Gaohang Yu

Higher Order Positive Semidefinite Diffusion Tensor Imaging

Due to the well-known limitations of diffusion tensor imaging, high angular resolution diffusion imaging (HARDI) is used to characterize non-Gaussian diffusion processes. One approach to analyzing HARDI data is to model the apparent diffusion coefficient (ADC) with higher order diffusion tensors. The diffusivity function is positive semidefinite. In the literature, some methods have been proposed to preserve positive semidefiniteness of second order and fourth order diffusion tensors. None of them can work for arbitrarily high order diffusion tensors. In this paper, we propose a comprehensive model to approximate the ADC profile by a positive semidefinite diffusion tensor of either second or higher order. We call this the positive semidefinite diffusion tensor (PSDT) model. PSDT is a convex optimization problem with a convex quadratic objective function constrained by the nonnegativity requirement on the smallest Z-eigenvalue of the diffusivity function. The smallest Z-eigenvalue is a computable measure of the extent of positive definiteness of the diffusivity function. We also propose some other invariants for the ADC profile analysis. Experiment results show that higher order tensors could improve the estimation of anisotropic diffusion and that the PSDT model can depict the characterization of diffusion anisotropy which is consistent with known neuroanatomy.

The Z-eigenvalues of a symmetric tensor and its application to spectral hypergraph theory

SUMMARY In this paper, using variational analysis and optimization techniques, we examine some fundamental analytic properties of Z-eigenvalues of a real symmetric tensor with even order. We first establish that the maximum Z-eigenvalue function is a continuous and convex function on the symmetric tensor space and so provide formulas of the convex conjugate function and e-subdifferential of the maximum Z-eigenvalue function. Consequently, for an mth-order N-dimensional tensor A, we show that the normalized eigenspace associated with maximum Z-eigenvalue function is ρth-order Holder stable at A with ρ=1m(3m−3)n−1−1. As a by-product, we also establish that the maximum Z-eigenvalue function is always at least ρth-order semismooth at A. As an application, we introduce the characteristic tensor of a hypergraph and show that the maximum Z-eigenvalue function of the associated characteristic tensor provides a natural link for the combinatorial structure and the analytic structure of the underlying hypergraph. Finally, we establish a variational formula for the second largest Z-eigenvalue for the characteristic tensor of a hypergraph and use it to provide lower bounds for the bipartition width of a hypergraph. Some numerical examples are also provided to show how one can compute the largest/second-largest Z-eigenvalue of a medium size tensor, using polynomial optimization techniques and our variational formula. Copyright

The Superlinear Convergence of a Modified BFGS-Type Method for Unconstrained Optimization

The BFGS method is the most effective of the quasi-Newton methods for solving unconstrained optimization problems. Wei, Li, and Qi [16] have proposed some modified BFGS methods based on the new quasi-Newton equation Bk+1sk = y*k, where y*k is the sum of yk and Aksk, and Ak is some matrix. The average performance of Algorithm 4.3 in [16] is better than that of the BFGS method, but its superlinear convergence is still open. This article proves the superlinear convergence of Algorithm 4.3 under some suitable conditions.

Spectral conjugate gradient methods with sufficient descent property for large-scale unconstrained optimization

A class of new spectral conjugate gradient methods are proposed in this paper. First, we modify the spectral Perrys conjugate gradient method, which is the best spectral conjugate gradient algorithm SCG by Birgin and Martinez [E.G. Birgin and J.M. Martinez, A spectral conjugate gradient method for unconstrained optimization, Appl. Math. Optim. 43 (2001), 117–128.], such that it possesses sufficient descent property for any (inexact) line search. It is shown that, for strongly convex functions, the method is a global convergent. Further, a global convergence result for nonconvex minimization is established when the line search fulfils the Wolfe line search conditions. Some other spectral conjugate gradient methods with guaranteed descent are presented here. Numerical comparisons are given with both SCG and CG_DESCENT methods using the unconstrained optimization problems in the CUTE library.

A descent nonlinear conjugate gradient method for large-scale unconstrained optimization

In this paper, a new nonlinear conjugate gradient method was proposed for large-scale unconstrained optimization which possesses the following three properties: (i) the sufficient descent property holds without any line searches; (ii) employing some steplength technique which ensures the Zoutendijk condition to be held, this method is globally convergent; (iii) this method inherits an important property of the Polak-Ribiere-Polyak (PRP) method: the tendency to turn towards the steepest descent direction if a small step is generated away from the solution, preventing a sequence of tiny steps from happening. Preliminary numerical results show that this method is very promising.

