Comparison study of reconstruction algorithms for prototype digital breast tomosynthesis using various breast phantoms.

PubMed

Kim, Ye-seul; Park, Hye-suk; Lee, Haeng-Hwa; Choi, Young-Wook; Choi, Jae-Gu; Kim, Hak Hee; Kim, Hee-Joung

2016-02-01

Digital breast tomosynthesis (DBT) is a recently developed system for three-dimensional imaging that offers the potential to reduce the false positives of mammography by preventing tissue overlap. Many qualitative evaluations of digital breast tomosynthesis were previously performed by using a phantom with an unrealistic model and with heterogeneous background and noise, which is not representative of real breasts. The purpose of the present work was to compare reconstruction algorithms for DBT by using various breast phantoms; validation was also performed by using patient images. DBT was performed by using a prototype unit that was optimized for very low exposures and rapid readout. Three algorithms were compared: a back-projection (BP) algorithm, a filtered BP (FBP) algorithm, and an iterative expectation maximization (EM) algorithm. To compare the algorithms, three types of breast phantoms (homogeneous background phantom, heterogeneous background phantom, and anthropomorphic breast phantom) were evaluated, and clinical images were also reconstructed by using the different reconstruction algorithms. The in-plane image quality was evaluated based on the line profile and the contrast-to-noise ratio (CNR), and out-of-plane artifacts were evaluated by means of the artifact spread function (ASF). Parenchymal texture features of contrast and homogeneity were computed based on reconstructed images of an anthropomorphic breast phantom. The clinical images were studied to validate the effect of reconstruction algorithms. The results showed that the CNRs of masses reconstructed by using the EM algorithm were slightly higher than those obtained by using the BP algorithm, whereas the FBP algorithm yielded much lower CNR due to its high fluctuations of background noise. The FBP algorithm provides the best conspicuity for larger calcifications by enhancing their contrast and sharpness more than the other algorithms; however, in the case of small-size and low-contrast microcalcifications, the FBP reduced detectability due to its increased noise. The EM algorithm yielded high conspicuity for both microcalcifications and masses and yielded better ASFs in terms of the full width at half maximum. The higher contrast and lower homogeneity in terms of texture analysis were shown in FBP algorithm than in other algorithms. The patient images using the EM algorithm resulted in high visibility of low-contrast mass with clear border. In this study, we compared three reconstruction algorithms by using various kinds of breast phantoms and patient cases. Future work using these algorithms and considering the type of the breast and the acquisition techniques used (e.g., angular range, dose distribution) should include the use of actual patients or patient-like phantoms to increase the potential for practical applications.

Advanced Fast 3-D Electromagnetic Solver for Microwave Tomography Imaging.

PubMed

Simonov, Nikolai; Kim, Bo-Ra; Lee, Kwang-Jae; Jeon, Soon-Ik; Son, Seong-Ho

2017-10-01

This paper describes a fast-forward electromagnetic solver (FFS) for the image reconstruction algorithm of our microwave tomography system. Our apparatus is a preclinical prototype of a biomedical imaging system, designed for the purpose of early breast cancer detection. It operates in the 3-6-GHz frequency band using a circular array of probe antennas immersed in a matching liquid; it produces image reconstructions of the permittivity and conductivity profiles of the breast under examination. Our reconstruction algorithm solves the electromagnetic (EM) inverse problem and takes into account the real EM properties of the probe antenna array as well as the influence of the patient's body and that of the upper metal screen sheet. This FFS algorithm is much faster than conventional EM simulation solvers. In comparison, in the same PC, the CST solver takes ~45 min, while the FFS takes ~1 s of effective simulation time for the same EM model of a numerical breast phantom.

3D reconstruction of synapses with deep learning based on EM Images

NASA Astrophysics Data System (ADS)

Xiao, Chi; Rao, Qiang; Zhang, Dandan; Chen, Xi; Han, Hua; Xie, Qiwei

2017-03-01

Recently, due to the rapid development of electron microscope (EM) with its high resolution, stacks delivered by EM can be used to analyze a variety of components that are critical to understand brain function. Since synaptic study is essential in neurobiology and can be analyzed by EM stacks, the automated routines for reconstruction of synapses based on EM Images can become a very useful tool for analyzing large volumes of brain tissue and providing the ability to understand the mechanism of brain. In this article, we propose a novel automated method to realize 3D reconstruction of synapses for Automated Tapecollecting Ultra Microtome Scanning Electron Microscopy (ATUM-SEM) with deep learning. Being different from other reconstruction algorithms, which employ classifier to segment synaptic clefts directly. We utilize deep learning method and segmentation algorithm to obtain synaptic clefts as well as promote the accuracy of reconstruction. The proposed method contains five parts: (1) using modified Moving Least Square (MLS) deformation algorithm and Scale Invariant Feature Transform (SIFT) features to register adjacent sections, (2) adopting Faster Region Convolutional Neural Networks (Faster R-CNN) algorithm to detect synapses, (3) utilizing screening method which takes context cues of synapses into consideration to reduce the false positive rate, (4) combining a practical morphology algorithm with a suitable fitting function to segment synaptic clefts and optimize the shape of them, (5) applying the plugin in FIJI to show the final 3D visualization of synapses. Experimental results on ATUM-SEM images demonstrate the effectiveness of our proposed method.

Fisher's method of scoring in statistical image reconstruction: comparison of Jacobi and Gauss-Seidel iterative schemes.

PubMed

Hudson, H M; Ma, J; Green, P

1994-01-01

Many algorithms for medical image reconstruction adopt versions of the expectation-maximization (EM) algorithm. In this approach, parameter estimates are obtained which maximize a complete data likelihood or penalized likelihood, in each iteration. Implicitly (and sometimes explicitly) penalized algorithms require smoothing of the current reconstruction in the image domain as part of their iteration scheme. In this paper, we discuss alternatives to EM which adapt Fisher's method of scoring (FS) and other methods for direct maximization of the incomplete data likelihood. Jacobi and Gauss-Seidel methods for non-linear optimization provide efficient algorithms applying FS in tomography. One approach uses smoothed projection data in its iterations. We investigate the convergence of Jacobi and Gauss-Seidel algorithms with clinical tomographic projection data.

Using an EM Covariance Matrix to Estimate Structural Equation Models with Missing Data: Choosing an Adjusted Sample Size to Improve the Accuracy of Inferences

ERIC Educational Resources Information Center

Enders, Craig K.; Peugh, James L.

2004-01-01

Two methods, direct maximum likelihood (ML) and the expectation maximization (EM) algorithm, can be used to obtain ML parameter estimates for structural equation models with missing data (MD). Although the 2 methods frequently produce identical parameter estimates, it may be easier to satisfy missing at random assumptions using EM. However, no…

MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions

NASA Astrophysics Data System (ADS)

Novosad, Philip; Reader, Andrew J.

2016-06-01

Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [18F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/kernel model can also be used for effective post-reconstruction denoising, through the use of an EM-like image-space algorithm. Finally, we applied the proposed algorithm to reconstruction of real high-resolution dynamic [11C]SCH23390 data, showing promising results.

MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions.

PubMed

Novosad, Philip; Reader, Andrew J

2016-06-21

Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [(18)F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/kernel model can also be used for effective post-reconstruction denoising, through the use of an EM-like image-space algorithm. Finally, we applied the proposed algorithm to reconstruction of real high-resolution dynamic [(11)C]SCH23390 data, showing promising results.

Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting

PubMed Central

Zhao, Bo; Setsompop, Kawin; Ye, Huihui; Cauley, Stephen; Wald, Lawrence L.

2017-01-01

This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum likelihood (ML) formalism to estimate multiple parameter maps directly from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization. PMID:26915119

Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting.

PubMed

Zhao, Bo; Setsompop, Kawin; Ye, Huihui; Cauley, Stephen F; Wald, Lawrence L

2016-08-01

This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum likelihood (ML) formalism to estimate multiple MR tissue parameter maps directly from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization.

Multi-pinhole SPECT Imaging with Silicon Strip Detectors

PubMed Central

Peterson, Todd E.; Shokouhi, Sepideh; Furenlid, Lars R.; Wilson, Donald W.

2010-01-01

Silicon double-sided strip detectors offer outstanding instrinsic spatial resolution with reasonable detection efficiency for iodine-125 emissions. This spatial resolution allows for multiple-pinhole imaging at low magnification, minimizing the problem of multiplexing. We have conducted imaging studies using a prototype system that utilizes a detector of 300-micrometer thickness and 50-micrometer strip pitch together with a 23-pinhole collimator. These studies include an investigation of the synthetic-collimator imaging approach, which combines multiple-pinhole projections acquired at multiple magnifications to obtain tomographic reconstructions from limited-angle data using the ML-EM algorithm. Sub-millimeter spatial resolution was obtained, demonstrating the basic validity of this approach. PMID:20953300

Optimizing convergence rates of alternating minimization reconstruction algorithms for real-time explosive detection applications

NASA Astrophysics Data System (ADS)

Bosch, Carl; Degirmenci, Soysal; Barlow, Jason; Mesika, Assaf; Politte, David G.; O'Sullivan, Joseph A.

2016-05-01

X-ray computed tomography reconstruction for medical, security and industrial applications has evolved through 40 years of experience with rotating gantry scanners using analytic reconstruction techniques such as filtered back projection (FBP). In parallel, research into statistical iterative reconstruction algorithms has evolved to apply to sparse view scanners in nuclear medicine, low data rate scanners in Positron Emission Tomography (PET) [5, 7, 10] and more recently to reduce exposure to ionizing radiation in conventional X-ray CT scanners. Multiple approaches to statistical iterative reconstruction have been developed based primarily on variations of expectation maximization (EM) algorithms. The primary benefit of EM algorithms is the guarantee of convergence that is maintained when iterative corrections are made within the limits of convergent algorithms. The primary disadvantage, however is that strict adherence to correction limits of convergent algorithms extends the number of iterations and ultimate timeline to complete a 3D volumetric reconstruction. Researchers have studied methods to accelerate convergence through more aggressive corrections [1], ordered subsets [1, 3, 4, 9] and spatially variant image updates. In this paper we describe the development of an AM reconstruction algorithm with accelerated convergence for use in a real-time explosive detection application for aviation security. By judiciously applying multiple acceleration techniques and advanced GPU processing architectures, we are able to perform 3D reconstruction of scanned passenger baggage at a rate of 75 slices per second. Analysis of the results on stream of commerce passenger bags demonstrates accelerated convergence by factors of 8 to 15, when comparing images from accelerated and strictly convergent algorithms.

Direct 4D reconstruction of parametric images incorporating anato-functional joint entropy.

PubMed

Tang, Jing; Kuwabara, Hiroto; Wong, Dean F; Rahmim, Arman

2010-08-07

We developed an anatomy-guided 4D closed-form algorithm to directly reconstruct parametric images from projection data for (nearly) irreversible tracers. Conventional methods consist of individually reconstructing 2D/3D PET data, followed by graphical analysis on the sequence of reconstructed image frames. The proposed direct reconstruction approach maintains the simplicity and accuracy of the expectation-maximization (EM) algorithm by extending the system matrix to include the relation between the parametric images and the measured data. A closed-form solution was achieved using a different hidden complete-data formulation within the EM framework. Furthermore, the proposed method was extended to maximum a posterior reconstruction via incorporation of MR image information, taking the joint entropy between MR and parametric PET features as the prior. Using realistic simulated noisy [(11)C]-naltrindole PET and MR brain images/data, the quantitative performance of the proposed methods was investigated. Significant improvements in terms of noise versus bias performance were demonstrated when performing direct parametric reconstruction, and additionally upon extending the algorithm to its Bayesian counterpart using the MR-PET joint entropy measure.

Sparse-view proton computed tomography using modulated proton beams.

PubMed

Lee, Jiseoc; Kim, Changhwan; Min, Byungjun; Kwak, Jungwon; Park, Seyjoon; Lee, Se Byeong; Park, Sungyong; Cho, Seungryong

2015-02-01

Proton imaging that uses a modulated proton beam and an intensity detector allows a relatively fast image acquisition compared to the imaging approach based on a trajectory tracking detector. In addition, it requires a relatively simple implementation in a conventional proton therapy equipment. The model of geometric straight ray assumed in conventional computed tomography (CT) image reconstruction is however challenged by multiple-Coulomb scattering and energy straggling in the proton imaging. Radiation dose to the patient is another important issue that has to be taken care of for practical applications. In this work, the authors have investigated iterative image reconstructions after a deconvolution of the sparsely view-sampled data to address these issues in proton CT. Proton projection images were acquired using the modulated proton beams and the EBT2 film as an intensity detector. Four electron-density cylinders representing normal soft tissues and bone were used as imaged object and scanned at 40 views that are equally separated over 360°. Digitized film images were converted to water-equivalent thickness by use of an empirically derived conversion curve. For improving the image quality, a deconvolution-based image deblurring with an empirically acquired point spread function was employed. They have implemented iterative image reconstruction algorithms such as adaptive steepest descent-projection onto convex sets (ASD-POCS), superiorization method-projection onto convex sets (SM-POCS), superiorization method-expectation maximization (SM-EM), and expectation maximization-total variation minimization (EM-TV). Performance of the four image reconstruction algorithms was analyzed and compared quantitatively via contrast-to-noise ratio (CNR) and root-mean-square-error (RMSE). Objects of higher electron density have been reconstructed more accurately than those of lower density objects. The bone, for example, has been reconstructed within 1% error. EM-based algorithms produced an increased image noise and RMSE as the iteration reaches about 20, while the POCS-based algorithms showed a monotonic convergence with iterations. The ASD-POCS algorithm outperformed the others in terms of CNR, RMSE, and the accuracy of the reconstructed relative stopping power in the region of lung and soft tissues. The four iterative algorithms, i.e., ASD-POCS, SM-POCS, SM-EM, and EM-TV, have been developed and applied for proton CT image reconstruction. Although it still seems that the images need to be improved for practical applications to the treatment planning, proton CT imaging by use of the modulated beams in sparse-view sampling has demonstrated its feasibility.

Directly Reconstructing Principal Components of Heterogeneous Particles from Cryo-EM Images

PubMed Central

Tagare, Hemant D.; Kucukelbir, Alp; Sigworth, Fred J.; Wang, Hongwei; Rao, Murali

2015-01-01

Structural heterogeneity of particles can be investigated by their three-dimensional principal components. This paper addresses the question of whether, and with what algorithm, the three-dimensional principal components can be directly recovered from cryo-EM images. The first part of the paper extends the Fourier slice theorem to covariance functions showing that the three-dimensional covariance, and hence the principal components, of a heterogeneous particle can indeed be recovered from two-dimensional cryo-EM images. The second part of the paper proposes a practical algorithm for reconstructing the principal components directly from cryo-EM images without the intermediate step of calculating covariances. This algorithm is based on maximizing the (posterior) likelihood using the Expectation-Maximization algorithm. The last part of the paper applies this algorithm to simulated data and to two real cryo-EM data sets: a data set of the 70S ribosome with and without Elongation Factor-G (EF-G), and a data set of the inluenza virus RNA dependent RNA Polymerase (RdRP). The first principal component of the 70S ribosome data set reveals the expected conformational changes of the ribosome as the EF-G binds and unbinds. The first principal component of the RdRP data set reveals a conformational change in the two dimers of the RdRP. PMID:26049077

DOE Office of Scientific and Technical Information (OSTI.GOV)

The purpose of the computer program is to generate system matrices that model data acquisition process in dynamic single photon emission computed tomography (SPECT). The application is for the reconstruction of dynamic data from projection measurements that provide the time evolution of activity uptake and wash out in an organ of interest. The measurement of the time activity in the blood and organ tissue provide time-activity curves (TACs) that are used to estimate kinetic parameters. The program provides a correct model of the in vivo spatial and temporal distribution of radioactive in organs. The model accounts for the attenuation ofmore » the internal emitting radioactivity, it accounts for the vary point response of the collimators, and correctly models the time variation of the activity in the organs. One important application where the software is being used in a measuring the arterial input function (AIF) in a dynamic SPECT study where the data are acquired from a slow camera rotation. Measurement of the arterial input function (AIF) is essential to deriving quantitative estimates of regional myocardial blood flow using kinetic models. A study was performed to evaluate whether a slowly rotating SPECT system could provide accurate AIF's for myocardial perfusion imaging (MPI). Methods: Dynamic cardiac SPECT was first performed in human subjects at rest using a Phillips Precedence SPECT/CT scanner. Dynamic measurements of Tc-99m-tetrofosmin in the myocardium were obtained using an infusion time of 2 minutes. Blood input, myocardium tissue and liver TACs were estimated using spatiotemporal splines. These were fit to a one-compartment perfusion model to obtain wash-in rate parameters K1. Results: The spatiotemporal 4D ML-EM reconstructions gave more accurate reconstructions that did standard frame-by-frame 3D ML-EM reconstructions. From additional computer simulations and phantom studies, it was determined that a 1 minute infusion with a SPECT system rotation speed providing 180 degrees of projection data every 54s can produce measurements of blood pool and myocardial TACs. This has important application in the circulation of coronary flow reserve using rest/stress dynamic cardiac SPECT. They system matrices are used in maximum likelihood and maximum a posterior formulations in estimation theory where through iterative algorithms (conjugate gradient, expectation maximization, or maximum a posteriori probability algorithms) the solution is determined that maximizes a likelihood or a posteriori probability function.« less

Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm

PubMed Central

Xu, Yaofang; Wu, Jiayi; Yin, Chang-Cheng; Mao, Youdong

2016-01-01

In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis. PMID:27959895

Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm.

PubMed

Xu, Yaofang; Wu, Jiayi; Yin, Chang-Cheng; Mao, Youdong

2016-01-01

In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis.

Directly reconstructing principal components of heterogeneous particles from cryo-EM images.

PubMed

Tagare, Hemant D; Kucukelbir, Alp; Sigworth, Fred J; Wang, Hongwei; Rao, Murali

2015-08-01

Structural heterogeneity of particles can be investigated by their three-dimensional principal components. This paper addresses the question of whether, and with what algorithm, the three-dimensional principal components can be directly recovered from cryo-EM images. The first part of the paper extends the Fourier slice theorem to covariance functions showing that the three-dimensional covariance, and hence the principal components, of a heterogeneous particle can indeed be recovered from two-dimensional cryo-EM images. The second part of the paper proposes a practical algorithm for reconstructing the principal components directly from cryo-EM images without the intermediate step of calculating covariances. This algorithm is based on maximizing the posterior likelihood using the Expectation-Maximization algorithm. The last part of the paper applies this algorithm to simulated data and to two real cryo-EM data sets: a data set of the 70S ribosome with and without Elongation Factor-G (EF-G), and a data set of the influenza virus RNA dependent RNA Polymerase (RdRP). The first principal component of the 70S ribosome data set reveals the expected conformational changes of the ribosome as the EF-G binds and unbinds. The first principal component of the RdRP data set reveals a conformational change in the two dimers of the RdRP. Copyright © 2015 Elsevier Inc. All rights reserved.

Region of interest processing for iterative reconstruction in x-ray computed tomography

NASA Astrophysics Data System (ADS)

Kopp, Felix K.; Nasirudin, Radin A.; Mei, Kai; Fehringer, Andreas; Pfeiffer, Franz; Rummeny, Ernst J.; Noël, Peter B.

2015-03-01

The recent advancements in the graphics card technology raised the performance of parallel computing and contributed to the introduction of iterative reconstruction methods for x-ray computed tomography in clinical CT scanners. Iterative maximum likelihood (ML) based reconstruction methods are known to reduce image noise and to improve the diagnostic quality of low-dose CT. However, iterative reconstruction of a region of interest (ROI), especially ML based, is challenging. But for some clinical procedures, like cardiac CT, only a ROI is needed for diagnostics. A high-resolution reconstruction of the full field of view (FOV) consumes unnecessary computation effort that results in a slower reconstruction than clinically acceptable. In this work, we present an extension and evaluation of an existing ROI processing algorithm. Especially improvements for the equalization between regions inside and outside of a ROI are proposed. The evaluation was done on data collected from a clinical CT scanner. The performance of the different algorithms is qualitatively and quantitatively assessed. Our solution to the ROI problem provides an increase in signal-to-noise ratio and leads to visually less noise in the final reconstruction. The reconstruction speed of our technique was observed to be comparable with other previous proposed techniques. The development of ROI processing algorithms in combination with iterative reconstruction will provide higher diagnostic quality in the near future.

Acceleration of the direct reconstruction of linear parametric images using nested algorithms.

PubMed

Wang, Guobao; Qi, Jinyi

2010-03-07

Parametric imaging using dynamic positron emission tomography (PET) provides important information for biological research and clinical diagnosis. Indirect and direct methods have been developed for reconstructing linear parametric images from dynamic PET data. Indirect methods are relatively simple and easy to implement because the image reconstruction and kinetic modeling are performed in two separate steps. Direct methods estimate parametric images directly from raw PET data and are statistically more efficient. However, the convergence rate of direct algorithms can be slow due to the coupling between the reconstruction and kinetic modeling. Here we present two fast gradient-type algorithms for direct reconstruction of linear parametric images. The new algorithms decouple the reconstruction and linear parametric modeling at each iteration by employing the principle of optimization transfer. Convergence speed is accelerated by running more sub-iterations of linear parametric estimation because the computation cost of the linear parametric modeling is much less than that of the image reconstruction. Computer simulation studies demonstrated that the new algorithms converge much faster than the traditional expectation maximization (EM) and the preconditioned conjugate gradient algorithms for dynamic PET.

Experimental validation of an OSEM-type iterative reconstruction algorithm for inverse geometry computed tomography

NASA Astrophysics Data System (ADS)

David, Sabrina; Burion, Steve; Tepe, Alan; Wilfley, Brian; Menig, Daniel; Funk, Tobias

2012-03-01

Iterative reconstruction methods have emerged as a promising avenue to reduce dose in CT imaging. Another, perhaps less well-known, advance has been the development of inverse geometry CT (IGCT) imaging systems, which can significantly reduce the radiation dose delivered to a patient during a CT scan compared to conventional CT systems. Here we show that IGCT data can be reconstructed using iterative methods, thereby combining two novel methods for CT dose reduction. A prototype IGCT scanner was developed using a scanning beam digital X-ray system - an inverse geometry fluoroscopy system with a 9,000 focal spot x-ray source and small photon counting detector. 90 fluoroscopic projections or "superviews" spanning an angle of 360 degrees were acquired of an anthropomorphic phantom mimicking a 1 year-old boy. The superviews were reconstructed with a custom iterative reconstruction algorithm, based on the maximum-likelihood algorithm for transmission tomography (ML-TR). The normalization term was calculated based on flat-field data acquired without a phantom. 15 subsets were used, and a total of 10 complete iterations were performed. Initial reconstructed images showed faithful reconstruction of anatomical details. Good edge resolution and good contrast-to-noise properties were observed. Overall, ML-TR reconstruction of IGCT data collected by a bench-top prototype was shown to be viable, which may be an important milestone in the further development of inverse geometry CT.

A PET reconstruction formulation that enforces non-negativity in projection space for bias reduction in Y-90 imaging

NASA Astrophysics Data System (ADS)

Lim, Hongki; Dewaraja, Yuni K.; Fessler, Jeffrey A.

2018-02-01

Most existing PET image reconstruction methods impose a nonnegativity constraint in the image domain that is natural physically, but can lead to biased reconstructions. This bias is particularly problematic for Y-90 PET because of the low probability positron production and high random coincidence fraction. This paper investigates a new PET reconstruction formulation that enforces nonnegativity of the projections instead of the voxel values. This formulation allows some negative voxel values, thereby potentially reducing bias. Unlike the previously reported NEG-ML approach that modifies the Poisson log-likelihood to allow negative values, the new formulation retains the classical Poisson statistical model. To relax the non-negativity constraint embedded in the standard methods for PET reconstruction, we used an alternating direction method of multipliers (ADMM). Because choice of ADMM parameters can greatly influence convergence rate, we applied an automatic parameter selection method to improve the convergence speed. We investigated the methods using lung to liver slices of XCAT phantom. We simulated low true coincidence count-rates with high random fractions corresponding to the typical values from patient imaging in Y-90 microsphere radioembolization. We compared our new methods with standard reconstruction algorithms and NEG-ML and a regularized version thereof. Both our new method and NEG-ML allow more accurate quantification in all volumes of interest while yielding lower noise than the standard method. The performance of NEG-ML can degrade when its user-defined parameter is tuned poorly, while the proposed algorithm is robust to any count level without requiring parameter tuning.

Investigation of contrast-enhanced subtracted breast CT images with MAP-EM based on projection-based weighting imaging.

PubMed

Zhou, Zhengdong; Guan, Shaolin; Xin, Runchao; Li, Jianbo

2018-06-01

Contrast-enhanced subtracted breast computer tomography (CESBCT) images acquired using energy-resolved photon counting detector can be helpful to enhance the visibility of breast tumors. In such technology, one challenge is the limited number of photons in each energy bin, thereby possibly leading to high noise in separate images from each energy bin, the projection-based weighted image, and the subtracted image. In conventional low-dose CT imaging, iterative image reconstruction provides a superior signal-to-noise compared with the filtered back projection (FBP) algorithm. In this paper, maximum a posteriori expectation maximization (MAP-EM) based on projection-based weighting imaging for reconstruction of CESBCT images acquired using an energy-resolving photon counting detector is proposed, and its performance was investigated in terms of contrast-to-noise ratio (CNR). The simulation study shows that MAP-EM based on projection-based weighting imaging can improve the CNR in CESBCT images by 117.7%-121.2% compared with FBP based on projection-based weighting imaging method. When compared with the energy-integrating imaging that uses the MAP-EM algorithm, projection-based weighting imaging that uses the MAP-EM algorithm can improve the CNR of CESBCT images by 10.5%-13.3%. In conclusion, MAP-EM based on projection-based weighting imaging shows significant improvement the CNR of the CESBCT image compared with FBP based on projection-based weighting imaging, and MAP-EM based on projection-based weighting imaging outperforms MAP-EM based on energy-integrating imaging for CESBCT imaging.

Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation-maximization reconstruction

PubMed Central

Karakatsanis, Nicolas A.; Casey, Michael E.; Lodge, Martin A.; Rahmim, Arman; Zaidi, Habib

2016-01-01

Whole-body (WB) dynamic PET has recently demonstrated its potential in translating the quantitative benefits of parametric imaging to the clinic. Post-reconstruction standard Patlak (sPatlak) WB graphical analysis utilizes multi-bed multi-pass PET acquisition to produce quantitative WB images of the tracer influx rate Ki as a complimentary metric to the semi-quantitative standardized uptake value (SUV). The resulting Ki images may suffer from high noise due to the need for short acquisition frames. Meanwhile, a generalized Patlak (gPatlak) WB post-reconstruction method had been suggested to limit Ki bias of sPatlak analysis at regions with non-negligible 18F-FDG uptake reversibility; however, gPatlak analysis is non-linear and thus can further amplify noise. In the present study, we implemented, within the open-source Software for Tomographic Image Reconstruction (STIR) platform, a clinically adoptable 4D WB reconstruction framework enabling efficient estimation of sPatlak and gPatlak images directly from dynamic multi-bed PET raw data with substantial noise reduction. Furthermore, we employed the optimization transfer methodology to accelerate 4D expectation-maximization (EM) convergence by nesting the fast image-based estimation of Patlak parameters within each iteration cycle of the slower projection-based estimation of dynamic PET images. The novel gPatlak 4D method was initialized from an optimized set of sPatlak ML-EM iterations to facilitate EM convergence. Initially, realistic simulations were conducted utilizing published 18F-FDG kinetic parameters coupled with the XCAT phantom. Quantitative analyses illustrated enhanced Ki target-to-background ratio (TBR) and especially contrast-to-noise ratio (CNR) performance for the 4D vs. the indirect methods and static SUV. Furthermore, considerable convergence acceleration was observed for the nested algorithms involving 10–20 sub-iterations. Moreover, systematic reduction in Ki % bias and improved TBR were observed for gPatlak vs. sPatlak. Finally, validation on clinical WB dynamic data demonstrated the clinical feasibility and superior Ki CNR performance for the proposed 4D framework compared to indirect Patlak and SUV imaging. PMID:27383991

Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation-maximization reconstruction

NASA Astrophysics Data System (ADS)

Karakatsanis, Nicolas A.; Casey, Michael E.; Lodge, Martin A.; Rahmim, Arman; Zaidi, Habib

2016-08-01

Whole-body (WB) dynamic PET has recently demonstrated its potential in translating the quantitative benefits of parametric imaging to the clinic. Post-reconstruction standard Patlak (sPatlak) WB graphical analysis utilizes multi-bed multi-pass PET acquisition to produce quantitative WB images of the tracer influx rate K i as a complimentary metric to the semi-quantitative standardized uptake value (SUV). The resulting K i images may suffer from high noise due to the need for short acquisition frames. Meanwhile, a generalized Patlak (gPatlak) WB post-reconstruction method had been suggested to limit K i bias of sPatlak analysis at regions with non-negligible 18F-FDG uptake reversibility; however, gPatlak analysis is non-linear and thus can further amplify noise. In the present study, we implemented, within the open-source software for tomographic image reconstruction platform, a clinically adoptable 4D WB reconstruction framework enabling efficient estimation of sPatlak and gPatlak images directly from dynamic multi-bed PET raw data with substantial noise reduction. Furthermore, we employed the optimization transfer methodology to accelerate 4D expectation-maximization (EM) convergence by nesting the fast image-based estimation of Patlak parameters within each iteration cycle of the slower projection-based estimation of dynamic PET images. The novel gPatlak 4D method was initialized from an optimized set of sPatlak ML-EM iterations to facilitate EM convergence. Initially, realistic simulations were conducted utilizing published 18F-FDG kinetic parameters coupled with the XCAT phantom. Quantitative analyses illustrated enhanced K i target-to-background ratio (TBR) and especially contrast-to-noise ratio (CNR) performance for the 4D versus the indirect methods and static SUV. Furthermore, considerable convergence acceleration was observed for the nested algorithms involving 10-20 sub-iterations. Moreover, systematic reduction in K i % bias and improved TBR were observed for gPatlak versus sPatlak. Finally, validation on clinical WB dynamic data demonstrated the clinical feasibility and superior K i CNR performance for the proposed 4D framework compared to indirect Patlak and SUV imaging.

Blind deconvolution of 2-D and 3-D fluorescent micrographs

NASA Astrophysics Data System (ADS)

Krishnamurthi, Vijaykumar; Liu, Yi-Hwa; Holmes, Timothy J.; Roysam, Badrinath; Turner, James N.

1992-06-01

This paper presents recent results of our reconstructions of 3-D data from Drosophila chromosomes as well as our simulations with a refined version of the algorithm used in the former. It is well known that the calibration of the point spread function (PSF) of a fluorescence microscope is a tedious process and involves esoteric techniques in most cases. This problem is further compounded in the case of confocal microscopy where the measured intensities are usually low. A number of techniques have been developed to solve this problem, all of which are methods in blind deconvolution. These are so called because the measured PSF is not required in the deconvolution of degraded images from any optical system. Our own efforts in this area involved the maximum likelihood (ML) method, the numerical solution to which is obtained by the expectation maximization (EM) algorithm. Based on the reasonable early results obtained during our simulations with 2-D phantoms, we carried out experiments with real 3-D data. We found that the blind deconvolution method using the ML approach gave reasonable reconstructions. Next we tried to perform the reconstructions using some 2-D data, but we found that the results were not encouraging. We surmised that the poor reconstructions were primarily due to the large values of dark current in the input data. This, coupled with the fact that we are likely to have similar data with considerable dark current from a confocal microscope prompted us to look into ways of constraining the solution of the PSF. We observed that in the 2-D case, the reconstructed PSF has a tendency to retain values larger than those of the theoretical PSF in regions away from the center (outside of those we considered to be its region of support). This observation motivated us to apply an upper bound constraint on the PSF in these regions. Furthermore, we constrain the solution of the PSF to be a bandlimited function, as in the case in the true situation. We have derived two separate approaches for implementing the constraint. One approach involves the mathematical rigors of Lagrange multipliers. This approach is discussed in another paper. The second approach involves an adaptation of the Gershberg Saxton algorithm, which ensures bandlimitedness and non-negativity of the PSF. Although the latter approach is mathematically less rigorous than the former, we currently favor it because it has a simpler implementation on a computer and has smaller memory requirements. The next section describes briefly the theory and derivation of these constraint equations using Lagrange multipliers.

Evaluation of dynamic row-action maximum likelihood algorithm reconstruction for quantitative 15O brain PET.

