Fast Reconstruction Method Research Articles

Scaal: A Robust, Accurate, And High-efficient All-atomistic Protein Reconstruction Method From Low-resolution Protein Models

In the quest to develop multiscale molecular simulation methods for complex protein dynamics that fuse high-resolution and low-resolution protein representations, it is important to investigate the required information of reconstruction of all-atomistic proteins from low resolution ones with manageable uncertainly. In this paper, we introduce a robust, accurate, and fast reconstruction method (SCAAL) that produces reliable all-atomistic protein structure by taking few beads from a coarse-grained model with at least one side chain bead and one Cα bead in the backbone (Side chain-Cα Model, SCM) into accounts. Our algorithm (SCAAL) is compared with SCWRL3.0 and it excels in robustness and is more accurate in the reconstruction of large amino acids. In addition, we further test SCAAL in the reconstruction of a complete protein folding trajectory from SCM coarse-grained models. We show that the efficiency, accuracy, and robustness of SCAAL as leverage for multi-scale simulations are excellent in terms of low root mean square deviations that lie within 1A resolution.

A fast method for rectangular crack sizes reconstruction in magnetic flux leakage testing

Magnetic flux leakage (MFL) testing is widely used to examine ferromagnetic materials. For the reason of estimating the sizes of cracks in metals is important in piping industries, a fast method based on particle swarm optimization algorithm is proposed for reconstructing the sizes of rectangular crack in this article. Considering the magnetic leakage field intensity is related to the air gap between the inspection specimen and the sensor, we give the reconstruction results in different lift-off values. Besides, the influence of different magnetic conditions to the reconstruction effectiveness has been investigated. The simulation results have shown the rapidity and accuracy of the proposed method.

Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography

We investigate fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography for applications in small animal imaging. Our forward model uses a diffusion approximation for optically inhomogeneous tissue, which we solve using a finite element method (FEM). We examine two approaches to incorporating the forward model into the solution of the inverse problem. In a conventional direct calculation approach one computes the full forward model by repeated solution of the FEM problem, once for each potential source location. We describe an alternative on-the-fly approach where one does not explicitly solve for the full forward model. Instead, the solution to the forward problem is included implicitly in the formulation of the inverse problem, and the FEM problem is solved at each iteration for the current image estimate. We evaluate the convergence speeds of several representative iterative algorithms. We compare the computation cost of those two approaches, concluding that the on-the-fly approach can lead to substantial reductions in total cost when combined with a rapidly converging iterative algorithm.

Fast Nonlinear Image Reconstruction for Scanning Impedance Imaging

Scanning (electrical) impedance imaging (SII) is a novel high-resolution imaging modality that has the potential of imaging the electrical properties of thin biological tissues. In this paper, we apply the reciprocity principle to the modeling of the SII system and develop a fast nonlinear inverse method for image reconstruction. The method is fast because it uses convolution to eliminate the requirement of a numerical solver for the 3-D electrostatic field in the SII system. Numerical results show that our approach can accurately reveal the exact conductivity distribution from the measured current map for different 2-D simulation phantoms. Experiments were also performed using our SII system for a piece of butterfly wing and breast cancer cells. Two-dimensional current images were measured and corresponding quantitative conductivity images were restored using our approach. The reconstructed images are quantitative and reveal details not present in the measured images.

Comparative study of scoliotic spines using a fast 3D reconstruction method from biplanar X-rays

Biplanar X-ray based methods allow the 3D reconstruction of a skeleton in a standing position with a low radiation dose compared with a CT-scan. With the development of biplanar radiography in a cl...

