A novel approach for studies of multispectral bioluminescence tomography

Bioluminescence tomography (BLT) is a promising new area in biomedical imaging. The goal of BLT is to provide quantitative reconstruction of bioluminescent source distribution within a small animal from optical signals on the animal’s body surface. The multispectral version of BLT takes advantage of the measurement information in different spectrum bands. In this paper, we propose a novel approach for the multispectral BLT. The new feature of the mathematical framework is to use numerical prediction results based on two related but distinct boundary value problems. This mathematical framework includes the conventional framework in the study of multispectral BLT. For the new framework introduced here, we establish the solution existence, uniqueness and continuous dependence on data, and characterize the limiting behaviors when the regularization parameter approaches zero or when the penalty parameter approaches infinity. We study two kinds of numerical schemes for multispectral BLT and derive error estimates for the numerical solutions. We also present numerical examples to show the performance of the numerical methods.     

Studies of a mathematical model for temperature-modulated bioluminescence tomography

We introduce and study a mathematical model for temperature-modulated bioluminescence tomography (TBT). The model is capable of self-adjusting values of experimental parameters that are used in the formulation. Major theoretical results of this article include: Solution existence of the model, convergence of numerical solutions, an iterative scheme based on linearization, studies of the solution limiting behaviours when normalized total energy function and/or some or all the energy percentages in individual spectral bands are known exactly. Several numerical examples are included to illustrate the improvement of the accuracy of the reconstructed bioluminescent source distribution due to the employment of measurements from multiple temperature distributions.     

Theoretical and numerical analysis on multispectral bioluminescence tomography

Recently, molecular imaging has been rapidly developed to studyphysiological and pathological processes in vivo at the cellularand molecular levels. Among molecular imaging modalities, opticalimaging has attracted a major attention for its unique advantages.In this paper, we establish a mathematical framework for multispectralbioluminescence tomography (BLT) that allows simultaneous studiesof multiple optical reporters. We show solution existence, uniquenessand continuous dependence on data as well as the limiting behaviourswhen the regularization parameter approaches zero or when thepenalty parameter approaches infinity. Then, we propose twonumerical schemes for multispectral BLT and derive error estimatesfor the corresponding solutions.     

Recent progress on contactless inductive flow tomography

The contactless inductive flow tomography (CIFT) is a technique to reconstruct the velocity in electrically conducting melts using magnetic fields. One of its application could be the velocity reconstruction in the mould of the continuous casting process. In this paper, we present the numerical investigation and first measurements of the induced magnetic field taken in our lab on a small model of a continuous casting mould. (© 2009 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Approximate inverse meets local tomography

Local or lambda tomography reconstructs Λƒ which has the same discontinuities as the searched‐for density distribution ƒ. Computing Λƒ, however, requires only local tomographic measurements. Local tomography is usually implemented by a filtered backprojection algorithm (FBA). In the present article we design reconstruction filters for the FBA such that Λ^2m+1ƒ will be reconstructed for a given m∈ℕ₀. Moreover, we prove convergence and convergence rates for the FBA as the discretization step size goes to zero. To this end we express the FBA in the framework of approximate inverse. Based on our analysis we further propose a scheme which yields a proper scaling of the reconstruction filters. Numerical experiments illustrate the analytic results. Copyright © 2000 John Wiley & Sons, Ltd.     

Wave Surface Identification Based on Stereo Vision and Wave Theory: an Initial Attempt北大核心CSCD

波高是波浪信息最基本的元素,对波高的精确测量无论是对波浪理论的研究还是数值方法的拓展,都起着指导和验证的作用。文中基于双目立体视觉原理自主搭建了波面光学测量系统,突破了传统测量设备如浪高仪等单点测量的局限性,并将波浪理论融入到数据后处理方法中,对常用的单纯依赖图像的光学测量方法进行了改进。通过在拖曳水池中对单向规则波瞬时波面的识别和重构,并将结果与浪高仪以及理论来波参数进行了对比验证,结果表明该测量系统在大范围波面的测量中误差在1%左右,最后对其在非规则的来波下进行了初步尝试。     

A globally convergent numerical method for an inverse elliptic problem of optical tomography

A new globally convergent numerical method is developed for an inverse problem for the elliptic equation with the unknown potential. The boundary data simulating measurements in optical tomography are generated by the running source. Global convergence analysis is presented along with numerical experiments.     