PubMed

Ibaraki, Masanobu; Sato, Kaoru; Mizuta, Tetsuro; Kitamura, Keishi; Miura, Shuichi; Sugawara, Shigeki; Shinohara, Yuki; Kinoshita, Toshibumi

2009-09-01

A modified version of row-action maximum likelihood algorithm (RAMLA) using a 'subset-dependent' relaxation parameter for noise suppression, or dynamic RAMLA (DRAMA), has been proposed. The aim of this study was to assess the capability of DRAMA reconstruction for quantitative (15)O brain positron emission tomography (PET). Seventeen healthy volunteers were studied using a 3D PET scanner. The PET study included 3 sequential PET scans for C(15)O, (15)O(2) and H (2) (15) O. First, the number of main iterations (N (it)) in DRAMA was optimized in relation to image convergence and statistical image noise. To estimate the statistical variance of reconstructed images on a pixel-by-pixel basis, a sinogram bootstrap method was applied using list-mode PET data. Once the optimal N (it) was determined, statistical image noise and quantitative parameters, i.e., cerebral blood flow (CBF), cerebral blood volume (CBV), cerebral metabolic rate of oxygen (CMRO(2)) and oxygen extraction fraction (OEF) were compared between DRAMA and conventional FBP. DRAMA images were post-filtered so that their spatial resolutions were matched with FBP images with a 6-mm FWHM Gaussian filter. Based on the count recovery data, N (it) = 3 was determined as an optimal parameter for (15)O PET data. The sinogram bootstrap analysis revealed that DRAMA reconstruction resulted in less statistical noise, especially in a low-activity region compared to FBP. Agreement of quantitative values between FBP and DRAMA was excellent. For DRAMA images, average gray matter values of CBF, CBV, CMRO(2) and OEF were 46.1 +/- 4.5 (mL/100 mL/min), 3.35 +/- 0.40 (mL/100 mL), 3.42 +/- 0.35 (mL/100 mL/min) and 42.1 +/- 3.8 (%), respectively. These values were comparable to corresponding values with FBP images: 46.6 +/- 4.6 (mL/100 mL/min), 3.34 +/- 0.39 (mL/100 mL), 3.48 +/- 0.34 (mL/100 mL/min) and 42.4 +/- 3.8 (%), respectively. DRAMA reconstruction is applicable to quantitative (15)O PET study and is superior to conventional FBP in terms of image quality.

Single-particle cryo-EM-Improved ab initio 3D reconstruction with SIMPLE/PRIME.

PubMed

Reboul, Cyril F; Eager, Michael; Elmlund, Dominika; Elmlund, Hans

2018-01-01

Cryogenic electron microscopy (cryo-EM) and single-particle analysis now enables the determination of high-resolution structures of macromolecular assemblies that have resisted X-ray crystallography and other approaches. We developed the SIMPLE open-source image-processing suite for analysing cryo-EM images of single-particles. A core component of SIMPLE is the probabilistic PRIME algorithm for identifying clusters of images in 2D and determine relative orientations of single-particle projections in 3D. Here, we extend our previous work on PRIME and introduce new stochastic optimization algorithms that improve the robustness of the approach. Our refined method for identification of homogeneous subsets of images in accurate register substantially improves the resolution of the cluster centers and of the ab initio 3D reconstructions derived from them. We now obtain maps with a resolution better than 10 Å by exclusively processing cluster centers. Excellent parallel code performance on over-the-counter laptops and CPU workstations is demonstrated. © 2017 The Protein Society.

SPECT reconstruction using DCT-induced tight framelet regularization

NASA Astrophysics Data System (ADS)

Zhang, Jiahan; Li, Si; Xu, Yuesheng; Schmidtlein, C. R.; Lipson, Edward D.; Feiglin, David H.; Krol, Andrzej

2015-03-01

Wavelet transforms have been successfully applied in many fields of image processing. Yet, to our knowledge, they have never been directly incorporated to the objective function in Emission Computed Tomography (ECT) image reconstruction. Our aim has been to investigate if the ℓ1-norm of non-decimated discrete cosine transform (DCT) coefficients of the estimated radiotracer distribution could be effectively used as the regularization term for the penalized-likelihood (PL) reconstruction, where a regularizer is used to enforce the image smoothness in the reconstruction. In this study, the ℓ1-norm of 2D DCT wavelet decomposition was used as a regularization term. The Preconditioned Alternating Projection Algorithm (PAPA), which we proposed in earlier work to solve penalized likelihood (PL) reconstruction with non-differentiable regularizers, was used to solve this optimization problem. The DCT wavelet decompositions were performed on the transaxial reconstructed images. We reconstructed Monte Carlo simulated SPECT data obtained for a numerical phantom with Gaussian blobs as hot lesions and with a warm random lumpy background. Reconstructed images using the proposed method exhibited better noise suppression and improved lesion conspicuity, compared with images reconstructed using expectation maximization (EM) algorithm with Gaussian post filter (GPF). Also, the mean square error (MSE) was smaller, compared with EM-GPF. A critical and challenging aspect of this method was selection of optimal parameters. In summary, our numerical experiments demonstrated that the ℓ1-norm of discrete cosine transform (DCT) wavelet frame transform DCT regularizer shows promise for SPECT image reconstruction using PAPA method.

Viewing Angle Classification of Cryo-Electron Microscopy Images Using Eigenvectors

PubMed Central

Singer, A.; Zhao, Z.; Shkolnisky, Y.; Hadani, R.

2012-01-01

The cryo-electron microscopy (cryo-EM) reconstruction problem is to find the three-dimensional structure of a macromolecule given noisy versions of its two-dimensional projection images at unknown random directions. We introduce a new algorithm for identifying noisy cryo-EM images of nearby viewing angles. This identification is an important first step in three-dimensional structure determination of macromolecules from cryo-EM, because once identified, these images can be rotationally aligned and averaged to produce “class averages” of better quality. The main advantage of our algorithm is its extreme robustness to noise. The algorithm is also very efficient in terms of running time and memory requirements, because it is based on the computation of the top few eigenvectors of a specially designed sparse Hermitian matrix. These advantages are demonstrated in numerous numerical experiments. PMID:22506089

Effective noise-suppressed and artifact-reduced reconstruction of SPECT data using a preconditioned alternating projection algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Si; Xu, Yuesheng, E-mail: yxu06@syr.edu; Zhang, Jiahan

Purpose: The authors have recently developed a preconditioned alternating projection algorithm (PAPA) with total variation (TV) regularizer for solving the penalized-likelihood optimization model for single-photon emission computed tomography (SPECT) reconstruction. This algorithm belongs to a novel class of fixed-point proximity methods. The goal of this work is to investigate how PAPA performs while dealing with realistic noisy SPECT data, to compare its performance with more conventional methods, and to address issues with TV artifacts by proposing a novel form of the algorithm invoking high-order TV regularization, denoted as HOTV-PAPA, which has been explored and studied extensively in the present work.more » Methods: Using Monte Carlo methods, the authors simulate noisy SPECT data from two water cylinders; one contains lumpy “warm” background and “hot” lesions of various sizes with Gaussian activity distribution, and the other is a reference cylinder without hot lesions. The authors study the performance of HOTV-PAPA and compare it with PAPA using first-order TV regularization (TV-PAPA), the Panin–Zeng–Gullberg one-step-late method with TV regularization (TV-OSL), and an expectation–maximization algorithm with Gaussian postfilter (GPF-EM). The authors select penalty-weights (hyperparameters) by qualitatively balancing the trade-off between resolution and image noise separately for TV-PAPA and TV-OSL. However, the authors arrived at the same penalty-weight value for both of them. The authors set the first penalty-weight in HOTV-PAPA equal to the optimal penalty-weight found for TV-PAPA. The second penalty-weight needed for HOTV-PAPA is tuned by balancing resolution and the severity of staircase artifacts. The authors adjust the Gaussian postfilter to approximately match the local point spread function of GPF-EM and HOTV-PAPA. The authors examine hot lesion detectability, study local spatial resolution, analyze background noise properties, estimate mean square errors (MSEs), and report the convergence speed and computation time. Results: HOTV-PAPA yields the best signal-to-noise ratio, followed by TV-PAPA and TV-OSL/GPF-EM. The local spatial resolution of HOTV-PAPA is somewhat worse than that of TV-PAPA and TV-OSL. Images reconstructed using HOTV-PAPA have the lowest local noise power spectrum (LNPS) amplitudes, followed by TV-PAPA, TV-OSL, and GPF-EM. The LNPS peak of GPF-EM is shifted toward higher spatial frequencies than those for the three other methods. The PAPA-type methods exhibit much lower ensemble noise, ensemble voxel variance, and image roughness. HOTV-PAPA performs best in these categories. Whereas images reconstructed using both TV-PAPA and TV-OSL are degraded by severe staircase artifacts; HOTV-PAPA substantially reduces such artifacts. It also converges faster than the other three methods and exhibits the lowest overall reconstruction error level, as measured by MSE. Conclusions: For high-noise simulated SPECT data, HOTV-PAPA outperforms TV-PAPA, GPF-EM, and TV-OSL in terms of hot lesion detectability, noise suppression, MSE, and computational efficiency. Unlike TV-PAPA and TV-OSL, HOTV-PAPA does not create sizable staircase artifacts. Moreover, HOTV-PAPA effectively suppresses noise, with only limited loss of local spatial resolution. Of the four methods, HOTV-PAPA shows the best lesion detectability, thanks to its superior noise suppression. HOTV-PAPA shows promise for clinically useful reconstructions of low-dose SPECT data.« less

Effective noise-suppressed and artifact-reduced reconstruction of SPECT data using a preconditioned alternating projection algorithm

PubMed Central

Li, Si; Zhang, Jiahan; Krol, Andrzej; Schmidtlein, C. Ross; Vogelsang, Levon; Shen, Lixin; Lipson, Edward; Feiglin, David; Xu, Yuesheng

2015-01-01

Purpose: The authors have recently developed a preconditioned alternating projection algorithm (PAPA) with total variation (TV) regularizer for solving the penalized-likelihood optimization model for single-photon emission computed tomography (SPECT) reconstruction. This algorithm belongs to a novel class of fixed-point proximity methods. The goal of this work is to investigate how PAPA performs while dealing with realistic noisy SPECT data, to compare its performance with more conventional methods, and to address issues with TV artifacts by proposing a novel form of the algorithm invoking high-order TV regularization, denoted as HOTV-PAPA, which has been explored and studied extensively in the present work. Methods: Using Monte Carlo methods, the authors simulate noisy SPECT data from two water cylinders; one contains lumpy “warm” background and “hot” lesions of various sizes with Gaussian activity distribution, and the other is a reference cylinder without hot lesions. The authors study the performance of HOTV-PAPA and compare it with PAPA using first-order TV regularization (TV-PAPA), the Panin–Zeng–Gullberg one-step-late method with TV regularization (TV-OSL), and an expectation–maximization algorithm with Gaussian postfilter (GPF-EM). The authors select penalty-weights (hyperparameters) by qualitatively balancing the trade-off between resolution and image noise separately for TV-PAPA and TV-OSL. However, the authors arrived at the same penalty-weight value for both of them. The authors set the first penalty-weight in HOTV-PAPA equal to the optimal penalty-weight found for TV-PAPA. The second penalty-weight needed for HOTV-PAPA is tuned by balancing resolution and the severity of staircase artifacts. The authors adjust the Gaussian postfilter to approximately match the local point spread function of GPF-EM and HOTV-PAPA. The authors examine hot lesion detectability, study local spatial resolution, analyze background noise properties, estimate mean square errors (MSEs), and report the convergence speed and computation time. Results: HOTV-PAPA yields the best signal-to-noise ratio, followed by TV-PAPA and TV-OSL/GPF-EM. The local spatial resolution of HOTV-PAPA is somewhat worse than that of TV-PAPA and TV-OSL. Images reconstructed using HOTV-PAPA have the lowest local noise power spectrum (LNPS) amplitudes, followed by TV-PAPA, TV-OSL, and GPF-EM. The LNPS peak of GPF-EM is shifted toward higher spatial frequencies than those for the three other methods. The PAPA-type methods exhibit much lower ensemble noise, ensemble voxel variance, and image roughness. HOTV-PAPA performs best in these categories. Whereas images reconstructed using both TV-PAPA and TV-OSL are degraded by severe staircase artifacts; HOTV-PAPA substantially reduces such artifacts. It also converges faster than the other three methods and exhibits the lowest overall reconstruction error level, as measured by MSE. Conclusions: For high-noise simulated SPECT data, HOTV-PAPA outperforms TV-PAPA, GPF-EM, and TV-OSL in terms of hot lesion detectability, noise suppression, MSE, and computational efficiency. Unlike TV-PAPA and TV-OSL, HOTV-PAPA does not create sizable staircase artifacts. Moreover, HOTV-PAPA effectively suppresses noise, with only limited loss of local spatial resolution. Of the four methods, HOTV-PAPA shows the best lesion detectability, thanks to its superior noise suppression. HOTV-PAPA shows promise for clinically useful reconstructions of low-dose SPECT data. PMID:26233214

Statistical inference approach to structural reconstruction of complex networks from binary time series

NASA Astrophysics Data System (ADS)

Ma, Chuang; Chen, Han-Shuang; Lai, Ying-Cheng; Zhang, Hai-Feng

2018-02-01

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains challenging. We articulate a statistical inference based approach to this problem. In particular, exploiting the expectation-maximization (EM) algorithm, we develop a method to ascertain the neighbors of any node in the network based solely on binary data, thereby recovering the full topology of the network. A key ingredient of our method is the maximum-likelihood estimation of the probabilities associated with actual or nonexistent links, and we show that the EM algorithm can distinguish the two kinds of probability values without any ambiguity, insofar as the length of the available binary time series is reasonably long. Our method does not require any a priori knowledge of the detailed dynamical processes, is parameter-free, and is capable of accurate reconstruction even in the presence of noise. We demonstrate the method using combinations of distinct types of binary dynamical processes and network topologies, and provide a physical understanding of the underlying reconstruction mechanism. Our statistical inference based reconstruction method contributes an additional piece to the rapidly expanding "toolbox" of data based reverse engineering of complex networked systems.

Statistical inference approach to structural reconstruction of complex networks from binary time series.

PubMed

Ma, Chuang; Chen, Han-Shuang; Lai, Ying-Cheng; Zhang, Hai-Feng

2018-02-01

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains challenging. We articulate a statistical inference based approach to this problem. In particular, exploiting the expectation-maximization (EM) algorithm, we develop a method to ascertain the neighbors of any node in the network based solely on binary data, thereby recovering the full topology of the network. A key ingredient of our method is the maximum-likelihood estimation of the probabilities associated with actual or nonexistent links, and we show that the EM algorithm can distinguish the two kinds of probability values without any ambiguity, insofar as the length of the available binary time series is reasonably long. Our method does not require any a priori knowledge of the detailed dynamical processes, is parameter-free, and is capable of accurate reconstruction even in the presence of noise. We demonstrate the method using combinations of distinct types of binary dynamical processes and network topologies, and provide a physical understanding of the underlying reconstruction mechanism. Our statistical inference based reconstruction method contributes an additional piece to the rapidly expanding "toolbox" of data based reverse engineering of complex networked systems.

EM reconstruction of dual isotope PET using staggered injections and prompt gamma positron emitters

PubMed Central

Andreyev, Andriy; Sitek, Arkadiusz; Celler, Anna

2014-01-01

Purpose: The aim of dual isotope positron emission tomography (DIPET) is to create two separate images of two coinjected PET radiotracers. DIPET shortens the duration of the study, reduces patient discomfort, and produces perfectly coregistered images compared to the case when two radiotracers would be imaged independently (sequential PET studies). Reconstruction of data from such simultaneous acquisition of two PET radiotracers is difficult because positron decay of any isotope creates only 511 keV photons; therefore, the isotopes cannot be differentiated based on the detected energy. Methods: Recently, the authors have proposed a DIPET technique that uses a combination of radiotracer A which is a pure positron emitter (such as 18F or 11C) and radiotracer B in which positron decay is accompanied by the emission of a high-energy (HE) prompt gamma (such as 38K or 60Cu). Events that are detected as triple coincidences of HE gammas with the corresponding two 511 keV photons allow the authors to identify the lines-of-response (LORs) of isotope B. These LORs are used to separate the two intertwined distributions, using a dedicated image reconstruction algorithm. In this work the authors propose a new version of the DIPET EM-based reconstruction algorithm that allows the authors to include an additional, independent estimate of radiotracer A distribution which may be obtained if radioisotopes are administered using a staggered injections method. In this work the method is tested on simple simulations of static PET acquisitions. Results: The authors’ experiments performed using Monte-Carlo simulations with static acquisitions demonstrate that the combined method provides better results (crosstalk errors decrease by up to 50%) than the positron-gamma DIPET method or staggered injections alone. Conclusions: The authors demonstrate that the authors’ new EM algorithm which combines information from triple coincidences with prompt gammas and staggered injections improves the accuracy of DIPET reconstructions for static acquisitions so they reach almost the benchmark level calculated for perfectly separated tracers. PMID:24506645

SPECT data acquisition and image reconstruction in a stationary small animal SPECT/MRI system

NASA Astrophysics Data System (ADS)

Xu, Jingyan; Chen, Si; Yu, Jianhua; Meier, Dirk; Wagenaar, Douglas J.; Patt, Bradley E.; Tsui, Benjamin M. W.

2010-04-01

The goal of the study was to investigate data acquisition strategies and image reconstruction methods for a stationary SPECT insert that can operate inside an MRI scanner with a 12 cm bore diameter for simultaneous SPECT/MRI imaging of small animals. The SPECT insert consists of 3 octagonal rings of 8 MR-compatible CZT detectors per ring surrounding a multi-pinhole (MPH) collimator sleeve. Each pinhole is constructed to project the field-of-view (FOV) to one CZT detector. All 24 pinholes are focused to a cylindrical FOV of 25 mm in diameter and 34 mm in length. The data acquisition strategies we evaluated were optional collimator rotations to improve tomographic sampling; and the image reconstruction methods were iterative ML-EM with and without compensation for the geometric response function (GRF) of the MPH collimator. For this purpose, we developed an analytic simulator that calculates the system matrix with the GRF models of the MPH collimator. The simulator was used to generate projection data of a digital rod phantom with pinhole aperture sizes of 1 mm and 2 mm and with different collimator rotation patterns. Iterative ML-EM reconstruction with and without GRF compensation were used to reconstruct the projection data from the central ring of 8 detectors only, and from all 24 detectors. Our results indicated that without GRF compensation and at the default design of 24 projection views, the reconstructed images had significant artifacts. Accurate GRF compensation substantially improved the reconstructed image resolution and reduced image artifacts. With accurate GRF compensation, useful reconstructed images can be obtained using 24 projection views only. This last finding potentially enables dynamic SPECT (and/or MRI) studies in small animals, one of many possible application areas of the SPECT/MRI system. Further research efforts are warranted including experimentally measuring the system matrix for improved geometrical accuracy, incorporating the co-registered MRI image in SPECT reconstruction, and exploring potential applications of the simultaneous SPECT/MRI SA system including dynamic SPECT studies.

A hybrid genetic algorithm-extreme learning machine approach for accurate significant wave height reconstruction

NASA Astrophysics Data System (ADS)

Alexandre, E.; Cuadra, L.; Nieto-Borge, J. C.; Candil-García, G.; del Pino, M.; Salcedo-Sanz, S.

2015-08-01

Wave parameters computed from time series measured by buoys (significant wave height Hs, mean wave period, etc.) play a key role in coastal engineering and in the design and operation of wave energy converters. Storms or navigation accidents can make measuring buoys break down, leading to missing data gaps. In this paper we tackle the problem of locally reconstructing Hs at out-of-operation buoys by using wave parameters from nearby buoys, based on the spatial correlation among values at neighboring buoy locations. The novelty of our approach for its potential application to problems in coastal engineering is twofold. On one hand, we propose a genetic algorithm hybridized with an extreme learning machine that selects, among the available wave parameters from the nearby buoys, a subset FnSP with nSP parameters that minimizes the Hs reconstruction error. On the other hand, we evaluate to what extent the selected parameters in subset FnSP are good enough in assisting other machine learning (ML) regressors (extreme learning machines, support vector machines and gaussian process regression) to reconstruct Hs. The results show that all the ML method explored achieve a good Hs reconstruction in the two different locations studied (Caribbean Sea and West Atlantic).

High Resolution Image Reconstruction from Projection of Low Resolution Images DIffering in Subpixel Shifts

NASA Technical Reports Server (NTRS)

Mareboyana, Manohar; Le Moigne-Stewart, Jacqueline; Bennett, Jerome

2016-01-01

In this paper, we demonstrate a simple algorithm that projects low resolution (LR) images differing in subpixel shifts on a high resolution (HR) also called super resolution (SR) grid. The algorithm is very effective in accuracy as well as time efficiency. A number of spatial interpolation techniques using nearest neighbor, inverse-distance weighted averages, Radial Basis Functions (RBF) etc. used in projection yield comparable results. For best accuracy of reconstructing SR image by a factor of two requires four LR images differing in four independent subpixel shifts. The algorithm has two steps: i) registration of low resolution images and (ii) shifting the low resolution images to align with reference image and projecting them on high resolution grid based on the shifts of each low resolution image using different interpolation techniques. Experiments are conducted by simulating low resolution images by subpixel shifts and subsampling of original high resolution image and the reconstructing the high resolution images from the simulated low resolution images. The results of accuracy of reconstruction are compared by using mean squared error measure between original high resolution image and reconstructed image. The algorithm was tested on remote sensing images and found to outperform previously proposed techniques such as Iterative Back Projection algorithm (IBP), Maximum Likelihood (ML), and Maximum a posterior (MAP) algorithms. The algorithm is robust and is not overly sensitive to the registration inaccuracies.

A general probabilistic model for group independent component analysis and its estimation methods

PubMed Central

Guo, Ying

2012-01-01

SUMMARY Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multi-subject spatio-temporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood method is used for estimating this general group ICA model. We propose two EM algorithms to obtain the ML estimates. The first method is an exact EM algorithm which provides an exact E-step and an explicit noniterative M-step. The second method is an variational approximation EM algorithm which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods. PMID:21517789

Anatomically-aided PET reconstruction using the kernel method.

PubMed

Hutchcroft, Will; Wang, Guobao; Chen, Kevin T; Catana, Ciprian; Qi, Jinyi

2016-09-21

This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.

Anatomically-aided PET reconstruction using the kernel method

NASA Astrophysics Data System (ADS)

Hutchcroft, Will; Wang, Guobao; Chen, Kevin T.; Catana, Ciprian; Qi, Jinyi

2016-09-01

This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.

Species Tree Inference Using a Mixture Model.

PubMed

Ullah, Ikram; Parviainen, Pekka; Lagergren, Jens

2015-09-01

Species tree reconstruction has been a subject of substantial research due to its central role across biology and medicine. A species tree is often reconstructed using a set of gene trees or by directly using sequence data. In either of these cases, one of the main confounding phenomena is the discordance between a species tree and a gene tree due to evolutionary events such as duplications and losses. Probabilistic methods can resolve the discordance by coestimating gene trees and the species tree but this approach poses a scalability problem for larger data sets. We present MixTreEM-DLRS: A two-phase approach for reconstructing a species tree in the presence of gene duplications and losses. In the first phase, MixTreEM, a novel structural expectation maximization algorithm based on a mixture model is used to reconstruct a set of candidate species trees, given sequence data for monocopy gene families from the genomes under study. In the second phase, PrIME-DLRS, a method based on the DLRS model (Åkerborg O, Sennblad B, Arvestad L, Lagergren J. 2009. Simultaneous Bayesian gene tree reconstruction and reconciliation analysis. Proc Natl Acad Sci U S A. 106(14):5714-5719), is used for selecting the best species tree. PrIME-DLRS can handle multicopy gene families since DLRS, apart from modeling sequence evolution, models gene duplication and loss using a gene evolution model (Arvestad L, Lagergren J, Sennblad B. 2009. The gene evolution model and computing its associated probabilities. J ACM. 56(2):1-44). We evaluate MixTreEM-DLRS using synthetic and biological data, and compare its performance with a recent genome-scale species tree reconstruction method PHYLDOG (Boussau B, Szöllősi GJ, Duret L, Gouy M, Tannier E, Daubin V. 2013. Genome-scale coestimation of species and gene trees. Genome Res. 23(2):323-330) as well as with a fast parsimony-based algorithm Duptree (Wehe A, Bansal MS, Burleigh JG, Eulenstein O. 2008. Duptree: a program for large-scale phylogenetic analyses using gene tree parsimony. Bioinformatics 24(13):1540-1541). Our method is competitive with PHYLDOG in terms of accuracy and runs significantly faster and our method outperforms Duptree in accuracy. The analysis constituted by MixTreEM without DLRS may also be used for selecting the target species tree, yielding a fast and yet accurate algorithm for larger data sets. MixTreEM is freely available at http://prime.scilifelab.se/mixtreem/. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Cosmic muon induced EM showers in NO$$\

DOE PAGES

Yadav, Nitin; Duyang, Hongyue; Shanahan, Peter; ...

2016-11-15

Here, the NuMI Off-Axis v e Appearance (NOvA) experiment is a ne appearance neutrino oscillation experiment at Fermilab. It identifies the ne signal from the electromagnetic (EM) showers induced by the electrons in the final state of neutrino interactions. Cosmic muon induced EM showers, dominated by bremsstrahlung, are abundant in NOvA far detector. We use the Cosmic Muon- Removal technique to get pure EM shower sample from bremsstrahlung muons in data. We also use Cosmic muon decay in flight EM showers which are highly pure EM showers.The large Cosmic-EM sample can be used, as data driven method, to characterize themore » EM shower signature and provides valuable checks of the simulation, reconstruction, particle identification algorithm, and calibration across the NOvA detector.« less

Cosmic muon induced EM showers in NO$$\

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yadav, Nitin; Duyang, Hongyue; Shanahan, Peter

Here, the NuMI Off-Axis v e Appearance (NOvA) experiment is a ne appearance neutrino oscillation experiment at Fermilab. It identifies the ne signal from the electromagnetic (EM) showers induced by the electrons in the final state of neutrino interactions. Cosmic muon induced EM showers, dominated by bremsstrahlung, are abundant in NOvA far detector. We use the Cosmic Muon- Removal technique to get pure EM shower sample from bremsstrahlung muons in data. We also use Cosmic muon decay in flight EM showers which are highly pure EM showers.The large Cosmic-EM sample can be used, as data driven method, to characterize themore » EM shower signature and provides valuable checks of the simulation, reconstruction, particle identification algorithm, and calibration across the NOvA detector.« less

Application of distance-dependent resolution compensation and post-reconstruction filtering for myocardial SPECT

NASA Astrophysics Data System (ADS)

Hutton, Brian F.; Lau, Yiu H.

1998-06-01

Compensation for distance-dependent resolution can be directly incorporated in maximum likelihood reconstruction. Our objective was to examine the effectiveness of this compensation using either the standard expectation maximization (EM) algorithm or an accelerated algorithm based on use of ordered subsets (OSEM). We also investigated the application of post-reconstruction filtering in combination with resolution compensation. Using the MCAT phantom, projections were simulated for data, including attenuation and distance-dependent resolution. Projection data were reconstructed using conventional EM and OSEM with subset size 2 and 4, with/without 3D compensation for detector response (CDR). Also post-reconstruction filtering (PRF) was performed using a 3D Butterworth filter of order 5 with various cutoff frequencies (0.2-). Image quality and reconstruction accuracy were improved when CDR was included. Image noise was lower with CDR for a given iteration number. PRF with cutoff frequency greater than improved noise with no reduction in recovery coefficient for myocardium but the effect was less when CDR was incorporated in the reconstruction. CDR alone provided better results than use of PRF without CDR. Results suggest that using CDR without PRF, and stopping at a small number of iterations, may provide sufficiently good results for myocardial SPECT. Similar behaviour was demonstrated for OSEM.

EM reconstruction of dual isotope PET using staggered injections and prompt gamma positron emitters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andreyev, Andriy, E-mail: andriy.andreyev-1@philips.com; Sitek, Arkadiusz; Celler, Anna

2014-02-15

Purpose: The aim of dual isotope positron emission tomography (DIPET) is to create two separate images of two coinjected PET radiotracers. DIPET shortens the duration of the study, reduces patient discomfort, and produces perfectly coregistered images compared to the case when two radiotracers would be imaged independently (sequential PET studies). Reconstruction of data from such simultaneous acquisition of two PET radiotracers is difficult because positron decay of any isotope creates only 511 keV photons; therefore, the isotopes cannot be differentiated based on the detected energy. Methods: Recently, the authors have proposed a DIPET technique that uses a combination of radiotracermore » A which is a pure positron emitter (such as{sup 18}F or {sup 11}C) and radiotracer B in which positron decay is accompanied by the emission of a high-energy (HE) prompt gamma (such as {sup 38}K or {sup 60}Cu). Events that are detected as triple coincidences of HE gammas with the corresponding two 511 keV photons allow the authors to identify the lines-of-response (LORs) of isotope B. These LORs are used to separate the two intertwined distributions, using a dedicated image reconstruction algorithm. In this work the authors propose a new version of the DIPET EM-based reconstruction algorithm that allows the authors to include an additional, independent estimate of radiotracer A distribution which may be obtained if radioisotopes are administered using a staggered injections method. In this work the method is tested on simple simulations of static PET acquisitions. Results: The authors’ experiments performed using Monte-Carlo simulations with static acquisitions demonstrate that the combined method provides better results (crosstalk errors decrease by up to 50%) than the positron-gamma DIPET method or staggered injections alone. Conclusions: The authors demonstrate that the authors’ new EM algorithm which combines information from triple coincidences with prompt gammas and staggered injections improves the accuracy of DIPET reconstructions for static acquisitions so they reach almost the benchmark level calculated for perfectly separated tracers.« less

Single-particle cryo-EM using alignment by classification (ABC): the structure of Lumbricus terrestris haemoglobin.

PubMed

Afanasyev, Pavel; Seer-Linnemayr, Charlotte; Ravelli, Raimond B G; Matadeen, Rishi; De Carlo, Sacha; Alewijnse, Bart; Portugal, Rodrigo V; Pannu, Navraj S; Schatz, Michael; van Heel, Marin

2017-09-01

Single-particle cryogenic electron microscopy (cryo-EM) can now yield near-atomic resolution structures of biological complexes. However, the reference-based alignment algorithms commonly used in cryo-EM suffer from reference bias, limiting their applicability (also known as the 'Einstein from random noise' problem). Low-dose cryo-EM therefore requires robust and objective approaches to reveal the structural information contained in the extremely noisy data, especially when dealing with small structures. A reference-free pipeline is presented for obtaining near-atomic resolution three-dimensional reconstructions from heterogeneous ('four-dimensional') cryo-EM data sets. The methodologies integrated in this pipeline include a posteriori camera correction, movie-based full-data-set contrast transfer function determination, movie-alignment algorithms, (Fourier-space) multivariate statistical data compression and unsupervised classification, 'random-startup' three-dimensional reconstructions, four-dimensional structural refinements and Fourier shell correlation criteria for evaluating anisotropic resolution. The procedures exclusively use information emerging from the data set itself, without external 'starting models'. Euler-angle assignments are performed by angular reconstitution rather than by the inherently slower projection-matching approaches. The comprehensive 'ABC-4D' pipeline is based on the two-dimensional reference-free 'alignment by classification' (ABC) approach, where similar images in similar orientations are grouped by unsupervised classification. Some fundamental differences between X-ray crystallography versus single-particle cryo-EM data collection and data processing are discussed. The structure of the giant haemoglobin from Lumbricus terrestris at a global resolution of ∼3.8 Å is presented as an example of the use of the ABC-4D procedure.

Slow-rotation dynamic SPECT with a temporal second derivative constraint.

PubMed

Humphries, T; Celler, A; Trummer, M

2011-08-01

Dynamic tracer behavior in the human body arises as a result of continuous physiological processes. Hence, the change in tracer concentration within a region of interest (ROI) should follow a smooth curve. The authors propose a modification to an existing slow-rotation dynamic SPECT reconstruction algorithm (dSPECT) with the goal of improving the smoothness of time activity curves (TACs) and other properties of the reconstructed image. The new method, denoted d2EM, imposes a constraint on the second derivative (concavity) of the TAC in every voxel of the reconstructed image, allowing it to change sign at most once. Further constraints are enforced to prevent other nonphysical behaviors from arising. The new method is compared with dSPECT using digital phantom simulations and experimental dynamic 99mTc -DTPA renal SPECT data, to assess any improvement in image quality. In both phantom simulations and healthy volunteer experiments, the d2EM method provides smoother TACs than dSPECT, with more consistent shapes in regions with dynamic behavior. Magnitudes of TACs within an ROI still vary noticeably in both dSPECT and d2EM images, but also in images produced using an OSEM approach that reconstructs each time frame individually, based on much more complete projection data. TACs produced by averaging over a region are similar using either method, even for small ROIs. Results for experimental renal data show expected behavior in images produced by both methods, with d2EM providing somewhat smoother mean TACs and more consistent TAC shapes. The d2EM method is successful in improving the smoothness of time activity curves obtained from the reconstruction, as well as improving consistency of TAC shapes within ROIs.