An overview of fast convergent ordered-subsets reconstruction methods for emission tomography based on the incremental EM algorithm

Statistical reconstruction has become popular in emission computed tomography but suffers slow convergence (to the MAP or ML solution). Methods proposed to address this problem include the fast but non-convergent OSEM and the convergent RAMLA [J. Browne, A. De Pierro, IEEE Trans. Med. Imaging 15 (5) (1996) 687.] for the ML case, and the convergent BSREM [A. De Pierro, M. Yamagishi, IEEE Trans. Med. Imaging 20 (4) (2001) 280.], relaxed OS-SPS and modified BSREM [S. Ahn, J.A. Fessler, IEEE Trans. Med. Imaging 22 (5) (2003) 613.] for the MAP case. The convergent algorithms required a user-determined relaxation schedule. We proposed fast convergent OS reconstruction algorithms for both ML and MAP cases, called COSEM (Complete-data OSEM), which avoid the use of a relaxation schedule while maintaining convergence. COSEM is a form of incremental EM algorithm. Here, we provide a derivation of our COSEM algorithms and demonstrate COSEM using simulations. At early iterations, COSEM-ML is typically slower than RAMLA, and COSEM-MAP is typically slower than optimized BSREM while remaining much faster than conventional MAP-EM. We discuss how COSEM may be modified to overcome these limitations.

Direct fast method for time-limited signal reconstruction

We consider reconstruction of signals by a direct method for the solution of the discrete Fourier system. We note that the reconstruction of a time-limited signal can be simply realized by using only either the real part or the imaginary part of the discrete Fourier transform (DFT) matrix. Therefore, based on the study of the special structure of the real and imaginary parts of the discrete Fourier matrix, we propose a fast direct method for the signal reconstruction problem, which utilizes the numerically truncated singular value decomposition. The method enables us to recover the original signal in a stable way from the frequency information, which may be corrupted by noise and/or some missing data. The classical inverse Fourier transform cannot be applied directly in the latter situation. The pivotal point of the reconstruction is the explicit computation of the singular value decomposition of the real part of the DFT for any order. Numerical experiments for 1D and 2D signal reconstruction and image restoration are given.

Fast Reconstruction Methods for Bandlimited Functions from Periodic Nonuniform Sampling

A well-known generalization of Shannon's sampling theorem states that a bandlimited function can be reconstructed from its periodic nonuniformly spaced samples if the effective sampling rate is at least the Nyquist rate. Analogous to Shannon's sampling theorem this generalization requires that an infinite number of samples be available, which, however, is never the case in practice. Most existing reconstruction methods for periodic nonuniform sampling yield very low order (often not even first order) accuracy when only a finite number of samples is given. In this paper we propose a fast, numerically robust, root-exponential accurate reconstruction method. The efficiency and accuracy of the algorithm is obtained by fully exploiting the sampling structure and utilizing localized Fourier analysis. We discuss applications in analog-to-digital conversion where nonuniform periodic sampling arises in various situations. Finally, we demonstrate the performance of our algorithm by numerical examples.

Implementation of neutron tomography around the Algerian Es-Salam research reactor: preliminary studies and first steps

During the last three years a static neutron radiography facility has been designed and installed around one of the horizontal neutron beam channels of the Es-Salam research reactor. In order to extend the field of application of neutron radiography, a development project is actually undertaken. The purpose of this project is the implementation of neutron tomography (NT). The Es-Salam research reactor provides a 10 9 n/cm 2 s thermal neutron beam for the installation of a tomography facility. The aim of this Tomography facility will be to visualize dynamic processes and to implement fast reconstruction methods. A first step in this direction was the design and the realization of an exposition cell, a beam-catcher, a turn table and a neutron beam collimation system. All electronic parts of the detection system will be provided and installed within the frame work of a Technical Cooperation Project (TCP) with the International Atomic Energy Agency (IAEA). This paper describes the estimation procedure of the main design parameters according to the measurement of the beam properties for final tomography station as well as the composition and the performance of the proposed detection system. Moreover, the influence of physical parameters of the irradiation in-pile parts on the image quality and acquisition process is discussed.

Fast Inversion of the Radon Transform Using Log-polar Coordinates and Partial Back-Projections

In this paper a novel filtered back-projection algorithm for inversion of a discretized Radon transform is presented. It makes use of invariance properties possessed by both the Radon transform and its dual. By switching to log-polar coordinates, both operators can be expressed in a displacement invariant manner. Explicit expressions for the corresponding transfer functions are calculated. Furthermore, by dividing the back-projection into several partial back-projections, inversion can be performed by means of finite convolutions and hence implemented by an FFT-algorithm. In this way, a fast and accurate reconstruction method is obtained.

