Background: At present, in nuclear medicine to calculate the cumulative activity of various organs, different techniques are being used to correct the background radiations in the conjugate view method. Each of these techniques considers the effect and weight of background radiations to a different extent. These background radiations can cause errors in cumulative activity calculations. The pur...

Conjugate gradient methods have been paid attention to, because they can be directly applied to large-scale unconstrained optimization problems. In order to incorporate second order information of the objective function into conjugate gradient methods, Dai and Liao (2001) proposed a conjugate gradient method based on the secant condition. However, their method does not necessarily generate a de...

The Conjugate Gradient Method is a numerical optimization technique that finds the optimal solution by focusing on coefficient conjugate. This paper presents new coefficients conjugate gradient method for removing impulse noise from images, which based quadratic function and proven to be globally convergent. Results show it an effective image restoration.

Conjugate gradient methods are an important class of methods for solving unconstrained optimization problems, especially for large-scale problems. Recently, they have been studied in depth. In this paper, we further study the conjugate gradient method for unconstrained optimization. We focus our attention to the descent conjugate gradient method. This paper presents a modified conjugate gradien...

Background: In this study, Quantitative 32P bremsstrahlung planar and SPECT imaging and consequent dose assessment were carried out as a comprehensive phantom study to define an appropriate method for accurate Dosimetry in clinical practice. Materials and Methods: CT, planar and SPECT bremsstrahlung images of Jaszczak phantom containing a known activity of 32P were acquired. In addition, Phanto...

The techniques and analysis presented in this paper provide new methods to solve optimization problems posed on Riemannian manifolds. A new point of view is offered for the solution of constrained optimization problems. Some classical optimization techniques on Euclidean space are generalized to Riemannian manifolds. Several algorithms are presented and their convergence properties are analyzed...