Test of 3D CT reconstructions by EM + TV algorithm from undersampled data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Evseev, Ivan; Ahmann, Francielle; Silva, Hamilton P. da

2013-05-06

Computerized tomography (CT) plays an important role in medical imaging for diagnosis and therapy. However, CT imaging is connected with ionization radiation exposure of patients. Therefore, the dose reduction is an essential issue in CT. In 2011, the Expectation Maximization and Total Variation Based Model for CT Reconstruction (EM+TV) was proposed. This method can reconstruct a better image using less CT projections in comparison with the usual filtered back projection (FBP) technique. Thus, it could significantly reduce the overall dose of radiation in CT. This work reports the results of an independent numerical simulation for cone beam CT geometry withmore » alternative virtual phantoms. As in the original report, the 3D CT images of 128 Multiplication-Sign 128 Multiplication-Sign 128 virtual phantoms were reconstructed. It was not possible to implement phantoms with lager dimensions because of the slowness of code execution even by the CORE i7 CPU.« less

LOR-interleaving image reconstruction for PET imaging with fractional-crystal collimation

NASA Astrophysics Data System (ADS)

Li, Yusheng; Matej, Samuel; Karp, Joel S.; Metzler, Scott D.

2015-01-01

Positron emission tomography (PET) has become an important modality in medical and molecular imaging. However, in most PET applications, the resolution is still mainly limited by the physical crystal sizes or the detector’s intrinsic spatial resolution. To achieve images with better spatial resolution in a central region of interest (ROI), we have previously proposed using collimation in PET scanners. The collimator is designed to partially mask detector crystals to detect lines of response (LORs) within fractional crystals. A sequence of collimator-encoded LORs is measured with different collimation configurations. This novel collimated scanner geometry makes the reconstruction problem challenging, as both detector and collimator effects need to be modeled to reconstruct high-resolution images from collimated LORs. In this paper, we present a LOR-interleaving (LORI) algorithm, which incorporates these effects and has the advantage of reusing existing reconstruction software, to reconstruct high-resolution images for PET with fractional-crystal collimation. We also develop a 3D ray-tracing model incorporating both the collimator and crystal penetration for simulations and reconstructions of the collimated PET. By registering the collimator-encoded LORs with the collimator configurations, high-resolution LORs are restored based on the modeled transfer matrices using the non-negative least-squares method and EM algorithm. The resolution-enhanced images are then reconstructed from the high-resolution LORs using the MLEM or OSEM algorithm. For validation, we applied the LORI method to a small-animal PET scanner, A-PET, with a specially designed collimator. We demonstrate through simulated reconstructions with a hot-rod phantom and MOBY phantom that the LORI reconstructions can substantially improve spatial resolution and quantification compared to the uncollimated reconstructions. The LORI algorithm is crucial to improve overall image quality of collimated PET, which can have significant implications in preclinical and clinical ROI imaging applications.

On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization

NASA Astrophysics Data System (ADS)

Jacobsen, T. L.; Jørgensen, M. S.; Hammer, B.

2018-01-01

Machine learning (ML) is used to derive local stability information for density functional theory calculations of systems in relation to the recently discovered SnO2 (110 )-(4 ×1 ) reconstruction. The ML model is trained on (structure, total energy) relations collected during global minimum energy search runs with an evolutionary algorithm (EA). While being built, the ML model is used to guide the EA, thereby speeding up the overall rate by which the EA succeeds. Inspection of the local atomic potentials emerging from the model further shows chemically intuitive patterns.

Comparison of reconstruction methods and quantitative accuracy in Siemens Inveon PET scanner

NASA Astrophysics Data System (ADS)

Ram Yu, A.; Kim, Jin Su; Kang, Joo Hyun; Moo Lim, Sang

2015-04-01

PET reconstruction is key to the quantification of PET data. To our knowledge, no comparative study of reconstruction methods has been performed to date. In this study, we compared reconstruction methods with various filters in terms of their spatial resolution, non-uniformities (NU), recovery coefficients (RCs), and spillover ratios (SORs). In addition, the linearity of reconstructed radioactivity between linearity of measured and true concentrations were also assessed. A Siemens Inveon PET scanner was used in this study. Spatial resolution was measured with NEMA standard by using a 1 mm3 sized 18F point source. Image quality was assessed in terms of NU, RC and SOR. To measure the effect of reconstruction algorithms and filters, data was reconstructed using FBP, 3D reprojection algorithm (3DRP), ordered subset expectation maximization 2D (OSEM 2D), and maximum a posteriori (MAP) with various filters or smoothing factors (β). To assess the linearity of reconstructed radioactivity, image quality phantom filled with 18F was used using FBP, OSEM and MAP (β =1.5 & 5 × 10-5). The highest achievable volumetric resolution was 2.31 mm3 and the highest RCs were obtained when OSEM 2D was used. SOR was 4.87% for air and 3.97% for water, obtained OSEM 2D reconstruction was used. The measured radioactivity of reconstruction image was proportional to the injected one for radioactivity below 16 MBq/ml when FBP or OSEM 2D reconstruction methods were used. By contrast, when the MAP reconstruction method was used, activity of reconstruction image increased proportionally, regardless of the amount of injected radioactivity. When OSEM 2D or FBP were used, the measured radioactivity concentration was reduced by 53% compared with true injected radioactivity for radioactivity <16 MBq/ml. The OSEM 2D reconstruction method provides the highest achievable volumetric resolution and highest RC among all the tested methods and yields a linear relation between the measured and true concentrations for radioactivity Our data collectively showed that OSEM 2D reconstruction method provides quantitatively accurate reconstructed PET data results.

Practical implementation of tetrahedral mesh reconstruction in emission tomography

PubMed Central

Boutchko, R.; Sitek, A.; Gullberg, G. T.

2014-01-01

This paper presents a practical implementation of image reconstruction on tetrahedral meshes optimized for emission computed tomography with parallel beam geometry. Tetrahedral mesh built on a point cloud is a convenient image representation method, intrinsically three-dimensional and with a multi-level resolution property. Image intensities are defined at the mesh nodes and linearly interpolated inside each tetrahedron. For the given mesh geometry, the intensities can be computed directly from tomographic projections using iterative reconstruction algorithms with a system matrix calculated using an exact analytical formula. The mesh geometry is optimized for a specific patient using a two stage process. First, a noisy image is reconstructed on a finely-spaced uniform cloud. Then, the geometry of the representation is adaptively transformed through boundary-preserving node motion and elimination. Nodes are removed in constant intensity regions, merged along the boundaries, and moved in the direction of the mean local intensity gradient in order to provide higher node density in the boundary regions. Attenuation correction and detector geometric response are included in the system matrix. Once the mesh geometry is optimized, it is used to generate the final system matrix for ML-EM reconstruction of node intensities and for visualization of the reconstructed images. In dynamic PET or SPECT imaging, the system matrix generation procedure is performed using a quasi-static sinogram, generated by summing projection data from multiple time frames. This system matrix is then used to reconstruct the individual time frame projections. Performance of the new method is evaluated by reconstructing simulated projections of the NCAT phantom and the method is then applied to dynamic SPECT phantom and patient studies and to a dynamic microPET rat study. Tetrahedral mesh-based images are compared to the standard voxel-based reconstruction for both high and low signal-to-noise ratio projection datasets. The results demonstrate that the reconstructed images represented as tetrahedral meshes based on point clouds offer image quality comparable to that achievable using a standard voxel grid while allowing substantial reduction in the number of unknown intensities to be reconstructed and reducing the noise. PMID:23588373

Practical implementation of tetrahedral mesh reconstruction in emission tomography

NASA Astrophysics Data System (ADS)

Boutchko, R.; Sitek, A.; Gullberg, G. T.

2013-05-01

This paper presents a practical implementation of image reconstruction on tetrahedral meshes optimized for emission computed tomography with parallel beam geometry. Tetrahedral mesh built on a point cloud is a convenient image representation method, intrinsically three-dimensional and with a multi-level resolution property. Image intensities are defined at the mesh nodes and linearly interpolated inside each tetrahedron. For the given mesh geometry, the intensities can be computed directly from tomographic projections using iterative reconstruction algorithms with a system matrix calculated using an exact analytical formula. The mesh geometry is optimized for a specific patient using a two stage process. First, a noisy image is reconstructed on a finely-spaced uniform cloud. Then, the geometry of the representation is adaptively transformed through boundary-preserving node motion and elimination. Nodes are removed in constant intensity regions, merged along the boundaries, and moved in the direction of the mean local intensity gradient in order to provide higher node density in the boundary regions. Attenuation correction and detector geometric response are included in the system matrix. Once the mesh geometry is optimized, it is used to generate the final system matrix for ML-EM reconstruction of node intensities and for visualization of the reconstructed images. In dynamic PET or SPECT imaging, the system matrix generation procedure is performed using a quasi-static sinogram, generated by summing projection data from multiple time frames. This system matrix is then used to reconstruct the individual time frame projections. Performance of the new method is evaluated by reconstructing simulated projections of the NCAT phantom and the method is then applied to dynamic SPECT phantom and patient studies and to a dynamic microPET rat study. Tetrahedral mesh-based images are compared to the standard voxel-based reconstruction for both high and low signal-to-noise ratio projection datasets. The results demonstrate that the reconstructed images represented as tetrahedral meshes based on point clouds offer image quality comparable to that achievable using a standard voxel grid while allowing substantial reduction in the number of unknown intensities to be reconstructed and reducing the noise.

Fast registration and reconstruction of aliased low-resolution frames by use of a modified maximum-likelihood approach.

PubMed

Alam, M S; Bognar, J G; Cain, S; Yasuda, B J

1998-03-10

During the process of microscanning a controlled vibrating mirror typically is used to produce subpixel shifts in a sequence of forward-looking infrared (FLIR) images. If the FLIR is mounted on a moving platform, such as an aircraft, uncontrolled random vibrations associated with the platform can be used to generate the shifts. Iterative techniques such as the expectation-maximization (EM) approach by means of the maximum-likelihood algorithm can be used to generate high-resolution images from multiple randomly shifted aliased frames. In the maximum-likelihood approach the data are considered to be Poisson random variables and an EM algorithm is developed that iteratively estimates an unaliased image that is compensated for known imager-system blur while it simultaneously estimates the translational shifts. Although this algorithm yields high-resolution images from a sequence of randomly shifted frames, it requires significant computation time and cannot be implemented for real-time applications that use the currently available high-performance processors. The new image shifts are iteratively calculated by evaluation of a cost function that compares the shifted and interlaced data frames with the corresponding values in the algorithm's latest estimate of the high-resolution image. We present a registration algorithm that estimates the shifts in one step. The shift parameters provided by the new algorithm are accurate enough to eliminate the need for iterative recalculation of translational shifts. Using this shift information, we apply a simplified version of the EM algorithm to estimate a high-resolution image from a given sequence of video frames. The proposed modified EM algorithm has been found to reduce significantly the computational burden when compared with the original EM algorithm, thus making it more attractive for practical implementation. Both simulation and experimental results are presented to verify the effectiveness of the proposed technique.

Single-particle cryo-EM using alignment by classification (ABC): the structure of Lumbricus terrestris haemoglobin

PubMed Central

Seer-Linnemayr, Charlotte; Ravelli, Raimond B. G.; Matadeen, Rishi; De Carlo, Sacha; Alewijnse, Bart; Portugal, Rodrigo V.; Pannu, Navraj S.; Schatz, Michael; van Heel, Marin

2017-01-01

Single-particle cryogenic electron microscopy (cryo-EM) can now yield near-atomic resolution structures of biological complexes. However, the reference-based alignment algorithms commonly used in cryo-EM suffer from reference bias, limiting their applicability (also known as the ‘Einstein from random noise’ problem). Low-dose cryo-EM therefore requires robust and objective approaches to reveal the structural information contained in the extremely noisy data, especially when dealing with small structures. A reference-free pipeline is presented for obtaining near-atomic resolution three-dimensional reconstructions from heterogeneous (‘four-dimensional’) cryo-EM data sets. The methodologies integrated in this pipeline include a posteriori camera correction, movie-based full-data-set contrast transfer function determination, movie-alignment algorithms, (Fourier-space) multivariate statistical data compression and unsupervised classification, ‘random-startup’ three-dimensional reconstructions, four-dimensional structural refinements and Fourier shell correlation criteria for evaluating anisotropic resolution. The procedures exclusively use information emerging from the data set itself, without external ‘starting models’. Euler-angle assignments are performed by angular reconstitution rather than by the inherently slower projection-matching approaches. The comprehensive ‘ABC-4D’ pipeline is based on the two-dimensional reference-free ‘alignment by classification’ (ABC) approach, where similar images in similar orientations are grouped by unsupervised classification. Some fundamental differences between X-ray crystallography versus single-particle cryo-EM data collection and data processing are discussed. The structure of the giant haemoglobin from Lumbricus terrestris at a global resolution of ∼3.8 Å is presented as an example of the use of the ABC-4D procedure. PMID:28989723

Multi-ray-based system matrix generation for 3D PET reconstruction

NASA Astrophysics Data System (ADS)

Moehrs, Sascha; Defrise, Michel; Belcari, Nicola; DelGuerra, Alberto; Bartoli, Antonietta; Fabbri, Serena; Zanetti, Gianluigi

2008-12-01

Iterative image reconstruction algorithms for positron emission tomography (PET) require a sophisticated system matrix (model) of the scanner. Our aim is to set up such a model offline for the YAP-(S)PET II small animal imaging tomograph in order to use it subsequently with standard ML-EM (maximum-likelihood expectation maximization) and OSEM (ordered subset expectation maximization) for fully three-dimensional image reconstruction. In general, the system model can be obtained analytically, via measurements or via Monte Carlo simulations. In this paper, we present the multi-ray method, which can be considered as a hybrid method to set up the system model offline. It incorporates accurate analytical (geometric) considerations as well as crystal depth and crystal scatter effects. At the same time, it has the potential to model seamlessly other physical aspects such as the positron range. The proposed method is based on multiple rays which are traced from/to the detector crystals through the image volume. Such a ray-tracing approach itself is not new; however, we derive a novel mathematical formulation of the approach and investigate the positioning of the integration (ray-end) points. First, we study single system matrix entries and show that the positioning and weighting of the ray-end points according to Gaussian integration give better results compared to equally spaced integration points (trapezoidal integration), especially if only a small number of integration points (rays) are used. Additionally, we show that, for a given variance of the single matrix entries, the number of rays (events) required to calculate the whole matrix is a factor of 20 larger when using a pure Monte-Carlo-based method. Finally, we analyse the quality of the model by reconstructing phantom data from the YAP-(S)PET II scanner.

Evolutionary tabu search strategies for the simultaneous registration of multiple atomic structures in cryo-EM reconstructions.

PubMed

Rusu, Mirabela; Birmanns, Stefan

2010-04-01

A structural characterization of multi-component cellular assemblies is essential to explain the mechanisms governing biological function. Macromolecular architectures may be revealed by integrating information collected from various biophysical sources - for instance, by interpreting low-resolution electron cryomicroscopy reconstructions in relation to the crystal structures of the constituent fragments. A simultaneous registration of multiple components is beneficial when building atomic models as it introduces additional spatial constraints to facilitate the native placement inside the map. The high-dimensional nature of such a search problem prevents the exhaustive exploration of all possible solutions. Here we introduce a novel method based on genetic algorithms, for the efficient exploration of the multi-body registration search space. The classic scheme of a genetic algorithm was enhanced with new genetic operations, tabu search and parallel computing strategies and validated on a benchmark of synthetic and experimental cryo-EM datasets. Even at a low level of detail, for example 35-40 A, the technique successfully registered multiple component biomolecules, measuring accuracies within one order of magnitude of the nominal resolutions of the maps. The algorithm was implemented using the Sculptor molecular modeling framework, which also provides a user-friendly graphical interface and enables an instantaneous, visual exploration of intermediate solutions. (c) 2009 Elsevier Inc. All rights reserved.

A Modular Hierarchical Approach to 3D Electron Microscopy Image Segmentation

PubMed Central

Liu, Ting; Jones, Cory; Seyedhosseini, Mojtaba; Tasdizen, Tolga

2014-01-01

The study of neural circuit reconstruction, i.e., connectomics, is a challenging problem in neuroscience. Automated and semi-automated electron microscopy (EM) image analysis can be tremendously helpful for connectomics research. In this paper, we propose a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images. A hierarchical merge tree structure is built to represent multiple region hypotheses and supervised classification techniques are used to evaluate their potentials, based on which we resolve the merge tree with consistency constraints to acquire final intra-section segmentation. Then, we use a supervised learning based linking procedure for the inter-section neuron reconstruction. Also, we develop a semi-automatic method that utilizes the intermediate outputs of our automatic algorithm and achieves intra-segmentation with minimal user intervention. The experimental results show that our automatic method can achieve close-to-human intra-segmentation accuracy and state-of-the-art inter-section reconstruction accuracy. We also show that our semi-automatic method can further improve the intra-segmentation accuracy. PMID:24491638

Biologically inspired EM image alignment and neural reconstruction.

PubMed

Knowles-Barley, Seymour; Butcher, Nancy J; Meinertzhagen, Ian A; Armstrong, J Douglas

2011-08-15

Three-dimensional reconstruction of consecutive serial-section transmission electron microscopy (ssTEM) images of neural tissue currently requires many hours of manual tracing and annotation. Several computational techniques have already been applied to ssTEM images to facilitate 3D reconstruction and ease this burden. Here, we present an alternative computational approach for ssTEM image analysis. We have used biologically inspired receptive fields as a basis for a ridge detection algorithm to identify cell membranes, synaptic contacts and mitochondria. Detected line segments are used to improve alignment between consecutive images and we have joined small segments of membrane into cell surfaces using a dynamic programming algorithm similar to the Needleman-Wunsch and Smith-Waterman DNA sequence alignment procedures. A shortest path-based approach has been used to close edges and achieve image segmentation. Partial reconstructions were automatically generated and used as a basis for semi-automatic reconstruction of neural tissue. The accuracy of partial reconstructions was evaluated and 96% of membrane could be identified at the cost of 13% false positive detections. An open-source reference implementation is available in the Supplementary information. seymour.kb@ed.ac.uk; douglas.armstrong@ed.ac.uk Supplementary data are available at Bioinformatics online.

Fault Identification by Unsupervised Learning Algorithm

NASA Astrophysics Data System (ADS)

Nandan, S.; Mannu, U.

2012-12-01

Contemporary fault identification techniques predominantly rely on the surface expression of the fault. This biased observation is inadequate to yield detailed fault structures in areas with surface cover like cities deserts vegetation etc and the changes in fault patterns with depth. Furthermore it is difficult to estimate faults structure which do not generate any surface rupture. Many disastrous events have been attributed to these blind faults. Faults and earthquakes are very closely related as earthquakes occur on faults and faults grow by accumulation of coseismic rupture. For a better seismic risk evaluation it is imperative to recognize and map these faults. We implement a novel approach to identify seismically active fault planes from three dimensional hypocenter distribution by making use of unsupervised learning algorithms. We employ K-means clustering algorithm and Expectation Maximization (EM) algorithm modified to identify planar structures in spatial distribution of hypocenter after filtering out isolated events. We examine difference in the faults reconstructed by deterministic assignment in K- means and probabilistic assignment in EM algorithm. The method is conceptually identical to methodologies developed by Ouillion et al (2008, 2010) and has been extensively tested on synthetic data. We determined the sensitivity of the methodology to uncertainties in hypocenter location, density of clustering and cross cutting fault structures. The method has been applied to datasets from two contrasting regions. While Kumaon Himalaya is a convergent plate boundary, Koyna-Warna lies in middle of the Indian Plate but has a history of triggered seismicity. The reconstructed faults were validated by examining the fault orientation of mapped faults and the focal mechanism of these events determined through waveform inversion. The reconstructed faults could be used to solve the fault plane ambiguity in focal mechanism determination and constrain the fault orientations for finite source inversions. The faults produced by the method exhibited good correlation with the fault planes obtained by focal mechanism solutions and previously mapped faults.

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning

PubMed Central

Wu, Jiayi; Ma, Yong-Bei; Congdon, Charles; Brett, Bevin; Chen, Shuobing; Xu, Yaofang; Ouyang, Qi

2017-01-01

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization. PMID:28786986

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

PubMed

Wu, Jiayi; Ma, Yong-Bei; Congdon, Charles; Brett, Bevin; Chen, Shuobing; Xu, Yaofang; Ouyang, Qi; Mao, Youdong

2017-01-01

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

Crowdsourcing the creation of image segmentation algorithms for connectomics.

PubMed

Arganda-Carreras, Ignacio; Turaga, Srinivas C; Berger, Daniel R; Cireşan, Dan; Giusti, Alessandro; Gambardella, Luca M; Schmidhuber, Jürgen; Laptev, Dmitry; Dwivedi, Sarvesh; Buhmann, Joachim M; Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga; Kamentsky, Lee; Burget, Radim; Uher, Vaclav; Tan, Xiao; Sun, Changming; Pham, Tuan D; Bas, Erhan; Uzunbas, Mustafa G; Cardona, Albert; Schindelin, Johannes; Seung, H Sebastian

2015-01-01

To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.

Staff - Cheryl E. Cameron | Alaska Division of Geological & Geophysical

Science.gov Websites

Geothermal Research, v. 264, p. 85-106. http://dx.doi.org/10.1016/j.jvolgeores.2013.07.003 Coombs, M.L p. http://dggs.alaska.gov/pubs/id/23555 Larsen, J.F., Nye, C.J., Coombs, M.L., Tilman, Mariah Volcano, chapter 15, in Power, J.A., Coombs, M.L., and Freymueller, J.T., eds., The 2006 eruption of

Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters

NASA Astrophysics Data System (ADS)

Pasquato, Mario; Chung, Chul

2016-05-01

Context. Machine-learning (ML) solves problems by learning patterns from data with limited or no human guidance. In astronomy, ML is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims: We apply ML to gravitational N-body simulations of star clusters that are either formed by merging two progenitors or evolved in isolation, planning to later identify globular clusters (GCs) that may have a history of merging from observational data. Methods: We create mock-observations from simulated GCs, from which we measure a set of parameters (also called features in the machine-learning field). After carrying out dimensionality reduction on the feature space, the resulting datapoints are fed in to various classification algorithms. Using repeated random subsampling validation, we check whether the groups identified by the algorithms correspond to the underlying physical distinction between mergers and monolithically evolved simulations. Results: The three algorithms we considered (C5.0 trees, k-nearest neighbour, and support-vector machines) all achieve a test misclassification rate of about 10% without parameter tuning, with support-vector machines slightly outperforming the others. The first principal component of feature space correlates with cluster concentration. If we exclude it from the regression, the performance of the algorithms is only slightly reduced.

Accelerated time-of-flight (TOF) PET image reconstruction using TOF bin subsetization and TOF weighting matrix pre-computation.

PubMed

Mehranian, Abolfazl; Kotasidis, Fotis; Zaidi, Habib

2016-02-07

Time-of-flight (TOF) positron emission tomography (PET) technology has recently regained popularity in clinical PET studies for improving image quality and lesion detectability. Using TOF information, the spatial location of annihilation events is confined to a number of image voxels along each line of response, thereby the cross-dependencies of image voxels are reduced, which in turns results in improved signal-to-noise ratio and convergence rate. In this work, we propose a novel approach to further improve the convergence of the expectation maximization (EM)-based TOF PET image reconstruction algorithm through subsetization of emission data over TOF bins as well as azimuthal bins. Given the prevalence of TOF PET, we elaborated the practical and efficient implementation of TOF PET image reconstruction through the pre-computation of TOF weighting coefficients while exploiting the same in-plane and axial symmetries used in pre-computation of geometric system matrix. In the proposed subsetization approach, TOF PET data were partitioned into a number of interleaved TOF subsets, with the aim of reducing the spatial coupling of TOF bins and therefore to improve the convergence of the standard maximum likelihood expectation maximization (MLEM) and ordered subsets EM (OSEM) algorithms. The comparison of on-the-fly and pre-computed TOF projections showed that the pre-computation of the TOF weighting coefficients can considerably reduce the computation time of TOF PET image reconstruction. The convergence rate and bias-variance performance of the proposed TOF subsetization scheme were evaluated using simulated, experimental phantom and clinical studies. Simulations demonstrated that as the number of TOF subsets is increased, the convergence rate of MLEM and OSEM algorithms is improved. It was also found that for the same computation time, the proposed subsetization gives rise to further convergence. The bias-variance analysis of the experimental NEMA phantom and a clinical FDG-PET study also revealed that for the same noise level, a higher contrast recovery can be obtained by increasing the number of TOF subsets. It can be concluded that the proposed TOF weighting matrix pre-computation and subsetization approaches enable to further accelerate and improve the convergence properties of OSEM and MLEM algorithms, thus opening new avenues for accelerated TOF PET image reconstruction.

Accelerated time-of-flight (TOF) PET image reconstruction using TOF bin subsetization and TOF weighting matrix pre-computation

NASA Astrophysics Data System (ADS)

Mehranian, Abolfazl; Kotasidis, Fotis; Zaidi, Habib

2016-02-01

Time-of-flight (TOF) positron emission tomography (PET) technology has recently regained popularity in clinical PET studies for improving image quality and lesion detectability. Using TOF information, the spatial location of annihilation events is confined to a number of image voxels along each line of response, thereby the cross-dependencies of image voxels are reduced, which in turns results in improved signal-to-noise ratio and convergence rate. In this work, we propose a novel approach to further improve the convergence of the expectation maximization (EM)-based TOF PET image reconstruction algorithm through subsetization of emission data over TOF bins as well as azimuthal bins. Given the prevalence of TOF PET, we elaborated the practical and efficient implementation of TOF PET image reconstruction through the pre-computation of TOF weighting coefficients while exploiting the same in-plane and axial symmetries used in pre-computation of geometric system matrix. In the proposed subsetization approach, TOF PET data were partitioned into a number of interleaved TOF subsets, with the aim of reducing the spatial coupling of TOF bins and therefore to improve the convergence of the standard maximum likelihood expectation maximization (MLEM) and ordered subsets EM (OSEM) algorithms. The comparison of on-the-fly and pre-computed TOF projections showed that the pre-computation of the TOF weighting coefficients can considerably reduce the computation time of TOF PET image reconstruction. The convergence rate and bias-variance performance of the proposed TOF subsetization scheme were evaluated using simulated, experimental phantom and clinical studies. Simulations demonstrated that as the number of TOF subsets is increased, the convergence rate of MLEM and OSEM algorithms is improved. It was also found that for the same computation time, the proposed subsetization gives rise to further convergence. The bias-variance analysis of the experimental NEMA phantom and a clinical FDG-PET study also revealed that for the same noise level, a higher contrast recovery can be obtained by increasing the number of TOF subsets. It can be concluded that the proposed TOF weighting matrix pre-computation and subsetization approaches enable to further accelerate and improve the convergence properties of OSEM and MLEM algorithms, thus opening new avenues for accelerated TOF PET image reconstruction.

Joint reconstruction of activity and attenuation in Time-of-Flight PET: A Quantitative Analysis.

PubMed

Rezaei, Ahmadreza; Deroose, Christophe M; Vahle, Thomas; Boada, Fernando; Nuyts, Johan

2018-03-01

Joint activity and attenuation reconstruction methods from time of flight (TOF) positron emission tomography (PET) data provide an effective solution to attenuation correction when no (or incomplete/inaccurate) information on the attenuation is available. One of the main barriers limiting their use in clinical practice is the lack of validation of these methods on a relatively large patient database. In this contribution, we aim at validating the activity reconstructions of the maximum likelihood activity reconstruction and attenuation registration (MLRR) algorithm on a whole-body patient data set. Furthermore, a partial validation (since the scale problem of the algorithm is avoided for now) of the maximum likelihood activity and attenuation reconstruction (MLAA) algorithm is also provided. We present a quantitative comparison of the joint reconstructions to the current clinical gold-standard maximum likelihood expectation maximization (MLEM) reconstruction with CT-based attenuation correction. Methods: The whole-body TOF-PET emission data of each patient data set is processed as a whole to reconstruct an activity volume covering all the acquired bed positions, which helps to reduce the problem of a scale per bed position in MLAA to a global scale for the entire activity volume. Three reconstruction algorithms are used: MLEM, MLRR and MLAA. A maximum likelihood (ML) scaling of the single scatter simulation (SSS) estimate to the emission data is used for scatter correction. The reconstruction results are then analyzed in different regions of interest. Results: The joint reconstructions of the whole-body patient data set provide better quantification in case of PET and CT misalignments caused by patient and organ motion. Our quantitative analysis shows a difference of -4.2% (±2.3%) and -7.5% (±4.6%) between the joint reconstructions of MLRR and MLAA compared to MLEM, averaged over all regions of interest, respectively. Conclusion: Joint activity and attenuation estimation methods provide a useful means to estimate the tracer distribution in cases where CT-based attenuation images are subject to misalignments or are not available. With an accurate estimate of the scatter contribution in the emission measurements, the joint TOF-PET reconstructions are within clinical acceptable accuracy. Copyright © 2018 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Fast, automatic, and accurate catheter reconstruction in HDR brachytherapy using an electromagnetic 3D tracking system.

PubMed

Poulin, Eric; Racine, Emmanuel; Binnekamp, Dirk; Beaulieu, Luc

2015-03-01

In high dose rate brachytherapy (HDR-B), current catheter reconstruction protocols are relatively slow and error prone. The purpose of this technical note is to evaluate the accuracy and the robustness of an electromagnetic (EM) tracking system for automated and real-time catheter reconstruction. For this preclinical study, a total of ten catheters were inserted in gelatin phantoms with different trajectories. Catheters were reconstructed using a 18G biopsy needle, used as an EM stylet and equipped with a miniaturized sensor, and the second generation Aurora(®) Planar Field Generator from Northern Digital Inc. The Aurora EM system provides position and orientation value with precisions of 0.7 mm and 0.2°, respectively. Phantoms were also scanned using a μCT (GE Healthcare) and Philips Big Bore clinical computed tomography (CT) system with a spatial resolution of 89 μm and 2 mm, respectively. Reconstructions using the EM stylet were compared to μCT and CT. To assess the robustness of the EM reconstruction, five catheters were reconstructed twice and compared. Reconstruction time for one catheter was 10 s, leading to a total reconstruction time inferior to 3 min for a typical 17-catheter implant. When compared to the μCT, the mean EM tip identification error was 0.69 ± 0.29 mm while the CT error was 1.08 ± 0.67 mm. The mean 3D distance error was found to be 0.66 ± 0.33 mm and 1.08 ± 0.72 mm for the EM and CT, respectively. EM 3D catheter trajectories were found to be more accurate. A maximum difference of less than 0.6 mm was found between successive EM reconstructions. The EM reconstruction was found to be more accurate and precise than the conventional methods used for catheter reconstruction in HDR-B. This approach can be applied to any type of catheters and applicators.