Nonnegative Diffusion Orientation Distribution Function

Because of the well-known limitations of diffusion tensor imaging (DTI) in regions of low anisotropy and multiple fiber crossing, high angular resolution diffusion imaging (HARDI) and Q-Ball Imaging (QBI) are used to estimate the probability density function (PDF) of the average spin displacement of water molecules. In particular, QBI is used to obtain the diffusion orientation distribution function (ODF) of these multiple fiber crossing. As a probability distribution function, the orientation distribution function should be nonnegative which is not guaranteed in the existing methods. This paper proposes a novel technique to guarantee the nonnegative property of ODF by solving a convex optimization problem, which has a convex quadratic objective function and a constraint involving the nonnegativity requirement on the smallest Z-eigenvalue of the diffusivity tensor. Using convex analysis and optimization techniques, we first derive the optimality conditions of this convex optimization problem. Then, we propose a gradient descent algorithm to solve this problem. We also present formulas for determining the principal directions (maxima) of the ODF. Numerical examples on synthetic data as well as MRI data are displayed to demonstrate the significance of our approach.

On Nonmonotone Chambolle Gradient Projection Algorithms for Total Variation Image Restoration

The main aim of this paper is to accelerate the Chambolle gradient projection method for total variation image restoration. In the proposed minimization method model, we use the well known Barzilai-Borwein stepsize instead of the constant time stepsize in Chambolle’s method. Further, we adopt the adaptive nonmonotone line search scheme proposed by Dai and Fletcher to guarantee the global convergence of the proposed method. Numerical results illustrate the efficiency of this method and indicate that such a nonmonotone method is more suitable to solve some large-scale inverse problems.

Fast communication: Impulse noise removal by a nonmonotone adaptive gradient method

Image denoising is a fundamental problem in image processing. This paper proposes a nonmonotone adaptive gradient method (NAGM) for impulse noise removal. The NAGM is a low-complexity method and its global convergence can be established. Numerical results illustrate the efficiency of the NAGM and indicate that such a nonmonotone method is more suitable to solve some large-scale signal processing problems.

Multivariate spectral gradient method for unconstrained optimization

Multivariate spectral gradient method is proposed for solving unconstrained optimization problems. Combined with some quasi-Newton property multivariate spectral gradient method allows an individual adaptive stepsize along each coordinate direction, which guarantees that the method is finitely convergent for positive definite quadratics. Especially, it converges no more than two steps for positive definite quadratics with diagonal Hessian, and quadratically for objective functions with positive definite diagonal Hessian. Moreover, based on a nonmonotone line search, global convergence is established for multivariate spectral gradient algorithm. At last numerical results are reported, which show that this method is promising and deserves further discussing.

Low-dose cerebral perfusion computed tomography image restoration via low-rank and total variation regularizations

Cerebral perfusion x-ray computed tomography (PCT) is an important functional imaging modality for evaluating cerebrovascular diseases and has been widely used in clinics over the past decades. However, due to the protocol of PCT imaging with repeated dynamic sequential scans, the associative radiation dose unavoidably increases as compared with that used in conventional CT examinations. Minimizing the radiation exposure in PCT examination is a major task in the CT field. In this paper, considering the rich similarity redundancy information among enhanced sequential PCT images, we propose a low-dose PCT image restoration model by incorporating the low-rank and sparse matrix characteristic of sequential PCT images. Specifically, the sequential PCT images were first stacked into a matrix (i.e., low-rank matrix), and then a non-convex spectral norm/regularization and a spatio-temporal total variation norm/regularization were then built on the low-rank matrix to describe the low rank and sparsity of the sequential PCT images, respectively. Subsequently, an improved split Bregman method was adopted to minimize the associative objective function with a reasonable convergence rate. Both qualitative and quantitative studies were conducted using a digital phantom and clinical cerebral PCT datasets to evaluate the present method. Experimental results show that the presented method can achieve images with several noticeable advantages over the existing methods in terms of noise reduction and universal quality index. More importantly, the present method can produce more accurate kinetic enhanced details and diagnostic hemodynamic parameter maps.