Multiparameter measurement for raceway groove of bearing based on 3D reconstruction with digital structured light

A fast 3D reconstruction method based on structured light to measure various parameters of the raceway groove is presented. Digital parallel grating stripes distributed with sine density are projected onto the raceway groove by a DLP projector, and distorting of stripes is happened on the raceway. Simultaneously, aided by three-step phase-shifting approach, three images covered by different stripes are obtained by a high-resolution CCD camera at the same location, thus a more accuracy local topography can be obtained. And then the bearing is rotated on a high precision computer controlled rotational stage. Three images are also obtained as the former step at next planned location triggered by the motor. After one cycle, all images information is combined through the mosaics. As a result, the 3D information of raceway groove can be gained. Not only geometric properties but also surface flaws can be extracted by software. A preliminary hardware system has been built, with which some geometric parameters have been extracted from reconstructed local topography.

Implicit surface reconstruction from contours

This paper presents a fast and efficient surface reconstruction method from contour data sets. The reconstructed surface is defined as an implicit surface. We need not create any geometric skeleton and the blending of the three dimensional contour functions enables us to avoid the correspondence and the branching problems that occur in geometrical methods. Tests carried out with medical scanner data-sets show that the reconstruction may be performed at interactive rates.

Wave-front reconstruction from shear phase maps by use of the discrete Fourier transform

Fast wave-front reconstruction methods are becoming increasingly important, for example, in large astronomical adaptive optics systems and high spatial resolution shear interferometry, where pseudoinverse matrix methods scale poorly with problem size. Wave-front reconstruction from difference measurements can be achieved by use of fast implementations of the discrete Fourier transform (DFT), obtaining performance comparable with that of the pseudoinverse in terms of the noise propagation coefficient. Existing methods that are based on the use of the DFT give exact results (in the absence of noise) only for the particular case in which the shear is a divisor of the number of samples to be reconstructed. We present two alternate solutions for the more general case when the shear is any integer. In the first solution the dimensions of the problem are enlarged, and in the second the problem is subdivided into a set of smaller problems with shear amplitude equal to one. We also show that the retrieved solutions have minimum norm and calculate the noise propagation coefficient for both methods. The proposed algorithms are implemented and timed against pseudoinverse multiplication. The results show a speed increase by a factor of 50 over the pseudoinverse multiplication for a grid with N = 3 x 10(3) samples.

Image Compression and Reconstruction based on Fuzzy Relation and Soft Computing Technology

A fast image reconstruction method for Image Compression method based on Fuzzy relational equation (ICF) and soft computing is proposed. In experiments using 20 images (Standard Image DataBAse), the decrease in image reconstruction time to 1/132.02 and 1/382.29 are obtained when the compression rate is 0.0156 and 0.0625, respectively, and the proposed method outperforms the conventional one in the Peak Signal to Noise Ratio (PSNR). ICF using nonuniform coders over YUV color space is proposed in order to achieve effective compression. Linear quantization of compressed image data is introduced in order to improve the compression rate. Through experiments using 100 typical images (Corel Gallery, Arizona Directory), PSNR increases at 7.9-14.1% compared with the conventional method under the condition that compression rates are 0.0234-0.0938.

Computing in optics - Computational aspects of diffuse optical tomography

Diffuse optical tomography (DOT) is a novel functional imaging modality for visualizing and continuously monitoring tissue and blood oxygenation levels, which is useful for brain imaging and tumor detection. Because of the nonlinearity of infrared light propagation in tissue, developing fast and robust reconstruction methods is the main challenge in making DOT a viable tool for clinical diagnostics.

Fast semiautomatic stereoradiographic reconstruction of scoliotic spines using multi-scale image processing and statistical geometric models

We propose a fast stereoradiographic reconstruction method for the spine. Semiautomatic detection of the vertebral bodies' corners is obtained using splines, multi-scale analysis and dynamic programming. These points, combined with calibration data, delimit a hexahedron in space where we can match the vertebral body. This hexahedron is then passed through a 3D statistical database built using local and global information generated from a bank of normal and scoliotic spines. Finally, models of the vertebrae are positioned with respect to these landmarks, completing the 3D reconstruction.