Fast, automatic, and accurate catheter reconstruction in HDR brachytherapy using an electromagnetic 3D tracking system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poulin, Eric; Racine, Emmanuel; Beaulieu, Luc, E-mail: Luc.Beaulieu@phy.ulaval.ca

2015-03-15

Purpose: In high dose rate brachytherapy (HDR-B), current catheter reconstruction protocols are relatively slow and error prone. The purpose of this technical note is to evaluate the accuracy and the robustness of an electromagnetic (EM) tracking system for automated and real-time catheter reconstruction. Methods: For this preclinical study, a total of ten catheters were inserted in gelatin phantoms with different trajectories. Catheters were reconstructed using a 18G biopsy needle, used as an EM stylet and equipped with a miniaturized sensor, and the second generation Aurora{sup ®} Planar Field Generator from Northern Digital Inc. The Aurora EM system provides position andmore » orientation value with precisions of 0.7 mm and 0.2°, respectively. Phantoms were also scanned using a μCT (GE Healthcare) and Philips Big Bore clinical computed tomography (CT) system with a spatial resolution of 89 μm and 2 mm, respectively. Reconstructions using the EM stylet were compared to μCT and CT. To assess the robustness of the EM reconstruction, five catheters were reconstructed twice and compared. Results: Reconstruction time for one catheter was 10 s, leading to a total reconstruction time inferior to 3 min for a typical 17-catheter implant. When compared to the μCT, the mean EM tip identification error was 0.69 ± 0.29 mm while the CT error was 1.08 ± 0.67 mm. The mean 3D distance error was found to be 0.66 ± 0.33 mm and 1.08 ± 0.72 mm for the EM and CT, respectively. EM 3D catheter trajectories were found to be more accurate. A maximum difference of less than 0.6 mm was found between successive EM reconstructions. Conclusions: The EM reconstruction was found to be more accurate and precise than the conventional methods used for catheter reconstruction in HDR-B. This approach can be applied to any type of catheters and applicators.« less

Item-Based Top-N Recommendation Algorithms

DTIC Science & Technology

2003-01-20

basket of items, utilized by many e-commerce sites, cannot take advantage of pre-computed user-to-user similarities. Finally, even though the...not discriminate between items that are present in frequent itemsets and items that are not, while still maintaining the computational advantages of...453219 0.02% 7.74 ccard 42629 68793 398619 0.01% 9.35 ecommerce 6667 17491 91222 0.08% 13.68 em 8002 1648 769311 5.83% 96.14 ml 943 1682 100000 6.31

WE-A-17A-10: Fast, Automatic and Accurate Catheter Reconstruction in HDR Brachytherapy Using An Electromagnetic 3D Tracking System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poulin, E; Racine, E; Beaulieu, L

2014-06-15

Purpose: In high dose rate brachytherapy (HDR-B), actual catheter reconstruction protocols are slow and errors prompt. The purpose of this study was to evaluate the accuracy and robustness of an electromagnetic (EM) tracking system for improved catheter reconstruction in HDR-B protocols. Methods: For this proof-of-principle, a total of 10 catheters were inserted in gelatin phantoms with different trajectories. Catheters were reconstructed using a Philips-design 18G biopsy needle (used as an EM stylet) and the second generation Aurora Planar Field Generator from Northern Digital Inc. The Aurora EM system exploits alternating current technology and generates 3D points at 40 Hz. Phantomsmore » were also scanned using a μCT (GE Healthcare) and Philips Big Bore clinical CT system with a resolution of 0.089 mm and 2 mm, respectively. Reconstructions using the EM stylet were compared to μCT and CT. To assess the robustness of the EM reconstruction, 5 catheters were reconstructed twice and compared. Results: Reconstruction time for one catheter was 10 seconds or less. This would imply that for a typical clinical implant of 17 catheters, the total reconstruction time would be less than 3 minutes. When compared to the μCT, the mean EM tip identification error was 0.69 ± 0.29 mm while the CT error was 1.08 ± 0.67 mm. The mean 3D distance error was found to be 0.92 ± 0.37 mm and 1.74 ± 1.39 mm for the EM and CT, respectively. EM 3D catheter trajectories were found to be significantly more accurate (unpaired t-test, p < 0.05). A mean difference of less than 0.5 mm was found between successive EM reconstructions. Conclusion: The EM reconstruction was found to be faster, more accurate and more robust than the conventional methods used for catheter reconstruction in HDR-B. This approach can be applied to any type of catheters and applicators. We would like to disclose that the equipments, used in this study, is coming from a collaboration with Philips Medical.« less

Staff - Janet R. Schaefer | Alaska Division of Geological & Geophysical

Science.gov Websites

. http://doi.org/10.14509/25602 Wallace, K.L., Schaefer, J.R., and Coombs, M.L., 2013, Character, mass ., Coombs, M.L., Diefenbach, A.K., Lopez, Taryn, McNutt, S.R., Neal, C.A., Payne, A.L., Power, J.A , J.E., Schilling, S.P., Schaefer, J.R., and Trusdell, F.A., 2007, Crisis GIS: Preparing for and

Improved electromagnetic tracking for catheter path reconstruction with application in high-dose-rate brachytherapy.

PubMed

Lugez, Elodie; Sadjadi, Hossein; Joshi, Chandra P; Akl, Selim G; Fichtinger, Gabor

2017-04-01

Electromagnetic (EM) catheter tracking has recently been introduced in order to enable prompt and uncomplicated reconstruction of catheter paths in various clinical interventions. However, EM tracking is prone to measurement errors which can compromise the outcome of the procedure. Minimizing catheter tracking errors is therefore paramount to improve the path reconstruction accuracy. An extended Kalman filter (EKF) was employed to combine the nonlinear kinematic model of an EM sensor inside the catheter, with both its position and orientation measurements. The formulation of the kinematic model was based on the nonholonomic motion constraints of the EM sensor inside the catheter. Experimental verification was carried out in a clinical HDR suite. Ten catheters were inserted with mean curvatures varying from 0 to [Formula: see text] in a phantom. A miniaturized Ascension (Burlington, Vermont, USA) trakSTAR EM sensor (model 55) was threaded within each catheter at various speeds ranging from 7.4 to [Formula: see text]. The nonholonomic EKF was applied on the tracking data in order to statistically improve the EM tracking accuracy. A sample reconstruction error was defined at each point as the Euclidean distance between the estimated EM measurement and its corresponding ground truth. A path reconstruction accuracy was defined as the root mean square of the sample reconstruction errors, while the path reconstruction precision was defined as the standard deviation of these sample reconstruction errors. The impacts of sensor velocity and path curvature on the nonholonomic EKF method were determined. Finally, the nonholonomic EKF catheter path reconstructions were compared with the reconstructions provided by the manufacturer's filters under default settings, namely the AC wide notch and the DC adaptive filter. With a path reconstruction accuracy of 1.9 mm, the nonholonomic EKF surpassed the performance of the manufacturer's filters (2.4 mm) by 21% and the raw EM measurements (3.5 mm) by 46%. Similarly, with a path reconstruction precision of 0.8 mm, the nonholonomic EKF surpassed the performance of the manufacturer's filters (1.0 mm) by 20% and the raw EM measurements (1.7 mm) by 53%. Path reconstruction accuracies did not follow an apparent trend when varying the path curvature and sensor velocity; instead, reconstruction accuracies were predominantly impacted by the position of the EM field transmitter ([Formula: see text]). The advanced nonholonomic EKF is effective in reducing EM measurement errors when reconstructing catheter paths, is robust to path curvature and sensor speed, and runs in real time. Our approach is promising for a plurality of clinical procedures requiring catheter reconstructions, such as cardiovascular interventions, pulmonary applications (Bender et al. in medical image computing and computer-assisted intervention-MICCAI 99. Springer, Berlin, pp 981-989, 1999), and brachytherapy.

Assessment of vulnerable plaque composition by matching the deformation of a parametric plaque model to measured plaque deformation.

PubMed

Baldewsing, Radj A; Schaar, Johannes A; Mastik, Frits; Oomens, Cees W J; van der Steen, Antonius F W

2005-04-01

Intravascular ultrasound (IVUS) elastography visualizes local radial strain of arteries in so-called elastograms to detect rupture-prone plaques. However, due to the unknown arterial stress distribution these elastograms cannot be directly interpreted as a morphology and material composition image. To overcome this limitation we have developed a method that reconstructs a Young's modulus image from an elastogram. This method is especially suited for thin-cap fibroatheromas (TCFAs), i.e., plaques with a media region containing a lipid pool covered by a cap. Reconstruction is done by a minimization algorithm that matches the strain image output, calculated with a parametric finite element model (PFEM) representation of a TCFA, to an elastogram by iteratively updating the PFEM geometry and material parameters. These geometry parameters delineate the TCFA media, lipid pool and cap regions by circles. The material parameter for each region is a Young's modulus, EM, EL, and EC, respectively. The method was successfully tested on computer-simulated TCFAs (n = 2), one defined by circles, the other by tracing TCFA histology, and additionally on a physical phantom (n = 1) having a stiff wall (measured EM = 16.8 kPa) with an eccentric soft region (measured EL = 4.2 kPa). Finally, it was applied on human coronary plaques in vitro (n = 1) and in vivo (n = 1). The corresponding simulated and measured elastograms of these plaques showed radial strain values from 0% up to 2% at a pressure differential of 20, 20, 1, 20, and 1 mmHg respectively. The used/reconstructed Young's moduli [kPa] were for the circular plaque EL = 50/66, EM = 1500/1484, EC = 2000/2047, for the traced plaque EL = 25/1, EM = 1000/1148, EC = 1500/1491, for the phantom EL = 4.2/4 kPa, EM = 16.8/16, for the in vitro plaque EL = n.a./29, EM = n.a./647, EC = n.a./1784 kPa and for the in vivo plaque EL = n.a./2, EM = n.a./188, Ec = n.a./188 kPa.

Defense.gov - Special Report - On Location in Afghanistan

Science.gov Websites

team in Afghanistan’s Paktia province safe as it travels around the region meeting with local leaders FORWARD OPERATING BASE GARDEZ, Afghanistan, Feb. 19, 2009 – A provincial reconstruction team is working are working with the Paktia Provincial Reconstruction Team to help convince villagers to reject the

The Orthogonally Partitioned EM Algorithm: Extending the EM Algorithm for Algorithmic Stability and Bias Correction Due to Imperfect Data.

PubMed

Regier, Michael D; Moodie, Erica E M

2016-05-01

We propose an extension of the EM algorithm that exploits the common assumption of unique parameterization, corrects for biases due to missing data and measurement error, converges for the specified model when standard implementation of the EM algorithm has a low probability of convergence, and reduces a potentially complex algorithm into a sequence of smaller, simpler, self-contained EM algorithms. We use the theory surrounding the EM algorithm to derive the theoretical results of our proposal, showing that an optimal solution over the parameter space is obtained. A simulation study is used to explore the finite sample properties of the proposed extension when there is missing data and measurement error. We observe that partitioning the EM algorithm into simpler steps may provide better bias reduction in the estimation of model parameters. The ability to breakdown a complicated problem in to a series of simpler, more accessible problems will permit a broader implementation of the EM algorithm, permit the use of software packages that now implement and/or automate the EM algorithm, and make the EM algorithm more accessible to a wider and more general audience.

Nonlinear inversion of borehole-radar tomography data to reconstruct velocity and attenuation distribution in earth materials

USGS Publications Warehouse

Zhou, C.; Liu, L.; Lane, J.W.

2001-01-01

A nonlinear tomographic inversion method that uses first-arrival travel-time and amplitude-spectra information from cross-hole radar measurements was developed to simultaneously reconstruct electromagnetic velocity and attenuation distribution in earth materials. Inversion methods were developed to analyze single cross-hole tomography surveys and differential tomography surveys. Assuming the earth behaves as a linear system, the inversion methods do not require estimation of source radiation pattern, receiver coupling, or geometrical spreading. The data analysis and tomographic inversion algorithm were applied to synthetic test data and to cross-hole radar field data provided by the US Geological Survey (USGS). The cross-hole radar field data were acquired at the USGS fractured-rock field research site at Mirror Lake near Thornton, New Hampshire, before and after injection of a saline tracer, to monitor the transport of electrically conductive fluids in the image plane. Results from the synthetic data test demonstrate the algorithm computational efficiency and indicate that the method robustly can reconstruct electromagnetic (EM) wave velocity and attenuation distribution in earth materials. The field test results outline zones of velocity and attenuation anomalies consistent with the finding of previous investigators; however, the tomograms appear to be quite smooth. Further work is needed to effectively find the optimal smoothness criterion in applying the Tikhonov regularization in the nonlinear inversion algorithms for cross-hole radar tomography. ?? 2001 Elsevier Science B.V. All rights reserved.

Simultaneous 99mtc/111in spect reconstruction using accelerated convolution-based forced detection monte carlo

NASA Astrophysics Data System (ADS)

Karamat, Muhammad I.; Farncombe, Troy H.

2015-10-01

Simultaneous multi-isotope Single Photon Emission Computed Tomography (SPECT) imaging has a number of applications in cardiac, brain, and cancer imaging. The major concern however, is the significant crosstalk contamination due to photon scatter between the different isotopes. The current study focuses on a method of crosstalk compensation between two isotopes in simultaneous dual isotope SPECT acquisition applied to cancer imaging using 99mTc and 111In. We have developed an iterative image reconstruction technique that simulates the photon down-scatter from one isotope into the acquisition window of a second isotope. Our approach uses an accelerated Monte Carlo (MC) technique for the forward projection step in an iterative reconstruction algorithm. The MC estimated scatter contamination of a radionuclide contained in a given projection view is then used to compensate for the photon contamination in the acquisition window of other nuclide. We use a modified ordered subset-expectation maximization (OS-EM) algorithm named simultaneous ordered subset-expectation maximization (Sim-OSEM), to perform this step. We have undertaken a number of simulation tests and phantom studies to verify this approach. The proposed reconstruction technique was also evaluated by reconstruction of experimentally acquired phantom data. Reconstruction using Sim-OSEM showed very promising results in terms of contrast recovery and uniformity of object background compared to alternative reconstruction methods implementing alternative scatter correction schemes (i.e., triple energy window or separately acquired projection data). In this study the evaluation is based on the quality of reconstructed images and activity estimated using Sim-OSEM. In order to quantitate the possible improvement in spatial resolution and signal to noise ratio (SNR) observed in this study, further simulation and experimental studies are required.

An improved image non-blind image deblurring method based on FoEs

NASA Astrophysics Data System (ADS)

Zhu, Qidan; Sun, Lei

2013-03-01

Traditional non-blind image deblurring algorithms always use maximum a posterior(MAP). MAP estimates involving natural image priors can reduce the ripples effectively in contrast to maximum likelihood(ML). However, they have been found lacking in terms of restoration performance. Based on this issue, we utilize MAP with KL penalty to replace traditional MAP. We develop an image reconstruction algorithm that minimizes the KL divergence between the reference distribution and the prior distribution. The approximate KL penalty can restrain over-smooth caused by MAP. We use three groups of images and Harris corner detection to prove our method. The experimental results show that our algorithm of non-blind image restoration can effectively reduce the ringing effect and exhibit the state-of-the-art deblurring results.

GOING THE DISTANCE: MAPPING HOST GALAXIES OF LIGO AND VIRGO SOURCES IN THREE DIMENSIONS USING LOCAL COSMOGRAPHY AND TARGETED FOLLOW-UP

DOE Office of Scientific and Technical Information (OSTI.GOV)

Singer, Leo P.; Cenko, S. Bradley; Gehrels, Neil

2016-09-20

The Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) discovered gravitational waves (GWs) from a binary black hole merger in 2015 September and may soon observe signals from neutron star mergers. There is considerable interest in searching for their faint and rapidly fading electromagnetic (EM) counterparts, though GW position uncertainties are as coarse as hundreds of square degrees. Because LIGO’s sensitivity to binary neutron stars is limited to the local universe, the area on the sky that must be searched could be reduced by weighting positions by mass, luminosity, or star formation in nearby galaxies. Since GW observations provide information about luminositymore » distance, combining the reconstructed volume with positions and redshifts of galaxies could reduce the area even more dramatically. A key missing ingredient has been a rapid GW parameter estimation algorithm that reconstructs the full distribution of sky location and distance. We demonstrate the first such algorithm, which takes under a minute, fast enough to enable immediate EM follow-up. By combining the three-dimensional posterior with a galaxy catalog, we can reduce the number of galaxies that could conceivably host the event by a factor of 1.4, the total exposure time for the Swift X-ray Telescope by a factor of 2, the total exposure time for a synoptic optical survey by a factor of 2, and the total exposure time for a narrow-field optical telescope by a factor of 3. This encourages us to suggest a new role for small field of view optical instruments in performing targeted searches of the most massive galaxies within the reconstructed volumes.« less

Going the Distance: Mapping Host Galaxies of LIGO and VIRGO Sources in Three Dimensions using Local Cosmography and Targeted Follow-Up

NASA Technical Reports Server (NTRS)

Singer, Leo P.; Chen, Hsin-Yu; Holz, Daniel E.; Farr, Will M.; Price, Larry R.; Raymond, Vivien; Cenko, S. Bradley; Gehrels, Neil; Cannizzo, John K.; Kasliwal, Mansi M.;

What does fault tolerant Deep Learning need from MPI?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Amatya, Vinay C.; Vishnu, Abhinav; Siegel, Charles M.

Deep Learning (DL) algorithms have become the {\\em de facto} Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive -- even distributed DL implementations which use MPI require days of training (model learning) time on commonly studied datasets. Long running DL applications become susceptible to faults -- requiring development of a fault tolerant system infrastructure, in addition to fault tolerant DL algorithms. This raises an important question: {\\em What is needed from MPI for designing fault tolerant DL implementations?} In this paper, we address this problem for permanent faults. We motivate the need for amore » fault tolerant MPI specification by an in-depth consideration of recent innovations in DL algorithms and their properties, which drive the need for specific fault tolerance features. We present an in-depth discussion on the suitability of different parallelism types (model, data and hybrid); a need (or lack thereof) for check-pointing of any critical data structures; and most importantly, consideration for several fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI and their applicability to fault tolerant DL implementations. We leverage a distributed memory implementation of Caffe, currently available under the Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches by extending MaTEx-Caffe for using ULFM-based implementation. Our evaluation using the ImageNet dataset and AlexNet neural network topology demonstrates the effectiveness of the proposed fault tolerant DL implementation using OpenMPI based ULFM.« less

Low Dose PET Image Reconstruction with Total Variation Using Alternating Direction Method.

PubMed

Yu, Xingjian; Wang, Chenye; Hu, Hongjie; Liu, Huafeng

2016-01-01

In this paper, a total variation (TV) minimization strategy is proposed to overcome the problem of sparse spatial resolution and large amounts of noise in low dose positron emission tomography (PET) imaging reconstruction. Two types of objective function were established based on two statistical models of measured PET data, least-square (LS) TV for the Gaussian distribution and Poisson-TV for the Poisson distribution. To efficiently obtain high quality reconstructed images, the alternating direction method (ADM) is used to solve these objective functions. As compared with the iterative shrinkage/thresholding (IST) based algorithms, the proposed ADM can make full use of the TV constraint and its convergence rate is faster. The performance of the proposed approach is validated through comparisons with the expectation-maximization (EM) method using synthetic and experimental biological data. In the comparisons, the results of both LS-TV and Poisson-TV are taken into consideration to find which models are more suitable for PET imaging, in particular low-dose PET. To evaluate the results quantitatively, we computed bias, variance, and the contrast recovery coefficient (CRC) and drew profiles of the reconstructed images produced by the different methods. The results show that both Poisson-TV and LS-TV can provide a high visual quality at a low dose level. The bias and variance of the proposed LS-TV and Poisson-TV methods are 20% to 74% less at all counting levels than those of the EM method. Poisson-TV gives the best performance in terms of high-accuracy reconstruction with the lowest bias and variance as compared to the ground truth (14.3% less bias and 21.9% less variance). In contrast, LS-TV gives the best performance in terms of the high contrast of the reconstruction with the highest CRC.

Low Dose PET Image Reconstruction with Total Variation Using Alternating Direction Method

PubMed Central

Yu, Xingjian; Wang, Chenye; Hu, Hongjie; Liu, Huafeng

2016-01-01

In this paper, a total variation (TV) minimization strategy is proposed to overcome the problem of sparse spatial resolution and large amounts of noise in low dose positron emission tomography (PET) imaging reconstruction. Two types of objective function were established based on two statistical models of measured PET data, least-square (LS) TV for the Gaussian distribution and Poisson-TV for the Poisson distribution. To efficiently obtain high quality reconstructed images, the alternating direction method (ADM) is used to solve these objective functions. As compared with the iterative shrinkage/thresholding (IST) based algorithms, the proposed ADM can make full use of the TV constraint and its convergence rate is faster. The performance of the proposed approach is validated through comparisons with the expectation-maximization (EM) method using synthetic and experimental biological data. In the comparisons, the results of both LS-TV and Poisson-TV are taken into consideration to find which models are more suitable for PET imaging, in particular low-dose PET. To evaluate the results quantitatively, we computed bias, variance, and the contrast recovery coefficient (CRC) and drew profiles of the reconstructed images produced by the different methods. The results show that both Poisson-TV and LS-TV can provide a high visual quality at a low dose level. The bias and variance of the proposed LS-TV and Poisson-TV methods are 20% to 74% less at all counting levels than those of the EM method. Poisson-TV gives the best performance in terms of high-accuracy reconstruction with the lowest bias and variance as compared to the ground truth (14.3% less bias and 21.9% less variance). In contrast, LS-TV gives the best performance in terms of the high contrast of the reconstruction with the highest CRC. PMID:28005929

Strawberry: Fast and accurate genome-guided transcript reconstruction and quantification from RNA-Seq.

PubMed

Liu, Ruolin; Dickerson, Julie

2017-11-01

We propose a novel method and software tool, Strawberry, for transcript reconstruction and quantification from RNA-Seq data under the guidance of genome alignment and independent of gene annotation. Strawberry consists of two modules: assembly and quantification. The novelty of Strawberry is that the two modules use different optimization frameworks but utilize the same data graph structure, which allows a highly efficient, expandable and accurate algorithm for dealing large data. The assembly module parses aligned reads into splicing graphs, and uses network flow algorithms to select the most likely transcripts. The quantification module uses a latent class model to assign read counts from the nodes of splicing graphs to transcripts. Strawberry simultaneously estimates the transcript abundances and corrects for sequencing bias through an EM algorithm. Based on simulations, Strawberry outperforms Cufflinks and StringTie in terms of both assembly and quantification accuracies. Under the evaluation of a real data set, the estimated transcript expression by Strawberry has the highest correlation with Nanostring probe counts, an independent experiment measure for transcript expression. Strawberry is written in C++14, and is available as open source software at https://github.com/ruolin/strawberry under the MIT license.

Optimized multiple linear mappings for single image super-resolution

NASA Astrophysics Data System (ADS)

Zhang, Kaibing; Li, Jie; Xiong, Zenggang; Liu, Xiuping; Gao, Xinbo

2017-12-01

Learning piecewise linear regression has been recognized as an effective way for example learning-based single image super-resolution (SR) in literature. In this paper, we employ an expectation-maximization (EM) algorithm to further improve the SR performance of our previous multiple linear mappings (MLM) based SR method. In the training stage, the proposed method starts with a set of linear regressors obtained by the MLM-based method, and then jointly optimizes the clustering results and the low- and high-resolution subdictionary pairs for regression functions by using the metric of the reconstruction errors. In the test stage, we select the optimal regressor for SR reconstruction by accumulating the reconstruction errors of m-nearest neighbors in the training set. Thorough experimental results carried on six publicly available datasets demonstrate that the proposed SR method can yield high-quality images with finer details and sharper edges in terms of both quantitative and perceptual image quality assessments.

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in vivo studies.

PubMed

Petibon, Yoann; Rakvongthai, Yothin; El Fakhri, Georges; Ouyang, Jinsong

2017-05-07

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves-TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18 F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans-each containing 1/8th of the total number of events-were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18 F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard ordered subset expectation maximization (OSEM) reconstruction algorithm on one side, and the one-step late maximum a posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of [Formula: see text], the tracer transport rate (ml · min -1 · ml -1 ), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced [Formula: see text] maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced [Formula: see text] estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance.

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in vivo studies

NASA Astrophysics Data System (ADS)

Petibon, Yoann; Rakvongthai, Yothin; El Fakhri, Georges; Ouyang, Jinsong

2017-05-01

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves-TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans—each containing 1/8th of the total number of events—were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard ordered subset expectation maximization (OSEM) reconstruction algorithm on one side, and the one-step late maximum a posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of {{K}1} , the tracer transport rate (ml · min-1 · ml-1), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced {{K}1} maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced {{K}1} estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance.

Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages

PubMed Central

Nunez-Iglesias, Juan; Kennedy, Ryan; Plaza, Stephen M.; Chakraborty, Anirban; Katz, William T.

2014-01-01

The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them. PMID:24772079

Study on the Spatial Resolution of Single and Multiple Coincidences Compton Camera

NASA Astrophysics Data System (ADS)

Andreyev, Andriy; Sitek, Arkadiusz; Celler, Anna

2012-10-01

In this paper we study the image resolution that can be obtained from the Multiple Coincidences Compton Camera (MCCC). The principle of MCCC is based on a simultaneous acquisition of several gamma-rays emitted in cascade from a single nucleus. Contrary to a standard Compton camera, MCCC can theoretically provide the exact location of a radioactive source (based only on the identification of the intersection point of three cones created by a single decay), without complicated tomographic reconstruction. However, practical implementation of the MCCC approach encounters several problems, such as low detection sensitivities result in very low probability of coincident triple gamma-ray detection, which is necessary for the source localization. It is also important to evaluate how the detection uncertainties (finite energy and spatial resolution) influence identification of the intersection of three cones, thus the resulting image quality. In this study we investigate how the spatial resolution of the reconstructed images using the triple-cone reconstruction (TCR) approach compares to images reconstructed from the same data using standard iterative method based on single-cone. Results show, that FWHM for the point source reconstructed with TCR was 20-30% higher than the one obtained from the standard iterative reconstruction based on expectation maximization (EM) algorithm and conventional single-cone Compton imaging. Finite energy and spatial resolutions of the MCCC detectors lead to errors in conical surfaces definitions (“thick” conical surfaces) which only amplify in image reconstruction when intersection of three cones is being sought. Our investigations show that, in spite of being conceptually appealing, the identification of triple cone intersection constitutes yet another restriction of the multiple coincidence approach which limits the image resolution that can be obtained with MCCC and TCR algorithm.

Three-dimensional ordered-subset expectation maximization iterative protocol for evaluation of left ventricular volumes and function by quantitative gated SPECT: a dynamic phantom study.

PubMed

Ceriani, Luca; Ruberto, Teresa; Delaloye, Angelika Bischof; Prior, John O; Giovanella, Luca

2010-03-01

The purposes of this study were to characterize the performance of a 3-dimensional (3D) ordered-subset expectation maximization (OSEM) algorithm in the quantification of left ventricular (LV) function with (99m)Tc-labeled agent gated SPECT (G-SPECT), the QGS program, and a beating-heart phantom and to optimize the reconstruction parameters for clinical applications. A G-SPECT image of a dynamic heart phantom simulating the beating left ventricle was acquired. The exact volumes of the phantom were known and were as follows: end-diastolic volume (EDV) of 112 mL, end-systolic volume (ESV) of 37 mL, and stroke volume (SV) of 75 mL; these volumes produced an LV ejection fraction (LVEF) of 67%. Tomographic reconstructions were obtained after 10-20 iterations (I) with 4, 8, and 16 subsets (S) at full width at half maximum (FWHM) gaussian postprocessing filter cutoff values of 8-15 mm. The QGS program was used for quantitative measurements. Measured values ranged from 72 to 92 mL for EDV, from 18 to 32 mL for ESV, and from 54 to 63 mL for SV, and the calculated LVEF ranged from 65% to 76%. Overall, the combination of 10 I, 8 S, and a cutoff filter value of 10 mm produced the most accurate results. The plot of the measures with respect to the expectation maximization-equivalent iterations (I x S product) revealed a bell-shaped curve for the LV volumes and a reverse distribution for the LVEF, with the best results in the intermediate range. In particular, FWHM cutoff values exceeding 10 mm affected the estimation of the LV volumes. The QGS program is able to correctly calculate the LVEF when used in association with an optimized 3D OSEM algorithm (8 S, 10 I, and FWHM of 10 mm) but underestimates the LV volumes. However, various combinations of technical parameters, including a limited range of I and S (80-160 expectation maximization-equivalent iterations) and low cutoff values (< or =10 mm) for the gaussian postprocessing filter, produced results with similar accuracies and without clinically relevant differences in the LV volumes and the estimated LVEF.

Investigation of probabilistic principal component analysis compared to proper orthogonal decomposition methods for basis extraction and missing data estimation

NASA Astrophysics Data System (ADS)

Lee, Kyunghoon

To evaluate the maximum likelihood estimates (MLEs) of probabilistic principal component analysis (PPCA) parameters such as a factor-loading, PPCA can invoke an expectation-maximization (EM) algorithm, yielding an EM algorithm for PPCA (EM-PCA). In order to examine the benefits of the EM-PCA for aerospace engineering applications, this thesis attempts to qualitatively and quantitatively scrutinize the EM-PCA alongside both POD and gappy POD using high-dimensional simulation data. In pursuing qualitative investigations, the theoretical relationship between POD and PPCA is transparent such that the factor-loading MLE of PPCA, evaluated by the EM-PCA, pertains to an orthogonal basis obtained by POD. By contrast, the analytical connection between gappy POD and the EM-PCA is nebulous because they distinctively approximate missing data due to their antithetical formulation perspectives: gappy POD solves a least-squares problem whereas the EM-PCA relies on the expectation of the observation probability model. To juxtapose both gappy POD and the EM-PCA, this research proposes a unifying least-squares perspective that embraces the two disparate algorithms within a generalized least-squares framework. As a result, the unifying perspective reveals that both methods address similar least-squares problems; however, their formulations contain dissimilar bases and norms. Furthermore, this research delves into the ramifications of the different bases and norms that will eventually characterize the traits of both methods. To this end, two hybrid algorithms of gappy POD and the EM-PCA are devised and compared to the original algorithms for a qualitative illustration of the different basis and norm effects. After all, a norm reflecting a curve-fitting method is found to more significantly affect estimation error reduction than a basis for two example test data sets: one is absent of data only at a single snapshot and the other misses data across all the snapshots. From a numerical performance aspect, the EM-PCA is computationally less efficient than POD for intact data since it suffers from slow convergence inherited from the EM algorithm. For incomplete data, this thesis quantitatively found that the number of data missing snapshots predetermines whether the EM-PCA or gappy POD outperforms the other because of the computational cost of a coefficient evaluation, resulting from a norm selection. For instance, gappy POD demands laborious computational effort in proportion to the number of data-missing snapshots as a consequence of the gappy norm. In contrast, the computational cost of the EM-PCA is invariant to the number of data-missing snapshots thanks to the L2 norm. In general, the higher the number of data-missing snapshots, the wider the gap between the computational cost of gappy POD and the EM-PCA. Based on the numerical experiments reported in this thesis, the following criterion is recommended regarding the selection between gappy POD and the EM-PCA for computational efficiency: gappy POD for an incomplete data set containing a few data-missing snapshots and the EM-PCA for an incomplete data set involving multiple data-missing snapshots. Last, the EM-PCA is applied to two aerospace applications in comparison to gappy POD as a proof of concept: one with an emphasis on basis extraction and the other with a focus on missing data reconstruction for a given incomplete data set with scattered missing data. The first application exploits the EM-PCA to efficiently construct reduced-order models of engine deck responses obtained by the numerical propulsion system simulation (NPSS), some of whose results are absent due to failed analyses caused by numerical instability. Model-prediction tests validate that engine performance metrics estimated by the reduced-order NPSS model exhibit considerably good agreement with those directly obtained by NPSS. Similarly, the second application illustrates that the EM-PCA is significantly more cost effective than gappy POD at repairing spurious PIV measurements obtained from acoustically-excited, bluff-body jet flow experiments. The EM-PCA reduces computational cost on factors 8 ˜ 19 compared to gappy POD while generating the same restoration results as those evaluated by gappy POD. All in all, through comprehensive theoretical and numerical investigation, this research establishes that the EM-PCA is an efficient alternative to gappy POD for an incomplete data set containing missing data over an entire data set. (Abstract shortened by UMI.)

PF2fit: Polar Fast Fourier Matched Alignment of Atomistic Structures with 3D Electron Microscopy Maps.

PubMed

Bettadapura, Radhakrishna; Rasheed, Muhibur; Vollrath, Antje; Bajaj, Chandrajit

2015-10-01

There continue to be increasing occurrences of both atomistic structure models in the PDB (possibly reconstructed from X-ray diffraction or NMR data), and 3D reconstructed cryo-electron microscopy (3D EM) maps (albeit at coarser resolution) of the same or homologous molecule or molecular assembly, deposited in the EMDB. To obtain the best possible structural model of the molecule at the best achievable resolution, and without any missing gaps, one typically aligns (match and fits) the atomistic structure model with the 3D EM map. We discuss a new algorithm and generalized framework, named PF(2) fit (Polar Fast Fourier Fitting) for the best possible structural alignment of atomistic structures with 3D EM. While PF(2) fit enables only a rigid, six dimensional (6D) alignment method, it augments prior work on 6D X-ray structure and 3D EM alignment in multiple ways: Scoring. PF(2) fit includes a new scoring scheme that, in addition to rewarding overlaps between the volumes occupied by the atomistic structure and 3D EM map, rewards overlaps between the volumes complementary to them. We quantitatively demonstrate how this new complementary scoring scheme improves upon existing approaches. PF(2) fit also includes two scoring functions, the non-uniform exterior penalty and the skeleton-secondary structure score, and implements the scattering potential score as an alternative to traditional Gaussian blurring. Search. PF(2) fit utilizes a fast polar Fourier search scheme, whose main advantage is the ability to search over uniformly and adaptively sampled subsets of the space of rigid-body motions. PF(2) fit also implements a new reranking search and scoring methodology that considerably improves alignment metrics in results obtained from the initial search.