The statement and numerical solution of an optimization problem in X-ray tomography

A nonclassical problem is considered for the transport equation with coefficients depending on the energy of radiation. The task is to find the discontinuity surfaces for the coefficients of the equation from measurements of the radiation flux leaving the medium. For this tomography problem, an optimization problem is stated and numerically analyzed. The latter consists in determining the radiation energy that ensures the best reconstruction of the unknown medium. A simplified optimization problem is solved analytically.     

NaClO3晶体生长过程光学诊断与数值模拟

运用光学干涉诊断方法实时观测NaClO3晶体生长过程，得到晶体生长过程中溶液浓度，晶体尺寸等物理参量．将这些参量与数值模拟得到的结果进行比对，研究重力条件下NaClO3晶体溶液生长过程中速度场、浓度场的分布与演化，尝试提出符合实际情况的晶体生长理论模型,对比两种方法得到的浓度边界层厚度，数值模拟得到了与实验数据相一致的结论.     

A direct impedance tomography algorithm for locating small inhomogeneities

Summary.  Impedance tomography seeks to recover the electrical conductivity distribution inside a body from measurements of current flows and voltages on its surface. In its most general form impedance tomography is quite ill-posed, but when additional a-priori information is admitted the situation changes dramatically. In this paper we consider the case where the goal is to find a number of small objects (inhomogeneities) inside an otherwise known conductor. Taking advantage of the smallness of the inhomogeneities, we can use asymptotic analysis to design a direct (i.e., non-iterative) reconstruction algorithm for the determination of their locations. The viability of this direct approach is documented by numerical examples. Received May 28, 2001 / Revised version received March 15, 2002 / Published online July 18, 2002 RID="⋆" ID="⋆" Supported by the Deutsche Forschungsgemeinschaft (DFG) under grant HA 2121/2-3 RID="⋆⋆" ID="⋆⋆" Supported by the National Science Foundation under grant DMS-0072556 Mathematics Subject Classification (2000): 65N21, 35R30, 35C20     

High contrast microwave tomography using topology optimization techniques

Microwave tomography for medical applications leads to a difficult reconstruction problem for the dielectric properties of biological tissue due to strongly diffracting waves in combination with large dielectric contrasts. We apply the material distribution technique used for topology optimization of elastic structures in order to solve the nonlinear least-squares problem underlying the reconstruction problem. Using simulated numerical data with an approximate signal-to-noise ratio of 40 dB and geometrical a priori information on the unknown objects, we obtain good estimates of the dielectric properties corresponding to biological objects.     

A multi-scale image reconstruction algorithm for electrical capacitance tomography

Electrical capacitance tomography (ECT) is considered as a promising process tomography (PT) technology, and its successful applications depend mainly on the precision and speed of the image reconstruction algorithms. In this paper, based on the wavelet multi-scale analysis method, an efficient image reconstruction algorithm is presented. The original inverse problem is decomposed into a sequence of inverse problems, which are solved successively from the largest scale to the smallest scale. At different scales, the inverse problem is solved by a generalized regularized total least squares (TLS) method, which is developed using a combinational minimax estimation method and an extended stabilizing functional, until the solution of the original inverse problem is found. The homotopy algorithm is employed to solve the objective functional. The proposed algorithm is tested by the noise-free capacitance data and the noise-contaminated capacitance data, and excellent numerical performances and satisfactory results are observed. In the cases considered in this paper, the reconstruction results show remarkable improvement in the accuracy. The spatial resolution of the reconstructed images by the proposed algorithm is enhanced and the artifacts in the reconstructed images can be eliminated effectively. As a result, a promising algorithm is introduced for ECT image reconstruction.     

A polychromatic inhomogeneity indicator in an unknown medium for an X-ray tomography problem

We pose and study an X-ray tomography problem, which is an inverse problem for the transport differential equation, making account for particle absorption by a medium and single scattering. The statement of the problem corresponds to a stage-by-stage probing of the unknown medium common in practice. Another step towards a more realistic problem is the use of integrals over energy of the density of emanating radiation flux as the known data, in contrast to specifying the flux density for every energy level, as it is customary in tomography. The required objects are the discontinuity surfaces of the coefficients of the equation, which corresponds to searching for the boundaries between various substances contained in the medium. We prove a uniqueness theorem for the solution under quite general assumptions and a condition ensuring the existence of the required surfaces. The proof is rather constructive in character and suitable for creating a numerical algorithm.     