Newton‐SOR method for fast statistical tomographic image reconstruction

AbstractThere are recent studies on image reconstruction for emission CT and transmission CT, using the maximum likelihood (ML) estimation or the maximum a posteriori (MAP) estimation, aiming at quality improvement of the reconstructed image. Those methods in general have the problem that minimization of the convex cost function by iterations is required, which increases the computational complexity. This paper proposes a new image reconstruction method, which requires less computation per iteration and realizes a fast convergence. The proposed method is based on the principle that the convex cost function is approximated in the iteration by a quadratic function, and the value of the quadratic function is reduced by SOR method, which is the solution procedure for the linear equation. The method can be called the Newton‐SOR method in the sense that both methods are combined. In order to guarantee global convergence of the Newton‐SOR method, mathematical techniques, called trust region and line search methods, are used, and the projection SOR method with the nonnegative condition is used, where a negative value appearing in the iteration is replaced by zero. The proposed method satisfies all of the desired properties of the iteration method as follows. (1) The convergence to the true solution minimizing the cost function is guaranteed. (2) The computational complexity per iteration is less, and a fast convergence is realized. (3) The method can be applied in a unified way to a wide range of convex cost functions. (4) The nonnegative condition can be handled easily. The effectiveness of the proposed method is demonstrated by a simulation experiment. The result of application to the clinical PET data is also shown. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(4): 1–11, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10178

Fast Local Reconstruction Methods for Nonuniform Sampling in Shift-Invariant Spaces

We present a new method for the fast reconstruction of a function f from its samples f(xj) under the assumption that f belongs to a shift-invariant space $V(\varphi)$. If the generator $\varphi$ has compact support, then the reconstruction is local, quite in contrast to methods based on band-limited functions. Using frame theoretic arguments, we show that the matrix of the corresponding linear system of equations is a positive-definite banded matrix. This special structure makes possible the fast local reconstruction algorithm in O(S2J) operations, where J is the number of samples and S is the support length of the generator $\varphi$. Further optimization can be achieved by means of data segmentation. Ample numerical simulation is provided.

HeinzelCluster: accelerated reconstruction for FORE and OSEM3D

Using iterative three-dimensional (3D) reconstruction techniques for reconstruction of positron emission tomography (PET) is not feasible on most single-processor machines due to the excessive computing time needed, especially so for the large sinogram sizes of our high-resolution research tomograph (HRRT).In our first approach to speed up reconstruction time we transform the 3D scan into the format of a two-dimensional (2D) scan with sinograms that can be reconstructed independently using Fourier rebinning (FORE) and a fast 2D reconstruction method. On our dedicated reconstruction cluster (seven four-processor systems, Intel PIII@700 MHz, switched fast ethernet and Myrinet, Windows NT Server), we process these 2D sinograms in parallel. We have achieved a speedup >23 using 26 processors and also compared results for different communication methods (RPC, Syngo, Myrinet GM).The other approach is to parallelize OSEM3D (implementation of C Michel), which has produced the best results for HRRT data so far and is more suitable for an adequate treatment of the sinogram gaps that result from the detector geometry of the HRRT. We have implemented two levels of parallelization for our dedicated cluster (a shared memory fine-grain level on each node utilizing all four processors and a coarse-grain level allowing for 15 nodes) reducing the time for one core iteration from over 7 h to about 35 min.

A direct sinogram-restoration method for fast image reconstruction in compact DOI-PET systems

In a recent study, we proposed a sophisticated data-processing technique for correcting parallax errors that we believe allows for different, more efficient, and cost-effective design strategies. In particular, we demonstrated with a conceptual design the general feasibility of adopting this approach for the construction of compact positron emission tomography (PET) systems. In our design, we employed the new-generation depth-of-interaction detectors in a circular, highly compact PET system to generate imperfect, but redundant, data. The redundancy was exploited by sinograin restoration (SR) techniques to correct for the data imperfection, thereby obtaining high-quality PET images despite the use of a compact PET configuration. In this paper, we consider the specific task of applying the proposed design concept for building a dedicated, compact, small-animal PET system. We develop a direct SR method that is suitable for use in the small-animal system. We evaluate the performance of this conceptual design with computer simulations. Our results indicate that, with the effects of scatter and randoms ignored, the proposed design can produce images of quality comparable to those generated by a conventional larger ring system.