PF2 fit: Polar Fast Fourier Matched Alignment of Atomistic Structures with 3D Electron Microscopy Maps

PubMed Central

Bettadapura, Radhakrishna; Rasheed, Muhibur; Vollrath, Antje; Bajaj, Chandrajit

2015-01-01

There continue to be increasing occurrences of both atomistic structure models in the PDB (possibly reconstructed from X-ray diffraction or NMR data), and 3D reconstructed cryo-electron microscopy (3D EM) maps (albeit at coarser resolution) of the same or homologous molecule or molecular assembly, deposited in the EMDB. To obtain the best possible structural model of the molecule at the best achievable resolution, and without any missing gaps, one typically aligns (match and fits) the atomistic structure model with the 3D EM map. We discuss a new algorithm and generalized framework, named PF2 fit (Polar Fast Fourier Fitting) for the best possible structural alignment of atomistic structures with 3D EM. While PF2 fit enables only a rigid, six dimensional (6D) alignment method, it augments prior work on 6D X-ray structure and 3D EM alignment in multiple ways: Scoring. PF2 fit includes a new scoring scheme that, in addition to rewarding overlaps between the volumes occupied by the atomistic structure and 3D EM map, rewards overlaps between the volumes complementary to them. We quantitatively demonstrate how this new complementary scoring scheme improves upon existing approaches. PF2 fit also includes two scoring functions, the non-uniform exterior penalty and the skeleton-secondary structure score, and implements the scattering potential score as an alternative to traditional Gaussian blurring. Search. PF2 fit utilizes a fast polar Fourier search scheme, whose main advantage is the ability to search over uniformly and adaptively sampled subsets of the space of rigid-body motions. PF2 fit also implements a new reranking search and scoring methodology that considerably improves alignment metrics in results obtained from the initial search. PMID:26469938

Advancements to the planogram frequency–distance rebinning algorithm

PubMed Central

Champley, Kyle M; Raylman, Raymond R; Kinahan, Paul E

2010-01-01

In this paper we consider the task of image reconstruction in positron emission tomography (PET) with the planogram frequency–distance rebinning (PFDR) algorithm. The PFDR algorithm is a rebinning algorithm for PET systems with panel detectors. The algorithm is derived in the planogram coordinate system which is a native data format for PET systems with panel detectors. A rebinning algorithm averages over the redundant four-dimensional set of PET data to produce a three-dimensional set of data. Images can be reconstructed from this rebinned three-dimensional set of data. This process enables one to reconstruct PET images more quickly than reconstructing directly from the four-dimensional PET data. The PFDR algorithm is an approximate rebinning algorithm. We show that implementing the PFDR algorithm followed by the (ramp) filtered backprojection (FBP) algorithm in linogram coordinates from multiple views reconstructs a filtered version of our image. We develop an explicit formula for this filter which can be used to achieve exact reconstruction by means of a modified FBP algorithm applied to the stack of rebinned linograms and can also be used to quantify the errors introduced by the PFDR algorithm. This filter is similar to the filter in the planogram filtered backprojection algorithm derived by Brasse et al. The planogram filtered backprojection and exact reconstruction with the PFDR algorithm require complete projections which can be completed with a reprojection algorithm. The PFDR algorithm is similar to the rebinning algorithm developed by Kao et al. By expressing the PFDR algorithm in detector coordinates, we provide a comparative analysis between the two algorithms. Numerical experiments using both simulated data and measured data from a positron emission mammography/tomography (PEM/PET) system are performed. Images are reconstructed by PFDR+FBP (PFDR followed by 2D FBP reconstruction), PFDRX (PFDR followed by the modified FBP algorithm for exact reconstruction) and planogram filtered backprojection image reconstruction algorithms. We show that the PFDRX algorithm produces images that are nearly as accurate as images reconstructed with the planogram filtered backprojection algorithm and more accurate than images reconstructed with the PFDR+FBP algorithm. Both the PFDR+FBP and PFDRX algorithms provide a dramatic improvement in computation time over the planogram filtered backprojection algorithm. PMID:20436790

Multi-frequency time-difference complex conductivity imaging of canine and human lungs using the KHU Mark1 EIT system.

PubMed

Kuen, Jihyeon; Woo, Eung Je; Seo, Jin Keun

2009-06-01

We evaluated the performance of the lately developed electrical impedance tomography (EIT) system KHU Mark1 through time-difference imaging experiments of canine and human lungs. We derived a multi-frequency time-difference EIT (mftdEIT) image reconstruction algorithm based on the concept of the equivalent homogeneous complex conductivity. Imaging experiments were carried out at three different frequencies of 10, 50 and 100 kHz with three different postures of right lateral, sitting (or prone) and left lateral positions. For three normal canine subjects, we controlled the ventilation using a ventilator at three tidal volumes of 100, 150 and 200 ml. Three human subjects were asked to breath spontaneously at a normal tidal volume. Real- and imaginary-part images of the canine and human lungs were reconstructed at three frequencies and three postures. Images showed different stages of breathing cycles and we could interpret them based on the understanding of the proposed mftdEIT image reconstruction algorithm. Time series of images were further analyzed by using the functional EIT (fEIT) method. Images of human subjects showed the gravity effect on air distribution in two lungs. In the canine subjects, the morphological change seems to dominate the gravity effect. We could also observe that two different types of ventilation should have affected the results. The KHU Mark1 EIT system is expected to provide reliable mftdEIT images of the human lungs. In terms of the image reconstruction algorithm, it would be worthwhile including the effects of three-dimensional current flows inside the human thorax. We suggest clinical trials of the KHU Mark1 for pulmonary applications.

Analysis of an Optimized MLOS Tomographic Reconstruction Algorithm and Comparison to the MART Reconstruction Algorithm

NASA Astrophysics Data System (ADS)

La Foy, Roderick; Vlachos, Pavlos

2011-11-01

An optimally designed MLOS tomographic reconstruction algorithm for use in 3D PIV and PTV applications is analyzed. Using a set of optimized reconstruction parameters, the reconstructions produced by the MLOS algorithm are shown to be comparable to reconstructions produced by the MART algorithm for a range of camera geometries, camera numbers, and particle seeding densities. The resultant velocity field error calculated using PIV and PTV algorithms is further minimized by applying both pre and post processing to the reconstructed data sets.

Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability.

PubMed

Kim, Hyungjin; Park, Chang Min; Lee, Myunghee; Park, Sang Joon; Song, Yong Sub; Lee, Jong Hyuk; Hwang, Eui Jin; Goo, Jin Mo

2016-01-01

To identify the impact of reconstruction algorithms on CT radiomic features of pulmonary tumors and to reveal and compare the intra- and inter-reader and inter-reconstruction algorithm variability of each feature. Forty-two patients (M:F = 19:23; mean age, 60.43±10.56 years) with 42 pulmonary tumors (22.56±8.51mm) underwent contrast-enhanced CT scans, which were reconstructed with filtered back projection and commercial iterative reconstruction algorithm (level 3 and 5). Two readers independently segmented the whole tumor volume. Fifteen radiomic features were extracted and compared among reconstruction algorithms. Intra- and inter-reader variability and inter-reconstruction algorithm variability were calculated using coefficients of variation (CVs) and then compared. Among the 15 features, 5 first-order tumor intensity features and 4 gray level co-occurrence matrix (GLCM)-based features showed significant differences (p<0.05) among reconstruction algorithms. As for the variability, effective diameter, sphericity, entropy, and GLCM entropy were the most robust features (CV≤5%). Inter-reader variability was larger than intra-reader or inter-reconstruction algorithm variability in 9 features. However, for entropy, homogeneity, and 4 GLCM-based features, inter-reconstruction algorithm variability was significantly greater than inter-reader variability (p<0.013). Most of the radiomic features were significantly affected by the reconstruction algorithms. Inter-reconstruction algorithm variability was greater than inter-reader variability for entropy, homogeneity, and GLCM-based features.

Automatic segmentation and reconstruction of the cortex from neonatal MRI.

PubMed

Xue, Hui; Srinivasan, Latha; Jiang, Shuzhou; Rutherford, Mary; Edwards, A David; Rueckert, Daniel; Hajnal, Joseph V

2007-11-15

Segmentation and reconstruction of cortical surfaces from magnetic resonance (MR) images are more challenging for developing neonates than adults. This is mainly due to the dynamic changes in the contrast between gray matter (GM) and white matter (WM) in both T1- and T2-weighted images (T1w and T2w) during brain maturation. In particular in neonatal T2w images WM typically has higher signal intensity than GM. This causes mislabeled voxels during cortical segmentation, especially in the cortical regions of the brain and in particular at the interface between GM and cerebrospinal fluid (CSF). We propose an automatic segmentation algorithm detecting these mislabeled voxels and correcting errors caused by partial volume effects. Our results show that the proposed algorithm corrects errors in the segmentation of both GM and WM compared to the classic expectation maximization (EM) scheme. Quantitative validation against manual segmentation demonstrates good performance (the mean Dice value: 0.758+/-0.037 for GM and 0.794+/-0.078 for WM). The inner, central and outer cortical surfaces are then reconstructed using implicit surface evolution. A landmark study is performed to verify the accuracy of the reconstructed cortex (the mean surface reconstruction error: 0.73 mm for inner surface and 0.63 mm for the outer). Both segmentation and reconstruction have been tested on 25 neonates with the gestational ages ranging from approximately 27 to 45 weeks. This preliminary analysis confirms previous findings that cortical surface area and curvature increase with age, and that surface area scales to cerebral volume according to a power law, while cortical thickness is not related to age or brain growth.

Optimization of Rb-82 PET acquisition and reconstruction protocols for myocardial perfusion defect detection

NASA Astrophysics Data System (ADS)

Tang, Jing; Rahmim, Arman; Lautamäki, Riikka; Lodge, Martin A.; Bengel, Frank M.; Tsui, Benjamin M. W.

2009-05-01

The purpose of this study is to optimize the dynamic Rb-82 cardiac PET acquisition and reconstruction protocols for maximum myocardial perfusion defect detection using realistic simulation data and task-based evaluation. Time activity curves (TACs) of different organs under both rest and stress conditions were extracted from dynamic Rb-82 PET images of five normal patients. Combined SimSET-GATE Monte Carlo simulation was used to generate nearly noise-free cardiac PET data from a time series of 3D NCAT phantoms with organ activities modeling different pre-scan delay times (PDTs) and total acquisition times (TATs). Poisson noise was added to the nearly noise-free projections and the OS-EM algorithm was applied to generate noisy reconstructed images. The channelized Hotelling observer (CHO) with 32× 32 spatial templates corresponding to four octave-wide frequency channels was used to evaluate the images. The area under the ROC curve (AUC) was calculated from the CHO rating data as an index for image quality in terms of myocardial perfusion defect detection. The 0.5 cycle cm-1 Butterworth post-filtering on OS-EM (with 21 subsets) reconstructed images generates the highest AUC values while those from iteration numbers 1 to 4 do not show different AUC values. The optimized PDTs for both rest and stress conditions are found to be close to the cross points of the left ventricular chamber and myocardium TACs, which may promote an individualized PDT for patient data processing and image reconstruction. Shortening the TATs for <~3 min from the clinically employed acquisition time does not affect the myocardial perfusion defect detection significantly for both rest and stress studies.

A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.

PubMed

Zhu, Yanan; Ouyang, Qi; Mao, Youdong

2017-07-21

Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs. We developed a deep learning-based algorithmic framework, DeepEM, for single-particle recognition from noisy cryo-EM micrographs, enabling automated particle picking, selection and verification in an integrated fashion. The kernel of DeepEM is built upon a convolutional neural network (CNN) composed of eight layers, which can be recursively trained to be highly "knowledgeable". Our approach exhibits an improved performance and accuracy when tested on the standard KLH dataset. Application of DeepEM to several challenging experimental cryo-EM datasets demonstrated its ability to avoid the selection of un-wanted particles and non-particles even when true particles contain fewer features. The DeepEM methodology, derived from a deep CNN, allows automated particle extraction from raw cryo-EM micrographs in the absence of a template. It demonstrates an improved performance, objectivity and accuracy. Application of this novel method is expected to free the labor involved in single-particle verification, significantly improving the efficiency of cryo-EM data processing.

Study on the algorithm of computational ghost imaging based on discrete fourier transform measurement matrix

NASA Astrophysics Data System (ADS)

Zhang, Leihong; Liang, Dong; Li, Bei; Kang, Yi; Pan, Zilan; Zhang, Dawei; Gao, Xiumin; Ma, Xiuhua

2016-07-01

On the basis of analyzing the cosine light field with determined analytic expression and the pseudo-inverse method, the object is illuminated by a presetting light field with a determined discrete Fourier transform measurement matrix, and the object image is reconstructed by the pseudo-inverse method. The analytic expression of the algorithm of computational ghost imaging based on discrete Fourier transform measurement matrix is deduced theoretically, and compared with the algorithm of compressive computational ghost imaging based on random measurement matrix. The reconstruction process and the reconstruction error are analyzed. On this basis, the simulation is done to verify the theoretical analysis. When the sampling measurement number is similar to the number of object pixel, the rank of discrete Fourier transform matrix is the same as the one of the random measurement matrix, the PSNR of the reconstruction image of FGI algorithm and PGI algorithm are similar, the reconstruction error of the traditional CGI algorithm is lower than that of reconstruction image based on FGI algorithm and PGI algorithm. As the decreasing of the number of sampling measurement, the PSNR of reconstruction image based on FGI algorithm decreases slowly, and the PSNR of reconstruction image based on PGI algorithm and CGI algorithm decreases sharply. The reconstruction time of FGI algorithm is lower than that of other algorithms and is not affected by the number of sampling measurement. The FGI algorithm can effectively filter out the random white noise through a low-pass filter and realize the reconstruction denoising which has a higher denoising capability than that of the CGI algorithm. The FGI algorithm can improve the reconstruction accuracy and the reconstruction speed of computational ghost imaging.

Reconstruction of 2D PET data with Monte Carlo generated system matrix for generalized natural pixels

NASA Astrophysics Data System (ADS)

Vandenberghe, Stefaan; Staelens, Steven; Byrne, Charles L.; Soares, Edward J.; Lemahieu, Ignace; Glick, Stephen J.

2006-06-01

In discrete detector PET, natural pixels are image basis functions calculated from the response of detector pairs. By using reconstruction with natural pixel basis functions, the discretization of the object into a predefined grid can be avoided. Here, we propose to use generalized natural pixel reconstruction. Using this approach, the basis functions are not the detector sensitivity functions as in the natural pixel case but uniform parallel strips. The backprojection of the strip coefficients results in the reconstructed image. This paper proposes an easy and efficient way to generate the matrix M directly by Monte Carlo simulation. Elements of the generalized natural pixel system matrix are formed by calculating the intersection of a parallel strip with the detector sensitivity function. These generalized natural pixels are easier to use than conventional natural pixels because the final step from solution to a square pixel representation is done by simple backprojection. Due to rotational symmetry in the PET scanner, the matrix M is block circulant and only the first blockrow needs to be stored. Data were generated using a fast Monte Carlo simulator using ray tracing. The proposed method was compared to a listmode MLEM algorithm, which used ray tracing for doing forward and backprojection. Comparison of the algorithms with different phantoms showed that an improved resolution can be obtained using generalized natural pixel reconstruction with accurate system modelling. In addition, it was noted that for the same resolution a lower noise level is present in this reconstruction. A numerical observer study showed the proposed method exhibited increased performance as compared to a standard listmode EM algorithm. In another study, more realistic data were generated using the GATE Monte Carlo simulator. For these data, a more uniform contrast recovery and a better contrast-to-noise performance were observed. It was observed that major improvements in contrast recovery were obtained with MLEM when the correct system matrix was used instead of simple ray tracing. The correct modelling was the major cause of improved contrast for the same background noise. Less important factors were the choice of the algorithm (MLEM performed better than ART) and the basis functions (generalized natural pixels gave better results than pixels).

Actively triggered 4d cone-beam CT acquisition.

PubMed

Fast, Martin F; Wisotzky, Eric; Oelfke, Uwe; Nill, Simeon

2013-09-01

4d cone-beam computed tomography (CBCT) scans are usually reconstructed by extracting the motion information from the 2d projections or an external surrogate signal, and binning the individual projections into multiple respiratory phases. In this "after-the-fact" binning approach, however, projections are unevenly distributed over respiratory phases resulting in inefficient utilization of imaging dose. To avoid excess dose in certain respiratory phases, and poor image quality due to a lack of projections in others, the authors have developed a novel 4d CBCT acquisition framework which actively triggers 2d projections based on the forward-predicted position of the tumor. The forward-prediction of the tumor position was independently established using either (i) an electromagnetic (EM) tracking system based on implanted EM-transponders which act as a surrogate for the tumor position, or (ii) an external motion sensor measuring the chest-wall displacement and correlating this external motion to the phase-shifted diaphragm motion derived from the acquired images. In order to avoid EM-induced artifacts in the imaging detector, the authors devised a simple but effective "Faraday" shielding cage. The authors demonstrated the feasibility of their acquisition strategy by scanning an anthropomorphic lung phantom moving on 1d or 2d sinusoidal trajectories. With both tumor position devices, the authors were able to acquire 4d CBCTs free of motion blurring. For scans based on the EM tracking system, reconstruction artifacts stemming from the presence of the EM-array and the EM-transponders were greatly reduced using newly developed correction algorithms. By tuning the imaging frequency independently for each respiratory phase prior to acquisition, it was possible to harmonize the number of projections over respiratory phases. Depending on the breathing period (3.5 or 5 s) and the gantry rotation time (4 or 5 min), between ∼90 and 145 projections were acquired per respiratory phase resulting in a dose of ∼1.7-2.6 mGy per respiratory phase. Further dose savings and decreases in the scanning time are possible by acquiring only a subset of all respiratory phases, for example, peak-exhale and peak-inhale only scans. This study is the first experimental demonstration of a new 4d CBCT acquisition paradigm in which imaging dose is efficiently utilized by actively triggering only those projections that are desired for the reconstruction process.

Spatial performance of RegEM climate field reconstruction techniques in a realistic pseudoproxy context

NASA Astrophysics Data System (ADS)

Wang, J.; Emile-Geay, J.; Guillot, D.

2011-12-01

Several methods of climate field reconstructions (CFRs) have been introduced in the past few years to estimate past climate variability from proxy data over the Common Era. The pseudoproxy framework has become a tool of choice for assessing the relative merits of such methods. Here we compare four variants of the RegEM algorithm [Schneider, 2001], using a pseudoproxy network mimicking the key spatio-temporal characteristics of the network of Mann et al., 2008 (hereinafter M08); the methods are (1) RegEM TTLS (2) RegEM iTTLS (3) GraphEM and (4) RegEM iRIDGE. To ensure continuity with previous work [Smerdon et al. 2011], pseudoproxy series are designed as a white-noise degraded version of the simulated temperature field [Amman et al. 2007] over 850-1980 C.E. colocated with 1138 M08 proxies. We use signal-to-noise ratios (SNRs) of: ∞ (no noise), 1.0, 0.5 and 0.25, to simulate differences in proxy quality. Two novelties in pseudoproxy design are introduced here: (1) the decrease in proxy availability over time follows that found in M08, (2) a realistic case where the SNR is empirically derived from correlations between each M08 proxy and the HadCRUT3v temperature field. It is found that this realistic SNR is clustered around 0.3, but ranges from 0.1 to 0.8. Verification statistics such as RE, CE, r2, bias, standard deviation ratio and RMSE are presented for each method at each SNR level. The results show that all methods perform relatively well at SNR levels higher than 0.5, but display drastically different performances at lower SNR levels. Compared with results using pseudoproxy network of Mann et al., 1998, (hereinafter MBH98), the reconstruction skill of the M08 network is relatively improved, in line with the findings of Smerdon et al., 2011. Overall, we find that GraphEM and iTTLS tend to produce more robust estimates of the temperature field at low SNR levels than other schemes, while preserving a higher amount of variance in the target field. Ammann, C. M., F. Joos, D. S. Schimel, B. L. Otto-Bliesner, and R. A. Tomas (2007), Solar influence on climate during the past millennium: Results from transient simulations with the NCAR Climate System Model, Proc. Natl. Acad. Sci. U. S. A., 104, 3713-3718, doi:10.1073/pnas.0605064103. Mann, M. E., R. S. Bradley, and M. K. Hughes (1998), Global-scale temperaturepatterns and climate forcing over the past six centuries, Nature, 392, 779-787, doi:10.1038/33859. Mann, M. E., S. Rutherford, E. Wahl, and C. Ammann (2007), Robustness of proxy-based climate field reconstruction methods, J. Geophys. Res., 112, D12109, doi:10.1029/2006JD008272. Mann, M. E., et al. (2008), Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia, Proc. Natl. Acad. Sci. U. S. A., 105, 13,252-13,257, doi:10.1073/pnas.0805721105. Schneider, T. (2001), Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values, J. Clim., 14, 853-871, doi:10.1175/1520-0442(2001)014<0853: AOICDE>2.0.CO;2. Smerdon, J. E., A. Kaplan, E. Zorita, J. F. González-Rouco, and M. N. Evans (2011), Spatial performance of four climate field reconstruction methods targeting the Common Era, Geophys. Res. Lett., 38, L11705, doi:10.1029/2011GL047372.

Research on compressive sensing reconstruction algorithm based on total variation model

NASA Astrophysics Data System (ADS)

Gao, Yu-xuan; Sun, Huayan; Zhang, Tinghua; Du, Lin

2017-12-01

Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.

Digital tomosynthesis mammography using a parallel maximum-likelihood reconstruction method

NASA Astrophysics Data System (ADS)

Wu, Tao; Zhang, Juemin; Moore, Richard; Rafferty, Elizabeth; Kopans, Daniel; Meleis, Waleed; Kaeli, David

2004-05-01

A parallel reconstruction method, based on an iterative maximum likelihood (ML) algorithm, is developed to provide fast reconstruction for digital tomosynthesis mammography. Tomosynthesis mammography acquires 11 low-dose projections of a breast by moving an x-ray tube over a 50° angular range. In parallel reconstruction, each projection is divided into multiple segments along the chest-to-nipple direction. Using the 11 projections, segments located at the same distance from the chest wall are combined to compute a partial reconstruction of the total breast volume. The shape of the partial reconstruction forms a thin slab, angled toward the x-ray source at a projection angle 0°. The reconstruction of the total breast volume is obtained by merging the partial reconstructions. The overlap region between neighboring partial reconstructions and neighboring projection segments is utilized to compensate for the incomplete data at the boundary locations present in the partial reconstructions. A serial execution of the reconstruction is compared to a parallel implementation, using clinical data. The serial code was run on a PC with a single PentiumIV 2.2GHz CPU. The parallel implementation was developed using MPI and run on a 64-node Linux cluster using 800MHz Itanium CPUs. The serial reconstruction for a medium-sized breast (5cm thickness, 11cm chest-to-nipple distance) takes 115 minutes, while a parallel implementation takes only 3.5 minutes. The reconstruction time for a larger breast using a serial implementation takes 187 minutes, while a parallel implementation takes 6.5 minutes. No significant differences were observed between the reconstructions produced by the serial and parallel implementations.

Diagnostic accuracy of second-generation dual-source computed tomography coronary angiography with iterative reconstructions: a real-world experience.

PubMed

Maffei, E; Martini, C; Rossi, A; Mollet, N; Lario, C; Castiglione Morelli, M; Clemente, A; Gentile, G; Arcadi, T; Seitun, S; Catalano, O; Aldrovandi, A; Cademartiri, F

2012-08-01

The authors evaluated the diagnostic accuracy of second-generation dual-source (DSCT) computed tomography coronary angiography (CTCA) with iterative reconstructions for detecting obstructive coronary artery disease (CAD). Between June 2010 and February 2011, we enrolled 160 patients (85 men; mean age 61.2±11.6 years) with suspected CAD. All patients underwent CTCA and conventional coronary angiography (CCA). For the CTCA scan (Definition Flash, Siemens), we use prospective tube current modulation and 70-100 ml of iodinated contrast material (Iomeprol 400 mgI/ ml, Bracco). Data sets were reconstructed with iterative reconstruction algorithm (IRIS, Siemens). CTCA and CCA reports were used to evaluate accuracy using the threshold for significant stenosis at ≥50% and ≥70%, respectively. No patient was excluded from the analysis. Heart rate was 64.3±11.9 bpm and radiation dose was 7.2±2.1 mSv. Disease prevalence was 30% (48/160). Sensitivity, specificity and positive and negative predictive values of CTCA in detecting significant stenosis were 90.1%, 93.3%, 53.2% and 99.1% (per segment), 97.5%, 91.2%, 61.4% and 99.6% (per vessel) and 100%, 83%, 71.6% and 100% (per patient), respectively. Positive and negative likelihood ratios at the per-patient level were 5.89 and 0.0, respectively. CTCA with second-generation DSCT in the real clinical world shows a diagnostic performance comparable with previously reported validation studies. The excellent negative predictive value and likelihood ratio make CTCA a first-line noninvasive method for diagnosing obstructive CAD.

Low-contrast lesion detection in tomosynthetic breast imaging using a realistic breast phantom

NASA Astrophysics Data System (ADS)

Zhou, Lili; Oldan, Jorge; Fisher, Paul; Gindi, Gene

2006-03-01

Tomosynthesis mammography is a potentially valuable technique for detection of breast cancer. In this simulation study, we investigate the efficacy of three different tomographic reconstruction methods, EM, SART and Backprojection, in the context of an especially difficult mammographic detection task. The task is the detection of a very low-contrast mass embedded in very dense fibro-glandular tissue - a clinically useful task for which tomosynthesis may be well suited. The project uses an anatomically realistic 3D digital breast phantom whose normal anatomic variability limits lesion conspicuity. In order to capture anatomical object variability, we generate an ensemble of phantoms, each of which comprises random instances of various breast structures. We construct medium-sized 3D breast phantoms which model random instances of ductal structures, fibrous connective tissue, Cooper's ligaments and power law structural noise for small scale object variability. Random instances of 7-8 mm irregular masses are generated by a 3D random walk algorithm and placed in very dense fibro-glandular tissue. Several other components of the breast phantom are held fixed, i.e. not randomly generated. These include the fixed breast shape and size, nipple structure, fixed lesion location, and a pectoralis muscle. We collect low-dose data using an isocentric tomosynthetic geometry at 11 angles over 50 degrees and add Poisson noise. The data is reconstructed using the three algorithms. Reconstructed slices through the center of the lesion are presented to human observers in a 2AFC (two-alternative-forced-choice) test that measures detectability by computing AUC (area under the ROC curve). The data collected in each simulation includes two sources of variability, that due to the anatomical variability of the phantom and that due to the Poisson data noise. We found that for this difficult task that the AUC value for EM (0.89) was greater than that for SART (0.83) and Backprojection (0.66).

A constraint-based evolutionary learning approach to the expectation maximization for optimal estimation of the hidden Markov model for speech signal modeling.

PubMed

Huda, Shamsul; Yearwood, John; Togneri, Roberto

2009-02-01

This paper attempts to overcome the tendency of the expectation-maximization (EM) algorithm to locate a local rather than global maximum when applied to estimate the hidden Markov model (HMM) parameters in speech signal modeling. We propose a hybrid algorithm for estimation of the HMM in automatic speech recognition (ASR) using a constraint-based evolutionary algorithm (EA) and EM, the CEL-EM. The novelty of our hybrid algorithm (CEL-EM) is that it is applicable for estimation of the constraint-based models with many constraints and large numbers of parameters (which use EM) like HMM. Two constraint-based versions of the CEL-EM with different fusion strategies have been proposed using a constraint-based EA and the EM for better estimation of HMM in ASR. The first one uses a traditional constraint-handling mechanism of EA. The other version transforms a constrained optimization problem into an unconstrained problem using Lagrange multipliers. Fusion strategies for the CEL-EM use a staged-fusion approach where EM has been plugged with the EA periodically after the execution of EA for a specific period of time to maintain the global sampling capabilities of EA in the hybrid algorithm. A variable initialization approach (VIA) has been proposed using a variable segmentation to provide a better initialization for EA in the CEL-EM. Experimental results on the TIMIT speech corpus show that CEL-EM obtains higher recognition accuracies than the traditional EM algorithm as well as a top-standard EM (VIA-EM, constructed by applying the VIA to EM).

GPU implementation of prior image constrained compressed sensing (PICCS)

NASA Astrophysics Data System (ADS)

Nett, Brian E.; Tang, Jie; Chen, Guang-Hong

2010-04-01

The Prior Image Constrained Compressed Sensing (PICCS) algorithm (Med. Phys. 35, pg. 660, 2008) has been applied to several computed tomography applications with both standard CT systems and flat-panel based systems designed for guiding interventional procedures and radiation therapy treatment delivery. The PICCS algorithm typically utilizes a prior image which is reconstructed via the standard Filtered Backprojection (FBP) reconstruction algorithm. The algorithm then iteratively solves for the image volume that matches the measured data, while simultaneously assuring the image is similar to the prior image. The PICCS algorithm has demonstrated utility in several applications including: improved temporal resolution reconstruction, 4D respiratory phase specific reconstructions for radiation therapy, and cardiac reconstruction from data acquired on an interventional C-arm. One disadvantage of the PICCS algorithm, just as other iterative algorithms, is the long computation times typically associated with reconstruction. In order for an algorithm to gain clinical acceptance reconstruction must be achievable in minutes rather than hours. In this work the PICCS algorithm has been implemented on the GPU in order to significantly reduce the reconstruction time of the PICCS algorithm. The Compute Unified Device Architecture (CUDA) was used in this implementation.

Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2014-04-01

A new algorithm for the reconstruction of electrocardiogram (ECG) signals and a dictionary learning algorithm for the enhancement of its reconstruction performance for a class of signals are proposed. The signal reconstruction algorithm is based on minimizing the lp pseudo-norm of the second-order difference, called as the lp(2d) pseudo-norm, of the signal. The optimization involved is carried out using a sequential conjugate-gradient algorithm. The dictionary learning algorithm uses an iterative procedure wherein a signal reconstruction and a dictionary update steps are repeated until a convergence criterion is satisfied. The signal reconstruction step is implemented by using the proposed signal reconstruction algorithm and the dictionary update step is implemented by using the linear least-squares method. Extensive simulation results demonstrate that the proposed algorithm yields improved reconstruction performance for temporally correlated ECG signals relative to the state-of-the-art lp(1d)-regularized least-squares and Bayesian learning based algorithms. Also for a known class of signals, the reconstruction performance of the proposed algorithm can be improved by applying it in conjunction with a dictionary obtained using the proposed dictionary learning algorithm.

Covariance Matrix Estimation for the Cryo-EM Heterogeneity Problem*

PubMed Central

Katsevich, E.; Katsevich, A.; Singer, A.

2015-01-01

In cryo-electron microscopy (cryo-EM), a microscope generates a top view of a sample of randomly oriented copies of a molecule. The problem of single particle reconstruction (SPR) from cryo-EM is to use the resulting set of noisy two-dimensional projection images taken at unknown directions to reconstruct the three-dimensional (3D) structure of the molecule. In some situations, the molecule under examination exhibits structural variability, which poses a fundamental challenge in SPR. The heterogeneity problem is the task of mapping the space of conformational states of a molecule. It has been previously suggested that the leading eigenvectors of the covariance matrix of the 3D molecules can be used to solve the heterogeneity problem. Estimating the covariance matrix is challenging, since only projections of the molecules are observed, but not the molecules themselves. In this paper, we formulate a general problem of covariance estimation from noisy projections of samples. This problem has intimate connections with matrix completion problems and high-dimensional principal component analysis. We propose an estimator and prove its consistency. When there are finitely many heterogeneity classes, the spectrum of the estimated covariance matrix reveals the number of classes. The estimator can be found as the solution to a certain linear system. In the cryo-EM case, the linear operator to be inverted, which we term the projection covariance transform, is an important object in covariance estimation for tomographic problems involving structural variation. Inverting it involves applying a filter akin to the ramp filter in tomography. We design a basis in which this linear operator is sparse and thus can be tractably inverted despite its large size. We demonstrate via numerical experiments on synthetic datasets the robustness of our algorithm to high levels of noise. PMID:25699132

Robust multiperson detection and tracking for mobile service and social robots.

PubMed

Li, Liyuan; Yan, Shuicheng; Yu, Xinguo; Tan, Yeow Kee; Li, Haizhou

2012-10-01

This paper proposes an efficient system which integrates multiple vision models for robust multiperson detection and tracking for mobile service and social robots in public environments. The core technique is a novel maximum likelihood (ML)-based algorithm which combines the multimodel detections in mean-shift tracking. First, a likelihood probability which integrates detections and similarity to local appearance is defined. Then, an expectation-maximization (EM)-like mean-shift algorithm is derived under the ML framework. In each iteration, the E-step estimates the associations to the detections, and the M-step locates the new position according to the ML criterion. To be robust to the complex crowded scenarios for multiperson tracking, an improved sequential strategy to perform the mean-shift tracking is proposed. Under this strategy, human objects are tracked sequentially according to their priority order. To balance the efficiency and robustness for real-time performance, at each stage, the first two objects from the list of the priority order are tested, and the one with the higher score is selected. The proposed method has been successfully implemented on real-world service and social robots. The vision system integrates stereo-based and histograms-of-oriented-gradients-based human detections, occlusion reasoning, and sequential mean-shift tracking. Various examples to show the advantages and robustness of the proposed system for multiperson tracking from mobile robots are presented. Quantitative evaluations on the performance of multiperson tracking are also performed. Experimental results indicate that significant improvements have been achieved by using the proposed method.

The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation.