Sparse regularization in limited angle tomography

We investigate the reconstruction problem of limited angle tomography. Such problems arise naturally in applications like digital breast tomosynthesis, dental tomography, electron microscopy, etc. Since the acquired tomographic data is highly incomplete, the reconstruction problem is severely ill-posed and the traditional reconstruction methods, e.g. filtered backprojection (FBP), do not perform well in such situations.To stabilize the reconstruction procedure additional prior knowledge about the unknown object has to be integrated into the reconstruction process. In this work, we propose the use of the sparse regularization technique in combination with curvelets. We argue that this technique gives rise to an edge-preserving reconstruction. Moreover, we show that the dimension of the problem can be significantly reduced in the curvelet domain. To this end, we give a characterization of the kernel of the limited angle Radon transform in terms of curvelets and derive a characterization of solutions obtained through curvelet sparse regularization. In numerical experiments, we will show that the theoretical results directly translate into practice and that the proposed method outperforms classical reconstructions.     

Thermoacoustic tomography for an integro-differential wave equation modeling attenuation

In this article we study the inverse problem of thermoacoustic tomography (TAT) on a medium with attenuation represented by a time-convolution (or memory) term, and whose consideration is motivated by the modeling of ultrasound waves in heterogeneous tissue via fractional derivatives with spatially dependent parameters. Under the assumption of being able to measure data on the whole boundary, we prove uniqueness and stability, and propose a convergent reconstruction method for a class of smooth variable sound speeds. By a suitable modification of the time reversal technique, we obtain a Neumann series reconstruction formula.     

Boundary integral formulations for the forward problem in magnetic induction tomography

In this paper we present two models for the forward problem of magnetic induction tomography. In particular, we describe the eddy current model, and a reduced simplified model. The error between the reduced and the full model is analyzed in dependence of parameters such as the frequency and the conductivity. In the case of a piecewise constant conductivity we derive a boundary integral formulation for the reduced model. Finally, we comment on numerical results for the forward problem and give a comparison of both models. Copyrightcopyright 2011 John Wiley & Sons, Ltd.     

An inverse problem for the acoustics equations: A multilevel adaptive algorithm

A numerical solution to an inverse problem for the acoustic equations using an optimization method for a stratified medium is presented. With the distribution of an acoustic wave field on the medium’s surface, the 1D distributions of medium’s density, as well as the velocity and absorption coefficient of the acoustic wave, are determined. Absorption in a Voigt body model is considered. The conjugate gradients and the Newton method are used for minimization. To increase the efficiency of the numerical method, a multilevel adaptive algorithm is proposed. The algorithm is based on a division of the whole procedure of solving the inverse problem into a series of consecutive levels. Each level is characterized by the number of parameters to be determined at the level. In moving from one level to another, the number of parameters changes adaptively according to the functional minimized and the convergence rate. The minimization parameters are chosen as illustrated by results of solving the inverse problem in a spectral domain, where the desired quantities are presented as Chebyshev polynomial series and minimization is carried out with respect to the coefficients of these series. The method is compared in efficiency with a nonadaptive method. The optimal parameters of the multilevel method are chosen. It is shown that the multilevel algorithm offers several advantages over the one without partitioning into levels. The algorithm produces primarily a more accurate solution to the inverse problem.     

Towards analytical model optimization in atmospheric tomography

Modern ground‐based telescopes rely on a technology called adaptive optics in order to compensate for the loss of angular resolution caused by atmospheric turbulence. Next‐generation adaptive optics systems designed for a wide field of view require a stable and high‐resolution reconstruction of the turbulent atmosphere. By introducing a novel Bayesian method, we address the problem via reconstructing the atmospheric turbulence strength profile and the turbulent layers simultaneously, where we only use wavefront measurements of incoming light from guide stars. Most importantly, we demonstrate how this method can be used for model optimization as well. We propose two different algorithms for solving the maximum a posteriori estimate: the first approach is based on alternating minimization and has the advantage of integrability into existing atmospheric tomography methods. In the second approach, we formulate a convex non‐differentiable optimization problem, which is solved by an iterative thresholding method. This approach clearly illustrates the underlying sparsity‐enforcing mechanism for the strength profile. By introducing a tuning/regularization parameter, an automated model reduction of the layer structure of the atmosphere is achieved. Using numerical simulations, we demonstrate the performance of our method in practice. Copyright © 2016 John Wiley & Sons, Ltd.