenvalue and tensor component images of the diffusion tensor were explored. A computer generated phantom was used to simulate the diffusion tensor in a cardiac MRI study in which the diffusion model depended upon the fiber ...
متن کاملA Study of Numerical Algorithms for Regularized Poisson ML Image Reconstruction
In this report we solved a regularized Poisson maximum likelihood (ML) image reconstruction problem, using various numerical methods. Rather than the commonly assumed Gaussian ML formulation, we considered a Poisson ML formulation, which is more accurate in some applications such as low dose computed tomography (CT), and also avoids the problematic log conversion in the Gaussian formulation. Th...
متن کاملA Fast Iterative Shrinkage-Thresholding Algorithm for Electrical Resistance Tomography
Image reconstruction in Electrical Resistance Tomography (ERT) is an ill-posed nonlinear inverse problem. Considering the influence of the sparse measurement data on the quality of the reconstructed image, the l1 regularized least-squares program (l1 regularized LSP), which can be cast as a second order cone programming problem, is introduced to solve the inverse problem in this paper. A normal...
متن کاملShearlet-based regularized reconstruction in region-of-interest computed tomography
Region of interest (ROI) tomography has gained increasing attention in recent years due to its potential to reducing radiation exposure and shortening the scanning time. However, tomographic reconstruction from ROI-focused illumination involves truncated projection data and typically results in higher numerical instability even when the reconstruction problem has unique solution. To address thi...
متن کاملComparing IDREAM as an Iterative Reconstruction Algorithm against In Filtered Back Projection in Computed Tomography
Introduction: Recent studies of Computed Tomography (CT) conducted on patient dose reduction have recommended using an iterative reconstruction algorithm and mA (mili-Ampere) dose modulation. The current study aimed to evaluate Iterative Dose Reduction Algorithm (IDREAM) as an iterative reconstruction algorithm. Material and Methods: Two CT p...
متن کامل