PubMed

Zhao, Zhanqi; Frerichs, Inéz; Pulletz, Sven; Müller-Lisse, Ullrich; Möller, Knut

2014-06-01

Analysis methods of electrical impedance tomography (EIT) images based on different reconstruction algorithms were examined. EIT measurements were performed on eight mechanically ventilated patients with acute respiratory distress syndrome. A maneuver with step increase of airway pressure was performed. EIT raw data were reconstructed offline with (1) filtered back-projection (BP); (2) the Dräger algorithm based on linearized Newton-Raphson (DR); (3) the GREIT (Graz consensus reconstruction algorithm for EIT) reconstruction algorithm with a circular forward model (GR(C)) and (4) GREIT with individual thorax geometry (GR(T)). Individual thorax contours were automatically determined from the routine computed tomography images. Five indices were calculated on the resulting EIT images respectively: (a) the ratio between tidal and deep inflation impedance changes; (b) tidal impedance changes in the right and left lungs; (c) center of gravity; (d) the global inhomogeneity index and (e) ventilation delay at mid-dorsal regions. No significant differences were found in all examined indices among the four reconstruction algorithms (p > 0.2, Kruskal-Wallis test). The examined algorithms used for EIT image reconstruction do not influence the selected indices derived from the EIT image analysis. Indices that validated for images with one reconstruction algorithm are also valid for other reconstruction algorithms.

Filtered refocusing: a volumetric reconstruction algorithm for plenoptic-PIV

NASA Astrophysics Data System (ADS)

Fahringer, Timothy W.; Thurow, Brian S.

2016-09-01

A new algorithm for reconstruction of 3D particle fields from plenoptic image data is presented. The algorithm is based on the technique of computational refocusing with the addition of a post reconstruction filter to remove the out of focus particles. This new algorithm is tested in terms of reconstruction quality on synthetic particle fields as well as a synthetically generated 3D Gaussian ring vortex. Preliminary results indicate that the new algorithm performs as well as the MART algorithm (used in previous work) in terms of the reconstructed particle position accuracy, but produces more elongated particles. The major advantage to the new algorithm is the dramatic reduction in the computational cost required to reconstruct a volume. It is shown that the new algorithm takes 1/9th the time to reconstruct the same volume as MART while using minimal resources. Experimental results are presented in the form of the wake behind a cylinder at a Reynolds number of 185.

Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography

PubMed Central

Wang, Kun; Su, Richard; Oraevsky, Alexander A; Anastasio, Mark A

2012-01-01

Iterative image reconstruction algorithms for optoacoustic tomography (OAT), also known as photoacoustic tomography, have the ability to improve image quality over analytic algorithms due to their ability to incorporate accurate models of the imaging physics, instrument response, and measurement noise. However, to date, there have been few reported attempts to employ advanced iterative image reconstruction algorithms for improving image quality in three-dimensional (3D) OAT. In this work, we implement and investigate two iterative image reconstruction methods for use with a 3D OAT small animal imager: namely, a penalized least-squares (PLS) method employing a quadratic smoothness penalty and a PLS method employing a total variation norm penalty. The reconstruction algorithms employ accurate models of the ultrasonic transducer impulse responses. Experimental data sets are employed to compare the performances of the iterative reconstruction algorithms to that of a 3D filtered backprojection (FBP) algorithm. By use of quantitative measures of image quality, we demonstrate that the iterative reconstruction algorithms can mitigate image artifacts and preserve spatial resolution more effectively than FBP algorithms. These features suggest that the use of advanced image reconstruction algorithms can improve the effectiveness of 3D OAT while reducing the amount of data required for biomedical applications. PMID:22864062

Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms.

PubMed

Tang, Jie; Nett, Brian E; Chen, Guang-Hong

2009-10-07

Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.

SU-E-J-218: Evaluation of CT Images Created Using a New Metal Artifact Reduction Reconstruction Algorithm for Radiation Therapy Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niemkiewicz, J; Palmiotti, A; Miner, M

2014-06-01

Purpose: Metal in patients creates streak artifacts in CT images. When used for radiation treatment planning, these artifacts make it difficult to identify internal structures and affects radiation dose calculations, which depend on HU numbers for inhomogeneity correction. This work quantitatively evaluates a new metal artifact reduction (MAR) CT image reconstruction algorithm (GE Healthcare CT-0521-04.13-EN-US DOC1381483) when metal is present. Methods: A Gammex Model 467 Tissue Characterization phantom was used. CT images were taken of this phantom on a GE Optima580RT CT scanner with and without steel and titanium plugs using both the standard and MAR reconstruction algorithms. HU valuesmore » were compared pixel by pixel to determine if the MAR algorithm altered the HUs of normal tissues when no metal is present, and to evaluate the effect of using the MAR algorithm when metal is present. Also, CT images of patients with internal metal objects using standard and MAR reconstruction algorithms were compared. Results: Comparing the standard and MAR reconstructed images of the phantom without metal, 95.0% of pixels were within ±35 HU and 98.0% of pixels were within ±85 HU. Also, the MAR reconstruction algorithm showed significant improvement in maintaining HUs of non-metallic regions in the images taken of the phantom with metal. HU Gamma analysis (2%, 2mm) of metal vs. non-metal phantom imaging using standard reconstruction resulted in an 84.8% pass rate compared to 96.6% for the MAR reconstructed images. CT images of patients with metal show significant artifact reduction when reconstructed with the MAR algorithm. Conclusion: CT imaging using the MAR reconstruction algorithm provides improved visualization of internal anatomy and more accurate HUs when metal is present compared to the standard reconstruction algorithm. MAR reconstructed CT images provide qualitative and quantitative improvements over current reconstruction algorithms, thus improving radiation treatment planning accuracy.« less

Fast direct fourier reconstruction of radial and PROPELLER MRI data using the chirp transform algorithm on graphics hardware.

PubMed

Feng, Yanqiu; Song, Yanli; Wang, Cong; Xin, Xuegang; Feng, Qianjin; Chen, Wufan

2013-10-01

To develop and test a new algorithm for fast direct Fourier transform (DrFT) reconstruction of MR data on non-Cartesian trajectories composed of lines with equally spaced points. The DrFT, which is normally used as a reference in evaluating the accuracy of other reconstruction methods, can reconstruct images directly from non-Cartesian MR data without interpolation. However, DrFT reconstruction involves substantially intensive computation, which makes the DrFT impractical for clinical routine applications. In this article, the Chirp transform algorithm was introduced to accelerate the DrFT reconstruction of radial and Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) MRI data located on the trajectories that are composed of lines with equally spaced points. The performance of the proposed Chirp transform algorithm-DrFT algorithm was evaluated by using simulation and in vivo MRI data. After implementing the algorithm on a graphics processing unit, the proposed Chirp transform algorithm-DrFT algorithm achieved an acceleration of approximately one order of magnitude, and the speed-up factor was further increased to approximately three orders of magnitude compared with the traditional single-thread DrFT reconstruction. Implementation the Chirp transform algorithm-DrFT algorithm on the graphics processing unit can efficiently calculate the DrFT reconstruction of the radial and PROPELLER MRI data. Copyright © 2012 Wiley Periodicals, Inc.

Automated liver sampling using a gradient dual-echo Dixon-based technique.

PubMed

Bashir, Mustafa R; Dale, Brian M; Merkle, Elmar M; Boll, Daniel T

2012-05-01

Magnetic resonance spectroscopy of the liver requires input from a physicist or physician at the time of acquisition to insure proper voxel selection, while in multiecho chemical shift imaging, numerous regions of interest must be manually selected in order to ensure analysis of a representative portion of the liver parenchyma. A fully automated technique could improve workflow by selecting representative portions of the liver prior to human analysis. Complete volumes from three-dimensional gradient dual-echo acquisitions with two-point Dixon reconstruction acquired at 1.5 and 3 T were analyzed in 100 subjects, using an automated liver sampling algorithm, based on ratio pairs calculated from signal intensity image data as fat-only/water-only and log(in-phase/opposed-phase) on a voxel-by-voxel basis. Using different gridding variations of the algorithm, the average correct liver volume samples ranged from 527 to 733 mL. The average percentage of sample located within the liver ranged from 95.4 to 97.1%, whereas the average incorrect volume selected was 16.5-35.4 mL (2.9-4.6%). Average run time was 19.7-79.0 s. The algorithm consistently selected large samples of the hepatic parenchyma with small amounts of erroneous extrahepatic sampling, and run times were feasible for execution on an MRI system console during exam acquisition. Copyright © 2011 Wiley Periodicals, Inc.

Primary Fat Grafting to the Pectoralis Muscle during Latissimus Dorsi Breast Reconstruction.

PubMed

Niddam, Jeremy; Vidal, Luciano; Hersant, Barbara; Meningaud, Jean Paul

2016-11-01

Latissimus dorsi flap is one of the best options for immediate and delayed breast reconstruction. However, this technique is limited by the tissue volume provided by the flap. To improve breast volume while reducing complications, fat grafting is now very often used in addition to latissimus dorsi flap. To the best of our knowledge, fat grafting was always performed as a second-line surgery, at least a few months after the flap procedure. We aimed to report our experience with an associated breast reconstruction technique combining musculocutaneous latissimus dorsi flap with intrapectoral lipofilling for totally autologous breast reconstruction. Between September 2014 and January 2015, 20 patients underwent this technique for unilateral autologous breast reconstruction (14 delayed and 6 immediate breast reconstructions). A mean harvested fat volume of 278 ml (range: 190-350 ml) and a mean injected fat volume of 228 ml (range: 170-280 ml) were used. None of the patients experienced complications, such as flap necrosis, breast skin necrosis, hematomas, or infection. One of the patients developed a seroma, which was treated with 3 drainage punctions. Only 2 patients underwent delayed fat grafting procedure. Totally autologous breast reconstruction combining latissimus dorsi flap and intrapectoral fat grafting in the same procedure is a new technique allowing increased breast volume in a single surgery.

Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.

PubMed

Altazi, Baderaldeen A; Zhang, Geoffrey G; Fernandez, Daniel C; Montejo, Michael E; Hunt, Dylan; Werner, Joan; Biagioli, Matthew C; Moros, Eduardo G

2017-11-01

Site-specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from 18 Flourine-fluorodeoxyglucose ( 18 F-FDG) PET images for three parameters: manual versus computer-aided segmentation methods, gray-level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board-certified radiation oncologists manually segmented the metabolic tumor volume (MTV 1 and MTV 2 ) for each patient. For comparison, we used a graphical-based method to generate semiautomated segmented volumes (GBSV). To address any perturbations in radiomic feature values, we down-sampled the tumor volumes into three gray-levels: 32, 64, and 128 from the original gray-level of 256. Finally, we analyzed the effect on radiomic features on PET images of eight patients due to four PET 3D-reconstruction algorithms: maximum likelihood-ordered subset expectation maximization (OSEM) iterative reconstruction (IR) method, fourier rebinning-ML-OSEM (FOREIR), FORE-filtered back projection (FOREFBP), and 3D-Reprojection (3DRP) analytical method. We extracted 79 features from all segmentation method, gray-levels of down-sampled volumes, and PET reconstruction algorithms. The features were extracted using gray-level co-occurrence matrices (GLCM), gray-level size zone matrices (GLSZM), gray-level run-length matrices (GLRLM), neighborhood gray-tone difference matrices (NGTDM), shape-based features (SF), and intensity histogram features (IHF). We computed the Dice coefficient between each MTV and GBSV to measure segmentation accuracy. Coefficient values close to one indicate high agreement, and values close to zero indicate low agreement. We evaluated the effect on radiomic features by calculating the mean percentage differences (d¯) between feature values measured from each pair of parameter elements (i.e. segmentation methods: MTV 1 -MTV 2 , MTV 1 -GBSV, MTV 2 -GBSV; gray-levels: 64-32, 64-128, and 64-256; reconstruction algorithms: OSEM-FORE-OSEM, OSEM-FOREFBP, and OSEM-3DRP). We used |d¯| as a measure of radiomic feature reproducibility level, where any feature scored |d¯| ±SD ≤ |25|% ± 35% was considered reproducible. We used Bland-Altman analysis to evaluate the mean, standard deviation (SD), and upper/lower reproducibility limits (U/LRL) for radiomic features in response to variation in each testing parameter. Furthermore, we proposed U/LRL as a method to classify the level of reproducibility: High- ±1% ≤ U/LRL ≤ ±30%; Intermediate- ±30% < U/LRL ≤ ±45%; Low- ±45 < U/LRL ≤ ±50%. We considered any feature below the low level as nonreproducible (NR). Finally, we calculated the interclass correlation coefficient (ICC) to evaluate the reliability of radiomic feature measurements for each parameter. The segmented volumes of 65 patients (81.3%) scored Dice coefficient >0.75 for all three volumes. The result outcomes revealed a tendency of higher radiomic feature reproducibility among segmentation pair MTV 1 -GBSV than MTV 2 -GBSV, gray-level pairs of 64-32 and 64-128 than 64-256, and reconstruction algorithm pairs of OSEM-FOREIR and OSEM-FOREFBP than OSEM-3DRP. Although the choice of cervical tumor segmentation method, gray-level value, and reconstruction algorithm may affect radiomic features, some features were characterized by high reproducibility through all testing parameters. The number of radiomic features that showed insensitivity to variations in segmentation methods, gray-level discretization, and reconstruction algorithms was 10 (13%), 4 (5%), and 1 (1%), respectively. These results suggest that a careful analysis of the effects of these parameters is essential prior to any radiomics clinical application. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

CfAO Fall Science Retreat 2017

Science.gov Websites

: Mark Chun, Astronomy Dept., U. Hawaii) Point Spread Function Reconstruction for AO Science (Organizer : Jessica Lu, Astronomy Dept., UC Berkeley) High Contrast Exoplanet Instrumentation Program for TMT (Organizer: Michael Fitzgerald, Astronomy Dept., UCLA) Special Plenary Session: TMT international training

An Improved DINEOF Algorithm for Filling Missing Values in Spatio-Temporal Sea Surface Temperature Data.

PubMed

Ping, Bo; Su, Fenzhen; Meng, Yunshan

2016-01-01

In this study, an improved Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm for determination of missing values in a spatio-temporal dataset is presented. Compared with the ordinary DINEOF algorithm, the iterative reconstruction procedure until convergence based on every fixed EOF to determine the optimal EOF mode is not necessary and the convergence criterion is only reached once in the improved DINEOF algorithm. Moreover, in the ordinary DINEOF algorithm, after optimal EOF mode determination, the initial matrix with missing data will be iteratively reconstructed based on the optimal EOF mode until the reconstruction is convergent. However, the optimal EOF mode may be not the best EOF for some reconstructed matrices generated in the intermediate steps. Hence, instead of using asingle EOF to fill in the missing data, in the improved algorithm, the optimal EOFs for reconstruction are variable (because the optimal EOFs are variable, the improved algorithm is called VE-DINEOF algorithm in this study). To validate the accuracy of the VE-DINEOF algorithm, a sea surface temperature (SST) data set is reconstructed by using the DINEOF, I-DINEOF (proposed in 2015) and VE-DINEOF algorithms. Four parameters (Pearson correlation coefficient, signal-to-noise ratio, root-mean-square error, and mean absolute difference) are used as a measure of reconstructed accuracy. Compared with the DINEOF and I-DINEOF algorithms, the VE-DINEOF algorithm can significantly enhance the accuracy of reconstruction and shorten the computational time.

Markov prior-based block-matching algorithm for superdimension reconstruction of porous media

NASA Astrophysics Data System (ADS)

Li, Yang; He, Xiaohai; Teng, Qizhi; Feng, Junxi; Wu, Xiaohong

2018-04-01

A superdimension reconstruction algorithm is used for the reconstruction of three-dimensional (3D) structures of a porous medium based on a single two-dimensional image. The algorithm borrows the concepts of "blocks," "learning," and "dictionary" from learning-based superresolution reconstruction and applies them to the 3D reconstruction of a porous medium. In the neighborhood-matching process of the conventional superdimension reconstruction algorithm, the Euclidean distance is used as a criterion, although it may not really reflect the structural correlation between adjacent blocks in an actual situation. Hence, in this study, regular items are adopted as prior knowledge in the reconstruction process, and a Markov prior-based block-matching algorithm for superdimension reconstruction is developed for more accurate reconstruction. The algorithm simultaneously takes into consideration the probabilistic relationship between the already reconstructed blocks in three different perpendicular directions (x , y , and z ) and the block to be reconstructed, and the maximum value of the probability product of the blocks to be reconstructed (as found in the dictionary for the three directions) is adopted as the basis for the final block selection. Using this approach, the problem of an imprecise spatial structure caused by a point simulation can be overcome. The problem of artifacts in the reconstructed structure is also addressed through the addition of hard data and by neighborhood matching. To verify the improved reconstruction accuracy of the proposed method, the statistical and morphological features of the results from the proposed method and traditional superdimension reconstruction method are compared with those of the target system. The proposed superdimension reconstruction algorithm is confirmed to enable a more accurate reconstruction of the target system while also eliminating artifacts.

PCA based clustering for brain tumor segmentation of T1w MRI images.

PubMed

Kaya, Irem Ersöz; Pehlivanlı, Ayça Çakmak; Sekizkardeş, Emine Gezmez; Ibrikci, Turgay

2017-03-01

Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Assessment of phylogenetic sensitivity for reconstructing HIV-1 epidemiological relationships.

PubMed

Beloukas, Apostolos; Magiorkinis, Emmanouil; Magiorkinis, Gkikas; Zavitsanou, Asimina; Karamitros, Timokratis; Hatzakis, Angelos; Paraskevis, Dimitrios

2012-06-01

Phylogenetic analysis has been extensively used as a tool for the reconstruction of epidemiological relations for research or for forensic purposes. It was our objective to assess the sensitivity of different phylogenetic methods and various phylogenetic programs to reconstruct epidemiological links among HIV-1 infected patients that is the probability to reveal a true transmission relationship. Multiple datasets (90) were prepared consisting of HIV-1 sequences in protease (PR) and partial reverse transcriptase (RT) sampled from patients with documented epidemiological relationship (target population), and from unrelated individuals (control population) belonging to the same HIV-1 subtype as the target population. Each dataset varied regarding the number, the geographic origin and the transmission risk groups of the sequences among the control population. Phylogenetic trees were inferred by neighbor-joining (NJ), maximum likelihood heuristics (hML) and Bayesian methods. All clusters of sequences belonging to the target population were correctly reconstructed by NJ and Bayesian methods receiving high bootstrap and posterior probability (PP) support, respectively. On the other hand, TreePuzzle failed to reconstruct or provide significant support for several clusters; high puzzling step support was associated with the inclusion of control sequences from the same geographic area as the target population. In contrary, all clusters were correctly reconstructed by hML as implemented in PhyML 3.0 receiving high bootstrap support. We report that under the conditions of our study, hML using PhyML, NJ and Bayesian methods were the most sensitive for the reconstruction of epidemiological links mostly from sexually infected individuals. Copyright © 2012 Elsevier B.V. All rights reserved.

Imaging reconstruction based on improved wavelet denoising combined with parallel-beam filtered back-projection algorithm

NASA Astrophysics Data System (ADS)

Ren, Zhong; Liu, Guodong; Huang, Zhen

2012-11-01

The image reconstruction is a key step in medical imaging (MI) and its algorithm's performance determinates the quality and resolution of reconstructed image. Although some algorithms have been used, filter back-projection (FBP) algorithm is still the classical and commonly-used algorithm in clinical MI. In FBP algorithm, filtering of original projection data is a key step in order to overcome artifact of the reconstructed image. Since simple using of classical filters, such as Shepp-Logan (SL), Ram-Lak (RL) filter have some drawbacks and limitations in practice, especially for the projection data polluted by non-stationary random noises. So, an improved wavelet denoising combined with parallel-beam FBP algorithm is used to enhance the quality of reconstructed image in this paper. In the experiments, the reconstructed effects were compared between the improved wavelet denoising and others (directly FBP, mean filter combined FBP and median filter combined FBP method). To determine the optimum reconstruction effect, different algorithms, and different wavelet bases combined with three filters were respectively test. Experimental results show the reconstruction effect of improved FBP algorithm is better than that of others. Comparing the results of different algorithms based on two evaluation standards i.e. mean-square error (MSE), peak-to-peak signal-noise ratio (PSNR), it was found that the reconstructed effects of the improved FBP based on db2 and Hanning filter at decomposition scale 2 was best, its MSE value was less and the PSNR value was higher than others. Therefore, this improved FBP algorithm has potential value in the medical imaging.

Cathodoluminescence | Materials Science | NREL

Science.gov Websites

image, the time to acquire the entire spectrum series is about five minutes. When the acquisition is ) processes the spectrum series to reconstruct images of the photon emission (energy resolved) or to extract : Mapping of the photon energy and full-width-half maximum of selected transitions ASCII output Quantitative

Common lines modeling for reference free Ab-initio reconstruction in cryo-EM.

PubMed

Greenberg, Ido; Shkolnisky, Yoel

2017-11-01

We consider the problem of estimating an unbiased and reference-free ab initio model for non-symmetric molecules from images generated by single-particle cryo-electron microscopy. The proposed algorithm finds the globally optimal assignment of orientations that simultaneously respects all common lines between all images. The contribution of each common line to the estimated orientations is weighted according to a statistical model for common lines' detection errors. The key property of the proposed algorithm is that it finds the global optimum for the orientations given the common lines. In particular, any local optima in the common lines energy landscape do not affect the proposed algorithm. As a result, it is applicable to thousands of images at once, very robust to noise, completely reference free, and not biased towards any initial model. A byproduct of the algorithm is a set of measures that allow to asses the reliability of the obtained ab initio model. We demonstrate the algorithm using class averages from two experimental data sets, resulting in ab initio models with resolutions of 20Å or better, even from class averages consisting of as few as three raw images per class. Copyright © 2017 Elsevier Inc. All rights reserved.

Publications - RI 98-12 | Alaska Division of Geological & Geophysical

Science.gov Websites

content DGGS RI 98-12 Publication Details Title: Geology of the Sleetmute C-7, C-8, D-7, and D-8 ., Miller, M.L., Layer, P.W., and Laird, G.M., 1999, Geology of the Sleetmute C-7, C-8, D-7, and D-8 ; Other Oversized Sheets Sheet 1 Geologic map of the Sleetmute C-7, C-8, D-7 and D-8 quadrangles, Horn

Phase Diversity and Polarization Augmented Techniques for Active Imaging

DTIC Science & Technology

2007-03-01

build up a system model for use in algorithm development. 32 IV. Conventional Imaging and Atmospheric Turbulence With an understanding of scalar...28, 59, 115 Cholesky Factorization, 14, 42 C2n, see Turbulence Coherent Image Model, 36 Complete Data, see EM Algorithm Complex Coherence...Data, see EM Algorithm Homotopic, 62 Impulse Response, 34, 44 Incoherent Image Model, 36 Incomplete Data, see EM Algorithm Lo- Turbulence Outer Scale

Global Convergence of the EM Algorithm for Unconstrained Latent Variable Models with Categorical Indicators

ERIC Educational Resources Information Center

Weissman, Alexander

2013-01-01

Convergence of the expectation-maximization (EM) algorithm to a global optimum of the marginal log likelihood function for unconstrained latent variable models with categorical indicators is presented. The sufficient conditions under which global convergence of the EM algorithm is attainable are provided in an information-theoretic context by…

Dynamic Reconstruction Algorithm of Three-Dimensional Temperature Field Measurement by Acoustic Tomography

PubMed Central

Li, Yanqiu; Liu, Shi; Inaki, Schlaberg H.

2017-01-01

Accuracy and speed of algorithms play an important role in the reconstruction of temperature field measurements by acoustic tomography. Existing algorithms are based on static models which only consider the measurement information. A dynamic model of three-dimensional temperature reconstruction by acoustic tomography is established in this paper. A dynamic algorithm is proposed considering both acoustic measurement information and the dynamic evolution information of the temperature field. An objective function is built which fuses measurement information and the space constraint of the temperature field with its dynamic evolution information. Robust estimation is used to extend the objective function. The method combines a tunneling algorithm and a local minimization technique to solve the objective function. Numerical simulations show that the image quality and noise immunity of the dynamic reconstruction algorithm are better when compared with static algorithms such as least square method, algebraic reconstruction technique and standard Tikhonov regularization algorithms. An effective method is provided for temperature field reconstruction by acoustic tomography. PMID:28895930

Publications - RI 2001-1B | Alaska Division of Geological & Geophysical

Science.gov Websites

content DGGS RI 2001-1B Publication Details Title: Geologic map of the Chulitna region, southcentral , M.L., Reifenstuhl, R.R., and Clough, J.G., 2001, Geologic map of the Chulitna region, southcentral of the Chulitna region, southcentral Alaska, scale 1:63,360 (12.0 M) Digital Geospatial Data Digital

CT cardiac imaging: evolution from 2D to 3D backprojection

NASA Astrophysics Data System (ADS)

Tang, Xiangyang; Pan, Tinsu; Sasaki, Kosuke

2004-04-01

The state-of-the-art multiple detector-row CT, which usually employs fan beam reconstruction algorithms by approximating a cone beam geometry into a fan beam geometry, has been well recognized as an important modality for cardiac imaging. At present, the multiple detector-row CT is evolving into volumetric CT, in which cone beam reconstruction algorithms are needed to combat cone beam artifacts caused by large cone angle. An ECG-gated cardiac cone beam reconstruction algorithm based upon the so-called semi-CB geometry is implemented in this study. To get the highest temporal resolution, only the projection data corresponding to 180° plus the cone angle are row-wise rebinned into the semi-CB geometry for three-dimensional reconstruction. Data extrapolation is utilized to extend the z-coverage of the ECG-gated cardiac cone beam reconstruction algorithm approaching the edge of a CT detector. A helical body phantom is used to evaluate the ECG-gated cone beam reconstruction algorithm"s z-coverage and capability of suppressing cone beam artifacts. Furthermore, two sets of cardiac data scanned by a multiple detector-row CT scanner at 16 x 1.25 (mm) and normalized pitch 0.275 and 0.3 respectively are used to evaluate the ECG-gated CB reconstruction algorithm"s imaging performance. As a reference, the images reconstructed by a fan beam reconstruction algorithm for multiple detector-row CT are also presented. The qualitative evaluation shows that, the ECG-gated cone beam reconstruction algorithm outperforms its fan beam counterpart from the perspective of cone beam artifact suppression and z-coverage while the temporal resolution is well maintained. Consequently, the scan speed can be increased to reduce the contrast agent amount and injection time, improve the patient comfort and x-ray dose efficiency. Based up on the comparison, it is believed that, with the transition of multiple detector-row CT into volumetric CT, ECG-gated cone beam reconstruction algorithms will provide better image quality for CT cardiac applications.

Expectation-maximization algorithms for learning a finite mixture of univariate survival time distributions from partially specified class values

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Youngrok

2013-05-15

Heterogeneity exists on a data set when samples from di erent classes are merged into the data set. Finite mixture models can be used to represent a survival time distribution on heterogeneous patient group by the proportions of each class and by the survival time distribution within each class as well. The heterogeneous data set cannot be explicitly decomposed to homogeneous subgroups unless all the samples are precisely labeled by their origin classes; such impossibility of decomposition is a barrier to overcome for estimating nite mixture models. The expectation-maximization (EM) algorithm has been used to obtain maximum likelihood estimates ofmore » nite mixture models by soft-decomposition of heterogeneous samples without labels for a subset or the entire set of data. In medical surveillance databases we can find partially labeled data, that is, while not completely unlabeled there is only imprecise information about class values. In this study we propose new EM algorithms that take advantages of using such partial labels, and thus incorporate more information than traditional EM algorithms. We particularly propose four variants of the EM algorithm named EM-OCML, EM-PCML, EM-HCML and EM-CPCML, each of which assumes a specific mechanism of missing class values. We conducted a simulation study on exponential survival trees with five classes and showed that the advantages of incorporating substantial amount of partially labeled data can be highly signi cant. We also showed model selection based on AIC values fairly works to select the best proposed algorithm on each specific data set. A case study on a real-world data set of gastric cancer provided by Surveillance, Epidemiology and End Results (SEER) program showed a superiority of EM-CPCML to not only the other proposed EM algorithms but also conventional supervised, unsupervised and semi-supervised learning algorithms.« less

Image reconstruction through thin scattering media by simulated annealing algorithm

NASA Astrophysics Data System (ADS)

Fang, Longjie; Zuo, Haoyi; Pang, Lin; Yang, Zuogang; Zhang, Xicheng; Zhu, Jianhua

2018-07-01

An idea for reconstructing the image of an object behind thin scattering media is proposed by phase modulation. The optimized phase mask is achieved by modulating the scattered light using simulated annealing algorithm. The correlation coefficient is exploited as a fitness function to evaluate the quality of reconstructed image. The reconstructed images optimized from simulated annealing algorithm and genetic algorithm are compared in detail. The experimental results show that our proposed method has better definition and higher speed than genetic algorithm.

Primary Fat Grafting to the Pectoralis Muscle during Latissimus Dorsi Breast Reconstruction

PubMed Central

Vidal, Luciano; Hersant, Barbara; Meningaud, Jean Paul

2016-01-01

Background: Latissimus dorsi flap is one of the best options for immediate and delayed breast reconstruction. However, this technique is limited by the tissue volume provided by the flap. To improve breast volume while reducing complications, fat grafting is now very often used in addition to latissimus dorsi flap. To the best of our knowledge, fat grafting was always performed as a second-line surgery, at least a few months after the flap procedure. We aimed to report our experience with an associated breast reconstruction technique combining musculocutaneous latissimus dorsi flap with intrapectoral lipofilling for totally autologous breast reconstruction. Methods: Between September 2014 and January 2015, 20 patients underwent this technique for unilateral autologous breast reconstruction (14 delayed and 6 immediate breast reconstructions). A mean harvested fat volume of 278 ml (range: 190–350 ml) and a mean injected fat volume of 228 ml (range: 170–280 ml) were used. Results: None of the patients experienced complications, such as flap necrosis, breast skin necrosis, hematomas, or infection. One of the patients developed a seroma, which was treated with 3 drainage punctions. Only 2 patients underwent delayed fat grafting procedure. Conclusion: Totally autologous breast reconstruction combining latissimus dorsi flap and intrapectoral fat grafting in the same procedure is a new technique allowing increased breast volume in a single surgery. PMID:27975006

Publications - PDF 93-47 | Alaska Division of Geological & Geophysical

Science.gov Websites

content DGGS PDF 93-47 Publication Details Title: Geologic map of the Sleetmute C-7, D-7, C-8, and D-8 ., Harris, E.E., Kline, J.T., and Miller, M.L., 1993, Geologic map of the Sleetmute C-7, D-7, C-8, and D-8 Sleetmute C-7, C-8, D-7 and D-8 quadrangles, Horn Mountain Area, southwest Alaska, scale 1:63,360 (34.0 M

Implementation and testing of the first prompt search for gravitational wave transients with electromagnetic counterparts

NASA Astrophysics Data System (ADS)

LIGO Scientific Collaboration; Virgo Collaboration; Abadie, J.; Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M.; Accadia, T.; Acernese, F.; Adams, C.; Adhikari, R.; Affeldt, C.; Ajith, P.; Allen, B.; Allen, G. S.; Amador Ceron, E.; Amariutei, D.; Amin, R. S.; Anderson, S. B.; Anderson, W. G.; Arai, K.; Arain, M. A.; Araya, M. C.; Aston, S. M.; Astone, P.; Atkinson, D.; Aufmuth, P.; Aulbert, C.; Aylott, B. E.; Babak, S.; Baker, P.; Ballardin, G.; Ballmer, S.; Barker, D.; Barone, F.; Barr, B.; Barriga, P.; Barsotti, L.; Barsuglia, M.; Barton, M. A.; Bartos, I.; Bassiri, R.; Bastarrika, M.; Basti, A.; Batch, J.; Bauchrowitz, J.; Bauer, Th. S.; Bebronne, M.; Behnke, B.; Beker, M. G.; Bell, A. S.; Belletoile, A.; Belopolski, I.; Benacquista, M.; Berliner, J. M.; Bertolini, A.; Betzwieser, J.; Beveridge, N.; Beyersdorf, P. T.; Bilenko, I. A.; Billingsley, G.; Birch, J.; Biswas, R.; Bitossi, M.; Bizouard, M. A.; Black, E.; Blackburn, J. K.; Blackburn, L.; Blair, D.; Bland, B.; Blom, M.; Bock, O.; Bodiya, T. P.; Bogan, C.; Bondarescu, R.; Bondu, F.; Bonelli, L.; Bonnand, R.; Bork, R.; Born, M.; Boschi, V.; Bose, S.; Bosi, L.; Bouhou, B.; Braccini, S.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Breyer, J.; Briant, T.; Bridges, D. O.; Brillet, A.; Brinkmann, M.; Brisson, V.; Britzger, M.; Brooks, A. F.; Brown, D. A.; Brummit, A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Burguet-Castell, J.; Burmeister, O.; Buskulic, D.; Buy, C.; Byer, R. L.; Cadonati, L.; Cagnoli, G.; Calloni, E.; Camp, J. B.; Campsie, P.; Cannizzo, J.; Cannon, K.; Canuel, B.; Cao, J.; Capano, C. D.; Carbognani, F.; Caride, S.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C.; Cesarin, E.; Chaibi, O.; Chalermsongsak, T.; Chalkley, E.; Charlton, P.; Chassande-Mottin, E.; Chelkowski, S.; Chen, Y.; Chincarini, A.; Chiummo, A.; Cho, H.; Christensen, N.; Chua, S. S. Y.; Chung, C. T. Y.; Chung, S.; Ciani, G.; Clara, F.; Clark, D. E.; Clark, J.; Clayton, J. H.; Cleva, F.; Coccia, E.; Cohadon, P.-F.; Colacino, C. N.; Colas, J.; Colla, A.; Colombini, M.; Conte, A.; Conte, R.; Cook, D.; Corbitt, T. R.; Cordier, M.; Cornish, N.; Corsi, A.; Costa, C. A.; Coughlin, M.; Coulon, J.-P.; Couvares, P.; Coward, D. M.; Coyne, D. C.; Creighton, J. D. E.; Creighton, T. D.; Cruise, A. M.; Cumming, A.; Cunningham, L.; Cuoco, E.; Cutler, R. M.; Dahl, K.; Danilishin, S. L.; Dannenberg, R.; D'Antonio, S.; Danzmann, K.; Dattilo, V.; Daudert, B.; Daveloza, H.; Davier, M.; Davies, G.; Daw, E. J.; Day, R.; Dayanga, T.; DeRosa, R.; Debra, D.; Debreczeni, G.; Degallaix, J.; Del Pozzo, W.; Del Prete, M.; Dent, T.; Dergachev, V.; Derosa, R.; Desalvo, R.; Dhillon, V.; Dhurandhar, S.; Di Fiore, L.; Di Lieto, A.; Di Palma, I.; De Paolo Emilio, M.; Di Virgilio, A.; Díaz, M.; Dietz, A.; Diguglielmo, J.; Donovan, F.; Dooley, K. L.; Dorsher, S.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Dumas, J.-C.; Dwyer, S.; Eberle, T.; Edgar, M.; Edwards, M.; Effler, A.; Ehrens, P.; Endröczi, G.; Engel, R.; Etzel, T.; Evans, K.; Evans, M.; Evans, T.; Factourovich, M.; Fafone, V.; Fairhurst, S.; Fan, Y.; Farr, B. F.; Farr, W.; Fazi, D.; Fehrmann, H.; Feldbaum, D.; Ferrante, I.; Fidecaro, F.; Finn, L. S.; Fiori, I.; Fisher, R. P.; Flaminio, R.; Flanigan, M.; Foley, S.; Forsi, E.; Forte, L. A.; Fotopoulos, N.; Fournier, J.-D.; Franc, J.; Frasca, S.; Frasconi, F.; Frede, M.; Frei, M.; Frei, Z.; Freise, A.; Frey, R.; Fricke, T. T.; Fridriksson, J. K.; Friedrich, D.; Fritschel, P.; Frolov, V. V.; Fulda, P. J.; Fyffe, M.; Galimberti, M.; Gammaitoni, L.; Ganija, M. R.; Garcia, J.; Garofoli, J. A.; Garufi, F.; Gáspár, M. E.; Gemme, G.; Geng, R.; Genin, E.; Gennai, A.; Gergely, L. Á.; Ghosh, S.; Giaime, J. A.; Giampanis, S.; Giardina, K. D.; Giazotto, A.; Gill, C.; Goetz, E.; Goggin, L. M.; González, G.; Gorodetsky, M. L.; Goßler, S.; Gouaty, R.; Graef, C.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Gray, N.; Greenhalgh, R. J. S.; Gretarsson, A. M.; Greverie, C.; Grosso, R.; Grote, H.; Grunewald, S.; Guidi, G. M.; Guido, C.; Gupta, R.; Gustafson, E. K.; Gustafson, R.; Ha, T.; Hage, B.; Hallam, J. M.; Hammer, D.; Hammond, G.; Hanks, J.; Hanna, C.; Hanson, J.; Harms, J.; Harry, G. M.; Harry, I. W.; Harstad, E. D.; Hartman, M. T.; Haughian, K.; Hayama, K.; Hayau, J.-F.; Hayler, T.; Heefner, J.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hendry, M. A.; Heng, I. S.; Heptonstall, A. W.; Herrera, V.; Hewitson, M.; Hild, S.; Hoak, D.; Hodge, K. A.; Holt, K.; Homan, J.; Hong, T.; Hooper, S.; Hosken, D. J.; Hough, J.; Howell, E. J.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Ingram, D. R.; Inta, R.; Isogai, T.; Ivanov, A.; Izumi, K.; Jacobson, M.; Jang, H.; Jaranowski, P.; Johnson, W. W.; Jones, D. I.; Jones, G.; Jones, R.; Ju, L.; Kalmus, P.; Kalogera, V.; Kamaretsos, I.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Katsavounidis, E.; Katzman, W.; Kaufer, H.; Kawabe, K.; Kawamura, S.; Kawazoe, F.; Kells, W.; Keppel, D. G.; Keresztes, Z.; Khalaidovski, A.; Khalili, F. Y.; Khazanov, E. A.; Kim, B.; Kim, C.; Kim, D.; Kim, H.; Kim, K.; Kim, N.; Kim, Y.-M.; King, P. J.; Kinsey, M.; Kinzel, D. L.; Kissel, J. S.; Klimenko, S.; Kokeyama, K.; Kondrashov, V.; Kopparapu, R.; Koranda, S.; Korth, W. Z.; Kowalska, I.; Kozak, D.; Kringel, V.; Krishnamurthy, S.; Krishnan, B.; Krâ´Olak, A.; Kuehn, G.; Kumar, R.; Kwee, P.; Laas-Bourez, M.; Lam, P. K.; Landry, M.; Lang, M.; Lantz, B.; Lastzka, N.; Lawrie, C.; Lazzarini, A.; Leaci, P.; Lee, C. H.; Lee, H. M.; Leindecker, N.; Leong, J. R.; Leonor, I.; Leroy, N.; Letendre, N.; Li, J.; Li, T. G. F.; Liguori, N.; Lindquist, P. E.; Lockerbie, N. A.; Lodhia, D.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Luan, J.; Lubinski, M.; Lück, H.; Lundgren, A. P.; MacDonald, E.; Machenschalk, B.; Macinnis, M.; MacLeod, D. M.; Mageswaran, M.; Mailand, K.; Majorana, E.; Maksimovic, I.; Man, N.; Mandel, I.; Mandic, V.; Mantovani, M.; Marandi, A.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A.; Maros, E.; Marque, J.; Martelli, F.; Martin, I. W.; Martin, R. M.; Marx, J. N.; Mason, K.; Masserot, A.; Matichard, F.; Matone, L.; Matzner, R. A.; Mavalvala, N.; Mazzolo, G.; McCarthy, R.; McClelland, D. E.; McDaniel, P.; McGuire, S. C.; McIntyre, G.; McIver, J.; McKechan, D. J. A.; Meadors, G. D.; Mehmet, M.; Meier, T.; Melatos, A.; Melissinos, A. C.; Mendell, G.; Menendez, D.; Mercer, R. A.; Meshkov, S.; Messenger, C.; Meyer, M. S.; Miao, H.; Michel, C.; Milano, L.; Miller, J.; Minenkov, Y.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Miyakawa, O.; Moe, B.; Moesta, P.; Mohan, M.; Mohanty, S. D.; Mohapatra, S. R. P.; Moraru, D.; Moreno, G.; Morgado, N.; Morgia, A.; Mori, T.; Mosca, S.; Mossavi, K.; Mours, B.; Mow-Lowry, C. M.; Mueller, C. L.; Mueller, G.; Mukherjee, S.; Mullavey, A.; Müller-Ebhardt, H.; Munch, J.; Murphy, D.; Murray, P. G.; Mytidis, A.; Nash, T.; Naticchioni, L.; Nawrodt, R.; Necula, V.; Nelson, J.; Newton, G.; Nishizawa, A.; Nocera, F.; Nolting, D.; Nuttall, L.; Ochsner, E.; O'Dell, J.; Oelker, E.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Oldenburg, R. G.; O'Reilly, B.; O'Shaughnessy, R.; Osthelder, C.; Ott, C. D.; Ottaway, D. J.; Ottens, R. S.; Overmier, H.; Owen, B. J.; Page, A.; Pagliaroli, G.; Palladino, L.; Palomba, C.; Pan, Y.; Pankow, C.; Paoletti, F.; Papa, M. A.; Parisi, M.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patel, P.; Pedraza, M.; Peiris, P.; Pekowsky, L.; Penn, S.; Peralta, C.; Perreca, A.; Persichetti, G.; Phelps, M.; Pickenpack, M.; Piergiovanni, F.; Pietka, M.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Pletsch, H. J.; Plissi, M. V.; Poggiani, R.; Pöld, J.; Postiglione, F.; Prato, M.; Predoi, V.; Price, L. R.; Prijatelj, M.; Principe, M.; Privitera, S.; Prix, R.; Prodi, G. A.; Prokhorov, L.; Puncken, O.; Punturo, M.; Puppo, P.; Quetschke, V.; Raab, F. J.; Rabeling, D. S.; Rácz, I.; Radkins, H.; Raffai, P.; Rakhmanov, M.; Ramet, C. R.; Rankins, B.; Rapagnani, P.; Rapoport, S.; Raymond, V.; Re, V.; Redwine, K.; Reed, C. M.; Reed, T.; Regimbau, T.; Reid, S.; Reitze, D. H.; Ricci, F.; Riesen, R.; Riles, K.; Robertson, N. A.; Robinet, F.; Robinson, C.; Robinson, E. L.; Rocchi, A.; Roddy, S.; Rodriguez, C.; Rodruck, M.; Rolland, L.; Rollins, J.; Romano, J. D.; Romano, R.; Romie, J. H.; Rosińska, D.; Röver, C.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Ryll, H.; Sainathan, P.; Sakosky, M.; Salemi, F.; Samblowski, A.; Sammut, L.; Sancho de La Jordana, L.; Sandberg, V.; Sankar, S.; Sannibale, V.; Santamaría, L.; Santiago-Prieto, I.; Santostasi, G.; Sassolas, B.; Sathyaprakash, B. S.; Sato, S.; Saulson, P. R.; Savage, R. L.; Schilling, R.; Schlamminger, S.; Schnabel, R.; Schofield, R. M. S.; Schulz, B.; Schutz, B. F.; Schwinberg, P.; Scott, J.; Scott, S. M.; Searle, A. C.; Seifert, F.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sergeev, A.; Shaddock, D. A.; Shaltev, M.; Shapiro, B.; Shawhan, P.; Shoemaker, D. H.; Sibley, A.; Siemens, X.; Sigg, D.; Singer, A.; Singer, L.; Sintes, A. M.; Skelton, G.; Slagmolen, B. J. J.; Slutsky, J.; Smith, J. R.; Smith, M. R.; Smith, N. D.; Smith, R. J. E.; Somiya, K.; Sorazu, B.; Soto, J.; Speirits, F. C.; Sperandio, L.; Stefszky, M.; Stein, A. J.; Steinert, E.; Steinlechner, J.; Steinlechner, S.; Steplewski, S.; Stochino, A.; Stone, R.; Strain, K. A.; Strigin, S.; Stroeer, A. S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sung, M.; Susmithan, S.; Sutton, P. J.; Swinkels, B.; Tacca, M.; Taffarello, L.; Talukder, D.; Tanner, D. B.; Tarabrin, S. P.; Taylor, J. R.; Taylor, R.; Thomas, P.; Thorne, K. A.; Thorne, K. S.; Thrane, E.; Thüring, A.; Titsler, C.; Tokmakov, K. V.; Toncelli, A.; Tonelli, M.; Torre, O.; Torres, C.; Torrie, C. I.; Tournefier, E.; Travasso, F.; Traylor, G.; Trias, M.; Tseng, K.; Ugolini, D.; Urbanek, K.; Vahlbruch, H.; Vajente, G.; Vallisneri, M.; van den Brand, J. F. J.; van den Broeck, C.; van der Putten, S.; van Veggel, A. A.; Vass, S.; Vasuth, M.; Vaulin, R.; Vavoulidis, M.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Veltkamp, C.; Verkindt, D.; Vetrano, F.; Viceré, A.; Villar, A. E.; Vinet, J.-Y.; Vitale, S.; Vitale, S.; Vocca, H.; Vorvick, C.; Vyatchanin, S. P.; Wade, A.; Waldman, S. J.; Wallace, L.; Wan, Y.; Wang, X.; Wang, Z.; Wanner, A.; Ward, R. L.; Was, M.; Wei, P.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Wen, L.; Wen, S.; Wessels, P.; West, M.; Westphal, T.; Wette, K.; Whelan, J. T.; Whitcomb, S. E.; White, D.; Whiting, B. F.; Wilkinson, C.; Willems, P. A.; Williams, H. R.; Williams, L.; Willke, B.; Winkelmann, L.; Winkler, W.; Wipf, C. C.; Wiseman, A. G.; Wittel, H.; Woan, G.; Wooley, R.; Worden, J.; Yablon, J.; Yakushin, I.; Yamamoto, H.; Yamamoto, K.; Yang, H.; Yeaton-Massey, D.; Yoshida, S.; Yu, P.; Yvert, M.; Zadroźny, A.; Zanolin, M.; Zendri, J.-P.; Zhang, F.; Zhang, L.; Zhang, W.; Zhang, Z.; Zhao, C.; Zotov, N.; Zucker, M. E.; Zweizig, J.; Akerlof, C.; Boer, M.; Fender, R.; Gehrels, N.; Klotz, A.; Ofek, E. O.; Smith, M.; Sokolowski, M.; Stappers, B. W.; Steele, I.; Swinbank, J.; Wijeres, R. A. M. J.

2012-04-01

Aims: A transient astrophysical event observed in both gravitational wave (GW) and electromagnetic (EM) channels would yield rich scientific rewards. A first program initiating EM follow-ups to possible transient GW events has been developed and exercised by the LIGO and Virgo community in association with several partners. In this paper, we describe and evaluate the methods used to promptly identify and localize GW event candidates and to request images of targeted sky locations. Methods: During two observing periods (Dec. 17, 2009 to Jan. 8, 2010 and Sep. 2 to Oct. 20, 2010), a low-latency analysis pipeline was used to identify GW event candidates and to reconstruct maps of possible sky locations. A catalog of nearby galaxies and Milky Way globular clusters was used to select the most promising sky positions to be imaged, and this directional information was delivered to EM observatories with time lags of about thirty minutes. A Monte Carlo simulation has been used to evaluate the low-latency GW pipeline's ability to reconstruct source positions correctly. Results: For signals near the detection threshold, our low-latency algorithms often localized simulated GW burst signals to tens of square degrees, while neutron star/neutron star inspirals and neutron star/black hole inspirals were localized to a few hundred square degrees. Localization precision improves for moderately stronger signals. The correct sky location of signals well above threshold and originating from nearby galaxies may be observed with ~50% or better probability with a few pointings of wide-field telescopes.

Implementation and Testing of the First Prompt for Electromagnetic Counterparts to Gravitational Wave Transients

NASA Technical Reports Server (NTRS)

Abadie, J.; Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M.; Accadia, T.; Acernese, F.; Adams, C.; Adhikari, R.; Affeldt, C.;

Implementation and Testing of the First Prompt Search for Gravitational Wave Transients with Electromagnetic Counterparts

NASA Technical Reports Server (NTRS)

Abadie, J.; Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M.; Accadia, T.; Acernese, F.; Adams, C.; Adhikari, R.; Affeldt, C.;

Covariance Structure Model Fit Testing under Missing Data: An Application of the Supplemented EM Algorithm

ERIC Educational Resources Information Center

Cai, Li; Lee, Taehun

2009-01-01

We apply the Supplemented EM algorithm (Meng & Rubin, 1991) to address a chronic problem with the "two-stage" fitting of covariance structure models in the presence of ignorable missing data: the lack of an asymptotically chi-square distributed goodness-of-fit statistic. We show that the Supplemented EM algorithm provides a…

Medical image reconstruction algorithm based on the geometric information between sensor detector and ROI

NASA Astrophysics Data System (ADS)

Ham, Woonchul; Song, Chulgyu; Lee, Kangsan; Roh, Seungkuk

2016-05-01

In this paper, we propose a new image reconstruction algorithm considering the geometric information of acoustic sources and senor detector and review the two-step reconstruction algorithm which was previously proposed based on the geometrical information of ROI(region of interest) considering the finite size of acoustic sensor element. In a new image reconstruction algorithm, not only mathematical analysis is very simple but also its software implementation is very easy because we don't need to use the FFT. We verify the effectiveness of the proposed reconstruction algorithm by showing the simulation results by using Matlab k-wave toolkit.

The algorithm of central axis in surface reconstruction

NASA Astrophysics Data System (ADS)

Zhao, Bao Ping; Zhang, Zheng Mei; Cai Li, Ji; Sun, Da Ming; Cao, Hui Ying; Xing, Bao Liang

2017-09-01

Reverse engineering is an important technique means of product imitation and new product development. Its core technology -- surface reconstruction is the current research for scholars. In the various algorithms of surface reconstruction, using axis reconstruction is a kind of important method. For the various reconstruction, using medial axis algorithm was summarized, pointed out the problems existed in various methods, as well as the place needs to be improved. Also discussed the later surface reconstruction and development of axial direction.

A reconstruction algorithm for helical CT imaging on PI-planes.

PubMed

Liang, Hongzhu; Zhang, Cishen; Yan, Ming

2006-01-01

In this paper, a Feldkamp type approximate reconstruction algorithm is presented for helical cone-beam Computed Tomography. To effectively suppress artifacts due to large cone angle scanning, it is proposed to reconstruct the object point-wisely on unique customized tilted PI-planes which are close to the data collecting helices of the corresponding points. Such a reconstruction scheme can considerably suppress the artifacts in the cone-angle scanning. Computer simulations show that the proposed algorithm can provide improved imaging performance compared with the existing approximate cone-beam reconstruction algorithms.

Photoacoustic image reconstruction via deep learning

NASA Astrophysics Data System (ADS)

Antholzer, Stephan; Haltmeier, Markus; Nuster, Robert; Schwab, Johannes

2018-02-01

Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms which allow to include prior knowledge such as smoothness, total variation (TV) or sparsity constraints. These algorithms tend to be time consuming as the forward and adjoint problems have to be solved repeatedly. Further, iterative algorithms have additional drawbacks. For example, the reconstruction quality strongly depends on a-priori model assumptions about the objects to be recovered, which are often not strictly satisfied in practical applications. To overcome these issues, in this paper, we develop direct and efficient reconstruction algorithms based on deep learning. As opposed to iterative algorithms, we apply a convolutional neural network, whose parameters are trained before the reconstruction process based on a set of training data. For actual image reconstruction, a single evaluation of the trained network yields the desired result. Our presented numerical results (using two different network architectures) demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative reconstruction methods.

Effective microorganism - X attenuates circulating superoxide dismutase following an acute bout of intermittent running in hot, humid conditions.

PubMed

Taylor, Lee; Lee, Ben J; Gibson, Oliver R; Midgley, Adrian W; Watt, Peter; Mauger, Alexis; Castle, Paul

2016-01-01

This study determined the effectiveness of antioxidant supplementation on high-intensity exercise-heat stress. Six males completed a high-intensity running protocol twice in temperate conditions (TEMP; 20.4°C), and twice in hot conditions (HOT; 34.7°C). Trials were completed following7 days supplementation with 70 ml·day(-1) effective microorganism-X (EM-X; TEMPEMX or HOTEMX) or placebo (TEMPPLA or HOTPLA). Plasma extracellular Hsp72 (eHsp72) and superoxide dismutase (SOD) were measured by ELISA. eHsp72 and SOD increased pre-post exercise (p < 0.001), with greater eHsp72 (p < 0.001) increases observed in HOT (+1.5 ng·ml(-1)) compared to TEMP (+0.8 ng·ml(-1)). EM-X did not influence eHsp72 (p > 0.05). Greater (p < 0.001) SOD increases were observed in HOT (+0.22 U·ml(-1)) versus TEMP (+0.10 U·ml(-1)) with SOD reduced in HOTEMX versus HOTPLA (p = 0.001). Physiological and perceptual responses were all greater (p < 0.001) in HOT versus TEMP conditions, with no difference followed EM-X (p > 0.05). EM-X supplementation attenuated the SOD increases following HOT, potentiating its application as an ergogenic aid to ameliorate oxidative stress.

Biotransformation of L-tyrosine to Dopamine by a Calcium Alginate Immobilized Mutant Strain of Aspergillus oryzae.

PubMed

Ali, Sikander; Nawaz, Wajeeha

2016-08-01

The present research work is concerned with the biotransformation of L-tyrosine to dopamine (DA) by calcium alginate entrapped conidiospores of a mutant strain of Aspergillus oryzae. Different strains of A. oryzae were isolated from soil. Out of 13 isolated strains, isolate-2 (I-2) was found to be a better DA producer. The wild-type I-2 was chemically improved by treating it with different concentrations of ethyl methyl sulfonate (EMS). Among seven mutant variants, EMS-6 exhibiting maximal DA activity of 43 μg/ml was selected. The strain was further exposed with L-cysteine HCl to make it resistant against diversion and environmental stress. The conidiospores of selected mutant variant A. oryzae EMS-6 strain were entrapped in calcium alginate beads. Different parameters for immobilization were investigated. The activity was further improved from 44 to 62 μg/ml under optimized conditions (1.5 % sodium alginate, 2 ml inoculum, and 2 mm bead size). The best resistant mutant variable exhibited over threefold increase in DA activity (62 μg/ml) than did wild-type I-2 (21 μg/ml) in the reaction mixture. From the results presented in the study, it was observed that high titers of DA activity in vitro could effectively be achieved by the EMS-induced mutagenesis of filamentous fungus culture used.

Parameter selection in limited data cone-beam CT reconstruction using edge-preserving total variation algorithms

NASA Astrophysics Data System (ADS)

Lohvithee, Manasavee; Biguri, Ander; Soleimani, Manuchehr

2017-12-01

There are a number of powerful total variation (TV) regularization methods that have great promise in limited data cone-beam CT reconstruction with an enhancement of image quality. These promising TV methods require careful selection of the image reconstruction parameters, for which there are no well-established criteria. This paper presents a comprehensive evaluation of parameter selection in a number of major TV-based reconstruction algorithms. An appropriate way of selecting the values for each individual parameter has been suggested. Finally, a new adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm is presented, which implements the edge-preserving function for CBCT reconstruction with limited data. The proposed algorithm shows significant robustness compared to three other existing algorithms: ASD-POCS, AwASD-POCS and PCSD. The proposed AwPCSD algorithm is able to preserve the edges of the reconstructed images better with fewer sensitive parameters to tune.

Analytic TOF PET reconstruction algorithm within DIRECT data partitioning framework

PubMed Central

Matej, Samuel; Daube-Witherspoon, Margaret E.; Karp, Joel S.

2016-01-01

Iterative reconstruction algorithms are routinely used for clinical practice; however, analytic algorithms are relevant candidates for quantitative research studies due to their linear behavior. While iterative algorithms also benefit from the inclusion of accurate data and noise models the widespread use of TOF scanners with less sensitivity to noise and data imperfections make analytic algorithms even more promising. In our previous work we have developed a novel iterative reconstruction approach (Direct Image Reconstruction for TOF) providing convenient TOF data partitioning framework and leading to very efficient reconstructions. In this work we have expanded DIRECT to include an analytic TOF algorithm with confidence weighting incorporating models of both TOF and spatial resolution kernels. Feasibility studies using simulated and measured data demonstrate that analytic-DIRECT with appropriate resolution and regularization filters is able to provide matched bias vs. variance performance to iterative TOF reconstruction with a matched resolution model. PMID:27032968

Analytic TOF PET reconstruction algorithm within DIRECT data partitioning framework

NASA Astrophysics Data System (ADS)

Matej, Samuel; Daube-Witherspoon, Margaret E.; Karp, Joel S.

2016-05-01

Iterative reconstruction algorithms are routinely used for clinical practice; however, analytic algorithms are relevant candidates for quantitative research studies due to their linear behavior. While iterative algorithms also benefit from the inclusion of accurate data and noise models the widespread use of time-of-flight (TOF) scanners with less sensitivity to noise and data imperfections make analytic algorithms even more promising. In our previous work we have developed a novel iterative reconstruction approach (DIRECT: direct image reconstruction for TOF) providing convenient TOF data partitioning framework and leading to very efficient reconstructions. In this work we have expanded DIRECT to include an analytic TOF algorithm with confidence weighting incorporating models of both TOF and spatial resolution kernels. Feasibility studies using simulated and measured data demonstrate that analytic-DIRECT with appropriate resolution and regularization filters is able to provide matched bias versus variance performance to iterative TOF reconstruction with a matched resolution model.

Theory and algorithms for image reconstruction on chords and within regions of interest

NASA Astrophysics Data System (ADS)

Zou, Yu; Pan, Xiaochuan; Sidky, Emilâ Y.

2005-11-01

We introduce a formula for image reconstruction on a chord of a general source trajectory. We subsequently develop three algorithms for exact image reconstruction on a chord from data acquired with the general trajectory. Interestingly, two of the developed algorithms can accommodate data containing transverse truncations. The widely used helical trajectory and other trajectories discussed in literature can be interpreted as special cases of the general trajectory, and the developed theory and algorithms are thus directly applicable to reconstructing images exactly from data acquired with these trajectories. For instance, chords on a helical trajectory are equivalent to the n-PI-line segments. In this situation, the proposed algorithms become the algorithms that we proposed previously for image reconstruction on PI-line segments. We have performed preliminary numerical studies, which include the study on image reconstruction on chords of two-circle trajectory, which is nonsmooth, and on n-PI lines of a helical trajectory, which is smooth. Quantitative results of these studies verify and demonstrate the proposed theory and algorithms.

A fast 4D cone beam CT reconstruction method based on the OSC-TV algorithm.

PubMed

Mascolo-Fortin, Julia; Matenine, Dmitri; Archambault, Louis; Després, Philippe

2018-01-01

Four-dimensional cone beam computed tomography allows for temporally resolved imaging with useful applications in radiotherapy, but raises particular challenges in terms of image quality and computation time. The purpose of this work is to develop a fast and accurate 4D algorithm by adapting a GPU-accelerated ordered subsets convex algorithm (OSC), combined with the total variation minimization regularization technique (TV). Different initialization schemes were studied to adapt the OSC-TV algorithm to 4D reconstruction: each respiratory phase was initialized either with a 3D reconstruction or a blank image. Reconstruction algorithms were tested on a dynamic numerical phantom and on a clinical dataset. 4D iterations were implemented for a cluster of 8 GPUs. All developed methods allowed for an adequate visualization of the respiratory movement and compared favorably to the McKinnon-Bates and adaptive steepest descent projection onto convex sets algorithms, while the 4D reconstructions initialized from a prior 3D reconstruction led to better overall image quality. The most suitable adaptation of OSC-TV to 4D CBCT was found to be a combination of a prior FDK reconstruction and a 4D OSC-TV reconstruction with a reconstruction time of 4.5 minutes. This relatively short reconstruction time could facilitate a clinical use.

Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm

NASA Astrophysics Data System (ADS)

Elahi, Sana; kaleem, Muhammad; Omer, Hammad

2018-01-01

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k -space. This paper introduces an improved iterative algorithm based on p -thresholding technique for CS-MRI image reconstruction. The use of p -thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p -thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p -thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.

Factor Analysis with EM Algorithm Never Gives Improper Solutions when Sample Covariance and Initial Parameter Matrices Are Proper

ERIC Educational Resources Information Center

Adachi, Kohei

2013-01-01

Rubin and Thayer ("Psychometrika," 47:69-76, 1982) proposed the EM algorithm for exploratory and confirmatory maximum likelihood factor analysis. In this paper, we prove the following fact: the EM algorithm always gives a proper solution with positive unique variances and factor correlations with absolute values that do not exceed one,…

Staff - Robert J. Gillis | Alaska Division of Geological & Geophysical

Science.gov Websites

the hydrocarbon potential of Alaska's sedimentary basins and reconstruct their geologic histories. My : Sedimentary Geology, Special Volume 182, p. 87-100. http://dx.doi.org/10.1016/j.sedgeo.2005.07.013 Selected

Center for Nondestructive Evaluation - Center for Nondestructive Evaluation

Science.gov Websites

available for the full range of inspection methods, housed in a 52,000 sq. ft. facility with over $5M in - 1990): Development of NDE methods for application to DOE energy and weapons programs, including multi for enhanced frequency bandwidth and improved flaw reconstruction, and novel methods for poling

Immediate direct-to-implant breast reconstruction using anatomical implants.

PubMed

Kim, Sung-Eun; Jung, Dong-Woo; Chung, Kyu-Jin; Lee, Jun Ho; Kim, Tae Gon; Kim, Yong-Ha; Lee, Soo Jung; Kang, Su Hwan; Choi, Jung Eun

2014-09-01

In 2012, a new anatomic breast implant of form-stable silicone gel was introduced onto the Korean market. The intended use of this implant is in the area of aesthetic breast surgery, and many reports are promising. Thus far, however, there have been no reports on the use of this implant for breast reconstruction in Korea. We used this breast implant in breast reconstruction surgery and report our early experience. From November 2012 to April 2013, the Natrelle Style 410 form-stable anatomically shaped cohesive silicone gel-filled breast implant was used in 31 breasts of 30 patients for implant breast reconstruction with an acellular dermal matrix. Patients were treated with skin-sparing mastectomies followed by immediate breast reconstruction. The mean breast resection volume was 240 mL (range, 83-540 mL). The mean size of the breast implants was 217 mL (range, 125-395 mL). Breast shape outcomes were considered acceptable. Infection and skin thinning occurred in one patient each, and hematoma and seroma did not occur. Three cases of wound dehiscence occurred, one requiring surgical intervention, while the others healed with conservative treatment in one month. Rippling did not occur. So far, complications such as capsular contracture and malrotation of breast implant have not yet arisen. By using anatomic breast implants in breast reconstruction, we achieved satisfactory results with aesthetics better than those obtained with round breast implants. Therefore, we concluded that the anatomical implant is suitable for breast reconstruction.

High resolution x-ray CMT: Reconstruction methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, J.K.

This paper qualitatively discusses the primary characteristics of methods for reconstructing tomographic images from a set of projections. These reconstruction methods can be categorized as either {open_quotes}analytic{close_quotes} or {open_quotes}iterative{close_quotes} techniques. Analytic algorithms are derived from the formal inversion of equations describing the imaging process, while iterative algorithms incorporate a model of the imaging process and provide a mechanism to iteratively improve image estimates. Analytic reconstruction algorithms are typically computationally more efficient than iterative methods; however, analytic algorithms are available for a relatively limited set of imaging geometries and situations. Thus, the framework of iterative reconstruction methods is better suited formore » high accuracy, tomographic reconstruction codes.« less

SU-F-BRCD-09: Total Variation (TV) Based Fast Convergent Iterative CBCT Reconstruction with GPU Acceleration.

PubMed

Xu, Q; Yang, D; Tan, J; Anastasio, M

2012-06-01

To improve image quality and reduce imaging dose in CBCT for radiation therapy applications and to realize near real-time image reconstruction based on use of a fast convergence iterative algorithm and acceleration by multi-GPUs. An iterative image reconstruction that sought to minimize a weighted least squares cost function that employed total variation (TV) regularization was employed to mitigate projection data incompleteness and noise. To achieve rapid 3D image reconstruction (< 1 min), a highly optimized multiple-GPU implementation of the algorithm was developed. The convergence rate and reconstruction accuracy were evaluated using a modified 3D Shepp-Logan digital phantom and a Catphan-600 physical phantom. The reconstructed images were compared with the clinical FDK reconstruction results. Digital phantom studies showed that only 15 iterations and 60 iterations are needed to achieve algorithm convergence for 360-view and 60-view cases, respectively. The RMSE was reduced to 10-4 and 10-2, respectively, by using 15 iterations for each case. Our algorithm required 5.4s to complete one iteration for the 60-view case using one Tesla C2075 GPU. The few-view study indicated that our iterative algorithm has great potential to reduce the imaging dose and preserve good image quality. For the physical Catphan studies, the images obtained from the iterative algorithm possessed better spatial resolution and higher SNRs than those obtained from by use of a clinical FDK reconstruction algorithm. We have developed a fast convergence iterative algorithm for CBCT image reconstruction. The developed algorithm yielded images with better spatial resolution and higher SNR than those produced by a commercial FDK tool. In addition, from the few-view study, the iterative algorithm has shown great potential for significantly reducing imaging dose. We expect that the developed reconstruction approach will facilitate applications including IGART and patient daily CBCT-based treatment localization. © 2012 American Association of Physicists in Medicine.

TVR-DART: A More Robust Algorithm for Discrete Tomography From Limited Projection Data With Automated Gray Value Estimation.

PubMed

Xiaodong Zhuge; Palenstijn, Willem Jan; Batenburg, Kees Joost

2016-01-01

In this paper, we present a novel iterative reconstruction algorithm for discrete tomography (DT) named total variation regularized discrete algebraic reconstruction technique (TVR-DART) with automated gray value estimation. This algorithm is more robust and automated than the original DART algorithm, and is aimed at imaging of objects consisting of only a few different material compositions, each corresponding to a different gray value in the reconstruction. By exploiting two types of prior knowledge of the scanned object simultaneously, TVR-DART solves the discrete reconstruction problem within an optimization framework inspired by compressive sensing to steer the current reconstruction toward a solution with the specified number of discrete gray values. The gray values and the thresholds are estimated as the reconstruction improves through iterations. Extensive experiments from simulated data, experimental μCT, and electron tomography data sets show that TVR-DART is capable of providing more accurate reconstruction than existing algorithms under noisy conditions from a small number of projection images and/or from a small angular range. Furthermore, the new algorithm requires less effort on parameter tuning compared with the original DART algorithm. With TVR-DART, we aim to provide the tomography society with an easy-to-use and robust algorithm for DT.

Intermediate view reconstruction using adaptive disparity search algorithm for real-time 3D processing

NASA Astrophysics Data System (ADS)

Bae, Kyung-hoon; Park, Changhan; Kim, Eun-soo

2008-03-01

In this paper, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (ASDA) is for realtime 3-dimensional (3D) processing proposed. The proposed algorithm can reduce processing time of disparity estimation by selecting adaptive disparity search range. Also, the proposed algorithm can increase the quality of the 3D imaging. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 4.8 dB by comparing with that of conventional algorithms, and reduces the Synthesizing time of a reconstructed image to about 7.02 sec by comparing with that of conventional algorithms.

Investigation of undersampling and reconstruction algorithm dependence on respiratory correlated 4D-MRI for online MR-guided radiation therapy

NASA Astrophysics Data System (ADS)

Mickevicius, Nikolai J.; Paulson, Eric S.

2017-04-01

The purpose of this work is to investigate the effects of undersampling and reconstruction algorithm on the total processing time and image quality of respiratory phase-resolved 4D MRI data. Specifically, the goal is to obtain quality 4D-MRI data with a combined acquisition and reconstruction time of five minutes or less, which we reasoned would be satisfactory for pre-treatment 4D-MRI in online MRI-gRT. A 3D stack-of-stars, self-navigated, 4D-MRI acquisition was used to scan three healthy volunteers at three image resolutions and two scan durations. The NUFFT, CG-SENSE, SPIRiT, and XD-GRASP reconstruction algorithms were used to reconstruct each dataset on a high performance reconstruction computer. The overall image quality, reconstruction time, artifact prevalence, and motion estimates were compared. The CG-SENSE and XD-GRASP reconstructions provided superior image quality over the other algorithms. The combination of a 3D SoS sequence and parallelized reconstruction algorithms using computing hardware more advanced than those typically seen on product MRI scanners, can result in acquisition and reconstruction of high quality respiratory correlated 4D-MRI images in less than five minutes.

Axial Cone-Beam Reconstruction by Weighted BPF/DBPF and Orthogonal Butterfly Filtering.

PubMed

Tang, Shaojie; Tang, Xiangyang

2016-09-01

The backprojection-filtration (BPF) and the derivative backprojection filtered (DBPF) algorithms, in which Hilbert filtering is the common algorithmic feature, are originally derived for exact helical reconstruction from cone-beam (CB) scan data and axial reconstruction from fan beam data, respectively. These two algorithms can be heuristically extended for image reconstruction from axial CB scan data, but induce severe artifacts in images located away from the central plane, determined by the circular source trajectory. We propose an algorithmic solution herein to eliminate the artifacts. The solution is an integration of three-dimensional (3-D) weighted axial CB-BPF/DBPF algorithm with orthogonal butterfly filtering, namely axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering. Using the computer simulated Forbild head and thoracic phantoms that are rigorous in inspecting the reconstruction accuracy, and an anthropomorphic thoracic phantom with projection data acquired by a CT scanner, we evaluate the performance of the proposed algorithm. Preliminary results show that the orthogonal butterfly filtering can eliminate the severe streak artifacts existing in the images reconstructed by the 3-D weighted axial CB-BPF/DBPF algorithm located at off-central planes. Integrated with orthogonal butterfly filtering, the 3-D weighted CB-BPF/DBPF algorithm can perform at least as well as the 3-D weighted CB-FBP algorithm in image reconstruction from axial CB scan data. The proposed 3-D weighted axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering can be an algorithmic solution for CT imaging in extensive clinical and preclinical applications.

Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers

PubMed Central

2010-01-01

Background The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved. Results This article proposes an expectation-maximization (EM) algorithm called emBayesB which allows only a proportion of SNP to be in LD with QTL and incorporates prior information about the distribution of SNP effects. The posterior probability of being in LD with at least one QTL is calculated for each SNP along with estimates of the hyperparameters for the mixture prior. A simulated example of genomic selection from an international workshop is used to demonstrate the features of the EM algorithm. The accuracy of prediction is comparable to a full Bayesian analysis but the EM algorithm is considerably faster. The EM algorithm was accurate in locating QTL which explained more than 1% of the total genetic variation. A computational algorithm for very large SNP panels is described. Conclusions emBayesB is a fast and accurate EM algorithm for implementing genomic selection and predicting complex traits by mapping QTL in genome-wide dense SNP marker data. Its accuracy is similar to Bayesian methods but it takes only a fraction of the time. PMID:20969788

Simultaneous reconstruction and segmentation for dynamic SPECT imaging

NASA Astrophysics Data System (ADS)

Burger, Martin; Rossmanith, Carolin; Zhang, Xiaoqun

2016-10-01

This work deals with the reconstruction of dynamic images that incorporate characteristic dynamics in certain subregions, as arising for the kinetics of many tracers in emission tomography (SPECT, PET). We make use of a basis function approach for the unknown tracer concentration by assuming that the region of interest can be divided into subregions with spatially constant concentration curves. Applying a regularised variational framework reminiscent of the Chan-Vese model for image segmentation we simultaneously reconstruct both the labelling functions of the subregions as well as the subconcentrations within each region. Our particular focus is on applications in SPECT with the Poisson noise model, resulting in a Kullback-Leibler data fidelity in the variational approach. We present a detailed analysis of the proposed variational model and prove existence of minimisers as well as error estimates. The latter apply to a more general class of problems and generalise existing results in literature since we deal with a nonlinear forward operator and a nonquadratic data fidelity. A computational algorithm based on alternating minimisation and splitting techniques is developed for the solution of the problem and tested on appropriately designed synthetic data sets. For those we compare the results to those of standard EM reconstructions and investigate the effects of Poisson noise in the data.

Compensation of missing wedge effects with sequential statistical reconstruction in electron tomography.

PubMed

Paavolainen, Lassi; Acar, Erman; Tuna, Uygar; Peltonen, Sari; Moriya, Toshio; Soonsawad, Pan; Marjomäki, Varpu; Cheng, R Holland; Ruotsalainen, Ulla

2014-01-01

Electron tomography (ET) of biological samples is used to study the organization and the structure of the whole cell and subcellular complexes in great detail. However, projections cannot be acquired over full tilt angle range with biological samples in electron microscopy. ET image reconstruction can be considered an ill-posed problem because of this missing information. This results in artifacts, seen as the loss of three-dimensional (3D) resolution in the reconstructed images. The goal of this study was to achieve isotropic resolution with a statistical reconstruction method, sequential maximum a posteriori expectation maximization (sMAP-EM), using no prior morphological knowledge about the specimen. The missing wedge effects on sMAP-EM were examined with a synthetic cell phantom to assess the effects of noise. An experimental dataset of a multivesicular body was evaluated with a number of gold particles. An ellipsoid fitting based method was developed to realize the quantitative measures elongation and contrast in an automated, objective, and reliable way. The method statistically evaluates the sub-volumes containing gold particles randomly located in various parts of the whole volume, thus giving information about the robustness of the volume reconstruction. The quantitative results were also compared with reconstructions made with widely-used weighted backprojection and simultaneous iterative reconstruction technique methods. The results showed that the proposed sMAP-EM method significantly suppresses the effects of the missing information producing isotropic resolution. Furthermore, this method improves the contrast ratio, enhancing the applicability of further automatic and semi-automatic analysis. These improvements in ET reconstruction by sMAP-EM enable analysis of subcellular structures with higher three-dimensional resolution and contrast than conventional methods.

Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets

PubMed Central

Jeong, Won-Ki; Beyer, Johanna; Hadwiger, Markus; Vazquez, Amelio; Pfister, Hanspeter; Whitaker, Ross T.

2011-01-01

Recent advances in scanning technology provide high resolution EM (Electron Microscopy) datasets that allow neuroscientists to reconstruct complex neural connections in a nervous system. However, due to the enormous size and complexity of the resulting data, segmentation and visualization of neural processes in EM data is usually a difficult and very time-consuming task. In this paper, we present NeuroTrace, a novel EM volume segmentation and visualization system that consists of two parts: a semi-automatic multiphase level set segmentation with 3D tracking for reconstruction of neural processes, and a specialized volume rendering approach for visualization of EM volumes. It employs view-dependent on-demand filtering and evaluation of a local histogram edge metric, as well as on-the-fly interpolation and ray-casting of implicit surfaces for segmented neural structures. Both methods are implemented on the GPU for interactive performance. NeuroTrace is designed to be scalable to large datasets and data-parallel hardware architectures. A comparison of NeuroTrace with a commonly used manual EM segmentation tool shows that our interactive workflow is faster and easier to use for the reconstruction of complex neural processes. PMID:19834227

Detection of Neuron Membranes in Electron Microscopy Images Using Multi-scale Context and Radon-Like Features

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seyedhosseini, Mojtaba; Kumar, Ritwik; Jurrus, Elizabeth R.

2011-10-01

Automated neural circuit reconstruction through electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that exploits multi-scale contextual information together with Radon-like features (RLF) to learn a series of discriminative models. The main idea is to build a framework which is capable of extracting information about cell membranes from a large contextual area of an EM image in a computationally efficient way. Toward this goal, we extract RLF that can be computed efficiently from the input image and generate a scale-space representation of the context images that are obtained at the output ofmore » each discriminative model in the series. Compared to a single-scale model, the use of a multi-scale representation of the context image gives the subsequent classifiers access to a larger contextual area in an effective way. Our strategy is general and independent of the classifier and has the potential to be used in any context based framework. We demonstrate that our method outperforms the state-of-the-art algorithms in detection of neuron membranes in EM images.« less

Robert Spencer | NREL

Science.gov Websites

& Simulation Research Interests Remote Sensing Natural Resource Modeling Machine Learning Education Analysis Center. Areas of Expertise Geospatial Analysis Data Visualization Algorithm Development Modeling

SU-G-201-09: Evaluation of a Novel Machine-Learning Algorithm for Permanent Prostate Brachytherapy Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nicolae, A; Department of Physics, Ryerson University, Toronto, ON; Lu, L

Purpose: A novel, automated, algorithm for permanent prostate brachytherapy (PPB) treatment planning has been developed. The novel approach uses machine-learning (ML), a form of artificial intelligence, to substantially decrease planning time while simultaneously retaining the clinical intuition of plans created by radiation oncologists. This study seeks to compare the ML algorithm against expert-planned PPB plans to evaluate the equivalency of dosimetric and clinical plan quality. Methods: Plan features were computed from historical high-quality PPB treatments (N = 100) and stored in a relational database (RDB). The ML algorithm matched new PPB features to a highly similar case in the RDB;more » this initial plan configuration was then further optimized using a stochastic search algorithm. PPB pre-plans (N = 30) generated using the ML algorithm were compared to plan variants created by an expert dosimetrist (RT), and radiation oncologist (MD). Planning time and pre-plan dosimetry were evaluated using a one-way Student’s t-test and ANOVA, respectively (significance level = 0.05). Clinical implant quality was evaluated by expert PPB radiation oncologists as part of a qualitative study. Results: Average planning time was 0.44 ± 0.42 min compared to 17.88 ± 8.76 min for the ML algorithm and RT, respectively, a significant advantage [t(9), p = 0.01]. A post-hoc ANOVA [F(2,87) = 6.59, p = 0.002] using Tukey-Kramer criteria showed a significantly lower mean prostate V150% for the ML plans (52.9%) compared to the RT (57.3%), and MD (56.2%) plans. Preliminary qualitative study results indicate comparable clinical implant quality between RT and ML plans with a trend towards preference for ML plans. Conclusion: PPB pre-treatment plans highly comparable to those of an expert radiation oncologist can be created using a novel ML planning model. The use of an ML-based planning approach is expected to translate into improved PPB accessibility and plan uniformity.« less

Algorithms to eliminate the influence of non-uniform intensity distributions on wavefront reconstruction by quadri-wave lateral shearing interferometers

NASA Astrophysics Data System (ADS)

Chen, Xiao-jun; Dong, Li-zhi; Wang, Shuai; Yang, Ping; Xu, Bing

2017-11-01

In quadri-wave lateral shearing interferometry (QWLSI), when the intensity distribution of the incident light wave is non-uniform, part of the information of the intensity distribution will couple with the wavefront derivatives to cause wavefront reconstruction errors. In this paper, we propose two algorithms to reduce the influence of a non-uniform intensity distribution on wavefront reconstruction. Our simulation results demonstrate that the reconstructed amplitude distribution (RAD) algorithm can effectively reduce the influence of the intensity distribution on the wavefront reconstruction and that the collected amplitude distribution (CAD) algorithm can almost eliminate it.

Challenges in the Verification of Reinforcement Learning Algorithms

NASA Technical Reports Server (NTRS)

Van Wesel, Perry; Goodloe, Alwyn E.

2017-01-01

Machine learning (ML) is increasingly being applied to a wide array of domains from search engines to autonomous vehicles. These algorithms, however, are notoriously complex and hard to verify. This work looks at the assumptions underlying machine learning algorithms as well as some of the challenges in trying to verify ML algorithms. Furthermore, we focus on the specific challenges of verifying reinforcement learning algorithms. These are highlighted using a specific example. Ultimately, we do not offer a solution to the complex problem of ML verification, but point out possible approaches for verification and interesting research opportunities.

Latissimus dorsi flap for total autologous immediate breast reconstruction without implants.

PubMed

Santanelli di Pompeo, Fabio; Laporta, Rosaria; Sorotos, Michail; Pagnoni, Marco; Falesiedi, Federica; Longo, Benedetto

2014-12-01

The latissimus dorsi flap provides suitable recipient tissue for fat transfer, with a good blood supply and a reasonable volume of host tissue to inject into. The authors present their experience with use of the pedicled latissimus dorsi flap for fat grafting in total autologous immediate breast reconstruction without implants. From 2010 to 2013, 23 patients underwent breast reconstruction with primary fat augmented latissimus dorsi flaps (21 unilateral procedures and two bilateral procedures). Mean patient age was 52.3 years (range, 39 to 68 years); mean body mass index was 24.77 kg/m2 (range, 21.5 to 28.7 kg/m2). Fat was harvested using the Coleman technique with 10-ml syringes and injected into the adipose layer and muscle fascia of the latissimus dorsi flap skin paddle with 1-ml syringes. The mean size of the harvested skin paddle was 19.7 × 11.04 cm (range, 18 × 10 cm to 21 × 12 cm). Mean operative time was 2.62 hours (range, 2.10 to 3.20 hours) and 4.12 hours (range, 4.10 to 4.15 hours) for unilateral and bilateral reconstructions, respectively. Mean harvested fat volume was 126 ml (range, 90 to 180 ml), and mean injected fat volume was 101 ml (range, 60 to 150 ml). All flaps healed uneventfully, no seroma occurred at the flap donor-site, and no fat grafting-related complications were observed. To the best of the authors' knowledge, this is the first report in which fat transfer was used to achieve immediate latissimus dorsi flap volume augmentation as an alternative for total autologous reconstruction, avoiding implant-related complications.

High-speed parallel implementation of a modified PBR algorithm on DSP-based EH topology

NASA Astrophysics Data System (ADS)

Rajan, K.; Patnaik, L. M.; Ramakrishna, J.

1997-08-01

Algebraic Reconstruction Technique (ART) is an age-old method used for solving the problem of three-dimensional (3-D) reconstruction from projections in electron microscopy and radiology. In medical applications, direct 3-D reconstruction is at the forefront of investigation. The simultaneous iterative reconstruction technique (SIRT) is an ART-type algorithm with the potential of generating in a few iterations tomographic images of a quality comparable to that of convolution backprojection (CBP) methods. Pixel-based reconstruction (PBR) is similar to SIRT reconstruction, and it has been shown that PBR algorithms give better quality pictures compared to those produced by SIRT algorithms. In this work, we propose a few modifications to the PBR algorithms. The modified algorithms are shown to give better quality pictures compared to PBR algorithms. The PBR algorithm and the modified PBR algorithms are highly compute intensive, Not many attempts have been made to reconstruct objects in the true 3-D sense because of the high computational overhead. In this study, we have developed parallel two-dimensional (2-D) and 3-D reconstruction algorithms based on modified PBR. We attempt to solve the two problems encountered by the PBR and modified PBR algorithms, i.e., the long computational time and the large memory requirements, by parallelizing the algorithm on a multiprocessor system. We investigate the possible task and data partitioning schemes by exploiting the potential parallelism in the PBR algorithm subject to minimizing the memory requirement. We have implemented an extended hypercube (EH) architecture for the high-speed execution of the 3-D reconstruction algorithm using the commercially available fast floating point digital signal processor (DSP) chips as the processing elements (PEs) and dual-port random access memories (DPR) as channels between the PEs. We discuss and compare the performances of the PBR algorithm on an IBM 6000 RISC workstation, on a Silicon Graphics Indigo 2 workstation, and on an EH system. The results show that an EH(3,1) using DSP chips as PEs executes the modified PBR algorithm about 100 times faster than an LBM 6000 RISC workstation. We have executed the algorithms on a 4-node IBM SP2 parallel computer. The results show that execution time of the algorithm on an EH(3,1) is better than that of a 4-node IBM SP2 system. The speed-up of an EH(3,1) system with eight PEs and one network controller is approximately 7.85.

Ancestral sequence reconstruction in primate mitochondrial DNA: compositional bias and effect on functional inference.

PubMed

Krishnan, Neeraja M; Seligmann, Hervé; Stewart, Caro-Beth; De Koning, A P Jason; Pollock, David D

2004-10-01

Reconstruction of ancestral DNA and amino acid sequences is an important means of inferring information about past evolutionary events. Such reconstructions suggest changes in molecular function and evolutionary processes over the course of evolution and are used to infer adaptation and convergence. Maximum likelihood (ML) is generally thought to provide relatively accurate reconstructed sequences compared to parsimony, but both methods lead to the inference of multiple directional changes in nucleotide frequencies in primate mitochondrial DNA (mtDNA). To better understand this surprising result, as well as to better understand how parsimony and ML differ, we constructed a series of computationally simple "conditional pathway" methods that differed in the number of substitutions allowed per site along each branch, and we also evaluated the entire Bayesian posterior frequency distribution of reconstructed ancestral states. We analyzed primate mitochondrial cytochrome b (Cyt-b) and cytochrome oxidase subunit I (COI) genes and found that ML reconstructs ancestral frequencies that are often more different from tip sequences than are parsimony reconstructions. In contrast, frequency reconstructions based on the posterior ensemble more closely resemble extant nucleotide frequencies. Simulations indicate that these differences in ancestral sequence inference are probably due to deterministic bias caused by high uncertainty in the optimization-based ancestral reconstruction methods (parsimony, ML, Bayesian maximum a posteriori). In contrast, ancestral nucleotide frequencies based on an average of the Bayesian set of credible ancestral sequences are much less biased. The methods involving simpler conditional pathway calculations have slightly reduced likelihood values compared to full likelihood calculations, but they can provide fairly unbiased nucleotide reconstructions and may be useful in more complex phylogenetic analyses than considered here due to their speed and flexibility. To determine whether biased reconstructions using optimization methods might affect inferences of functional properties, ancestral primate mitochondrial tRNA sequences were inferred and helix-forming propensities for conserved pairs were evaluated in silico. For ambiguously reconstructed nucleotides at sites with high base composition variability, ancestral tRNA sequences from Bayesian analyses were more compatible with canonical base pairing than were those inferred by other methods. Thus, nucleotide bias in reconstructed sequences apparently can lead to serious bias and inaccuracies in functional predictions.

Low dose reconstruction algorithm for differential phase contrast imaging.

PubMed

Wang, Zhentian; Huang, Zhifeng; Zhang, Li; Chen, Zhiqiang; Kang, Kejun; Yin, Hongxia; Wang, Zhenchang; Marco, Stampanoni

2011-01-01

Differential phase contrast imaging computed tomography (DPCI-CT) is a novel x-ray inspection method to reconstruct the distribution of refraction index rather than the attenuation coefficient in weakly absorbing samples. In this paper, we propose an iterative reconstruction algorithm for DPCI-CT which benefits from the new compressed sensing theory. We first realize a differential algebraic reconstruction technique (DART) by discretizing the projection process of the differential phase contrast imaging into a linear partial derivative matrix. In this way the compressed sensing reconstruction problem of DPCI reconstruction can be transformed to a resolved problem in the transmission imaging CT. Our algorithm has the potential to reconstruct the refraction index distribution of the sample from highly undersampled projection data. Thus it can significantly reduce the dose and inspection time. The proposed algorithm has been validated by numerical simulations and actual experiments.

Experimental Reconstructions of Surface Temperature using the PAGES 2k Network

NASA Astrophysics Data System (ADS)

Wang, Jianghao; Emile-Geay, Julien; Vaccaro, Adam; Guillot, Dominique; Rajaratnam, Bala

2014-05-01

Climate field reconstructions (CFRs) of the Common Era provide uniquely detailed characterizations of natural, low-frequency climate variability beyond the instrumental era. However, the accuracy and robustness of global-scale CFRs remains an open question. For instance, Wang et al. (2013) showed that CFRs are greatly method-dependent, highlighting the danger of forming dynamical interpretations based on a single reconstruction (e.g. Mann et al., 2009). This study will present a set of new reconstructions of global surface temperature and compare them with existing reconstructions from the IPCC AR5. The reconstructions are derived using the PAGES 2k network, which is composed of 501 high-resolution temperature-sensitive proxies from eight continental-scale regions (PAGES2K Consortium, 2013). Four CFR techniques are used to produce reconstructions, including RegEM-TTLS, the Mann et al. (2009) implementation of RegEM-TTLS (hereinafter M09-TTLS), CCA (Smerdon et al., 2010) and GraphEM (Guillot et al., submitted). First, we show that CFRs derived from the PAGES 2k network exhibit greater inter-method similarities than the same methods applied to the proxy network of Mann et al. (2009) (hereinafter M09 network). For instance, reconstructed NH mean temperature series using the PAGES 2k network are in better agreement over the last millennium than the M09-based reconstructions. Remarkably, for the reconstructed temperature difference between the Medieval Climate Anomaly and the Little Ice Age, the spatial patterns of the M09-based reconstructions are greatly divergent amongst methods. On the other hand, not a single PAGES 2k-based CFR displays the La Niña-like pattern found in Mann et al. (2009); rather, no systematic pattern emerges between the two epochs. Next, we quantify uncertainties associated with the PAGES 2k-based CFRs via ensemble methods, and show that GraphEM and CCA are less sensitive to random noise than RegEM-TTLS and M09-TTLS, consistent with pseudoproxy studies (Wang et al., 2014). The updated set of reconstructions, with uncertainties, will provide a broader context for the evaluation of the unusual character of the 20th century warming. The reconstructions will also be used to constrain fingerprinting analyses, which is particularly useful in discriminating between externally forced signals and internal variability. Reference: Guillot, D., B. Rajaratnam, and J. Emile-Geay, Statistical paleoclimate reconstructions via markov random fields, Ann. Appl. Stat., submitted. Mann, M. E., Z. Zhang, S. Rutherford, R. S. Bradley, M. K. Hughes, D. Shindell, C. Ammann, G. Faluvegi, and F. Ni, Global signatures and dynamical origins of the little ice age and medieval climate anomaly, Science, 326 (5957), 1256-1260, 2009. PAGES2K Consortium, Continental-scale temperature variability during the past two millennia, Nature Geosci, 6(5), 339-346, 2013. Smerdon, J. E., A. Kaplan, D. Chang, and M. N. Evans, A pseudoproxy evaluation of the CCA and RegEM methods for reconstructing climate fields of the last millennium*, J. Clim., 23(18), 4856-4880, 2010. Wang, J., J. Emile-Geay, A. D. Vaccaro, and D. Guillot, Fragility of estimated spatial temperature patterns in climate field reconstructions of the Common Era, Abstract PP41B-03 presented at Fall Meeting, AGU, San Francisco, Calif., 2013. Wang, J., J. Emile-Geay, D. Guillot, J. Smerdon, and B. Rajaratnam, Evaluating climate field reconstruction techniques using improved emulations of real-world conditions, Clim.Past, 10(1), 1-19, 2014.

Optimization-based reconstruction for reduction of CBCT artifact in IGRT

NASA Astrophysics Data System (ADS)

Xia, Dan; Zhang, Zheng; Paysan, Pascal; Seghers, Dieter; Brehm, Marcus; Munro, Peter; Sidky, Emil Y.; Pelizzari, Charles; Pan, Xiaochuan

2016-04-01

Kilo-voltage cone-beam computed tomography (CBCT) plays an important role in image guided radiation therapy (IGRT) by providing 3D spatial information of tumor potentially useful for optimizing treatment planning. In current IGRT CBCT system, reconstructed images obtained with analytic algorithms, such as FDK algorithm and its variants, may contain artifacts. In an attempt to compensate for the artifacts, we investigate optimization-based reconstruction algorithms such as the ASD-POCS algorithm for potentially reducing arti- facts in IGRT CBCT images. In this study, using data acquired with a physical phantom and a patient subject, we demonstrate that the ASD-POCS reconstruction can significantly reduce artifacts observed in clinical re- constructions. Moreover, patient images reconstructed by use of the ASD-POCS algorithm indicate a contrast level of soft-tissue improved over that of the clinical reconstruction. We have also performed reconstructions from sparse-view data, and observe that, for current clinical imaging conditions, ASD-POCS reconstructions from data collected at one half of the current clinical projection views appear to show image quality, in terms of spatial and soft-tissue-contrast resolution, higher than that of the corresponding clinical reconstructions.

A density based algorithm to detect cavities and holes from planar points

NASA Astrophysics Data System (ADS)

Zhu, Jie; Sun, Yizhong; Pang, Yueyong

2017-12-01

Delaunay-based shape reconstruction algorithms are widely used in approximating the shape from planar points. However, these algorithms cannot ensure the optimality of varied reconstructed cavity boundaries and hole boundaries. This inadequate reconstruction can be primarily attributed to the lack of efficient mathematic formulation for the two structures (hole and cavity). In this paper, we develop an efficient algorithm for generating cavities and holes from planar points. The algorithm yields the final boundary based on an iterative removal of the Delaunay triangulation. Our algorithm is mainly divided into two steps, namely, rough and refined shape reconstructions. The rough shape reconstruction performed by the algorithm is controlled by a relative parameter. Based on the rough result, the refined shape reconstruction mainly aims to detect holes and pure cavities. Cavity and hole are conceptualized as a structure with a low-density region surrounded by the high-density region. With this structure, cavity and hole are characterized by a mathematic formulation called as compactness of point formed by the length variation of the edges incident to point in Delaunay triangulation. The boundaries of cavity and hole are then found by locating a shape gradient change in compactness of point set. The experimental comparison with other shape reconstruction approaches shows that the proposed algorithm is able to accurately yield the boundaries of cavity and hole with varying point set densities and distributions.

Low Dose CT Reconstruction via Edge-preserving Total Variation Regularization

PubMed Central

Tian, Zhen; Jia, Xun; Yuan, Kehong; Pan, Tinsu; Jiang, Steve B.

2014-01-01

High radiation dose in CT scans increases a lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with Total Variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data acquired at low mAs levels in order to reduce the imaging dose. Nonetheless, the low contrast structures tend to be smoothed out by the TV regularization, posing a great challenge for the TV method. To solve this problem, in this work we develop an iterative CT reconstruction algorithm with edge-preserving TV regularization to reconstruct CT images from highly undersampled data obtained at low mAs levels. The CT image is reconstructed by minimizing an energy consisting of an edge-preserving TV norm and a data fidelity term posed by the x-ray projections. The edge-preserving TV term is proposed to preferentially perform smoothing only on non-edge part of the image in order to better preserve the edges, which is realized by introducing a penalty weight to the original total variation norm. During the reconstruction process, the pixels at edges would be gradually identified and given small penalty weight. Our iterative algorithm is implemented on GPU to improve its speed. We test our reconstruction algorithm on a digital NCAT phantom, a physical chest phantom, and a Catphan phantom. Reconstruction results from a conventional FBP algorithm and a TV regularization method without edge preserving penalty are also presented for comparison purpose. The experimental results illustrate that both TV-based algorithm and our edge-preserving TV algorithm outperform the conventional FBP algorithm in suppressing the streaking artifacts and image noise under the low dose context. Our edge-preserving algorithm is superior to the TV-based algorithm in that it can preserve more information of low contrast structures and therefore maintain acceptable spatial resolution. PMID:21860076

Comparison among Reconstruction Algorithms for Quantitative Analysis of 11C-Acetate Cardiac PET Imaging.

PubMed

Shi, Ximin; Li, Nan; Ding, Haiyan; Dang, Yonghong; Hu, Guilan; Liu, Shuai; Cui, Jie; Zhang, Yue; Li, Fang; Zhang, Hui; Huo, Li

2018-01-01

Kinetic modeling of dynamic 11 C-acetate PET imaging provides quantitative information for myocardium assessment. The quality and quantitation of PET images are known to be dependent on PET reconstruction methods. This study aims to investigate the impacts of reconstruction algorithms on the quantitative analysis of dynamic 11 C-acetate cardiac PET imaging. Suspected alcoholic cardiomyopathy patients ( N = 24) underwent 11 C-acetate dynamic PET imaging after low dose CT scan. PET images were reconstructed using four algorithms: filtered backprojection (FBP), ordered subsets expectation maximization (OSEM), OSEM with time-of-flight (TOF), and OSEM with both time-of-flight and point-spread-function (TPSF). Standardized uptake values (SUVs) at different time points were compared among images reconstructed using the four algorithms. Time-activity curves (TACs) in myocardium and blood pools of ventricles were generated from the dynamic image series. Kinetic parameters K 1 and k 2 were derived using a 1-tissue-compartment model for kinetic modeling of cardiac flow from 11 C-acetate PET images. Significant image quality improvement was found in the images reconstructed using iterative OSEM-type algorithms (OSME, TOF, and TPSF) compared with FBP. However, no statistical differences in SUVs were observed among the four reconstruction methods at the selected time points. Kinetic parameters K 1 and k 2 also exhibited no statistical difference among the four reconstruction algorithms in terms of mean value and standard deviation. However, for the correlation analysis, OSEM reconstruction presented relatively higher residual in correlation with FBP reconstruction compared with TOF and TPSF reconstruction, and TOF and TPSF reconstruction were highly correlated with each other. All the tested reconstruction algorithms performed similarly for quantitative analysis of 11 C-acetate cardiac PET imaging. TOF and TPSF yielded highly consistent kinetic parameter results with superior image quality compared with FBP. OSEM was relatively less reliable. Both TOF and TPSF were recommended for cardiac 11 C-acetate kinetic analysis.

Sinogram-based adaptive iterative reconstruction for sparse view x-ray computed tomography

NASA Astrophysics Data System (ADS)

Trinca, D.; Zhong, Y.; Wang, Y.-Z.; Mamyrbayev, T.; Libin, E.

2016-10-01

With the availability of more powerful computing processors, iterative reconstruction algorithms have recently been successfully implemented as an approach to achieving significant dose reduction in X-ray CT. In this paper, we propose an adaptive iterative reconstruction algorithm for X-ray CT, that is shown to provide results comparable to those obtained by proprietary algorithms, both in terms of reconstruction accuracy and execution time. The proposed algorithm is thus provided for free to the scientific community, for regular use, and for possible further optimization.

Axial Cone Beam Reconstruction by Weighted BPF/DBPF and Orthogonal Butterfly Filtering

PubMed Central

Tang, Shaojie; Tang, Xiangyang

2016-01-01

Goal The backprojection-filtration (BPF) and the derivative backprojection filtered (DBPF) algorithms, in which Hilbert filtering is the common algorithmic feature, are originally derived for exact helical reconstruction from cone beam (CB) scan data and axial reconstruction from fan beam data, respectively. These two algorithms can be heuristically extended for image reconstruction from axial CB scan data, but induce severe artifacts in images located away from the central plane determined by the circular source trajectory. We propose an algorithmic solution herein to eliminate the artifacts. Methods The solution is an integration of three-dimensional (3D) weighted axial CB-BPF/ DBPF algorithm with orthogonal butterfly filtering, namely axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering. Using the computer simulated Forbild head and thoracic phantoms that are rigorous in inspecting reconstruction accuracy and an anthropomorphic thoracic phantom with projection data acquired by a CT scanner, we evaluate performance of the proposed algorithm. Results Preliminary results show that the orthogonal butterfly filtering can eliminate the severe streak artifacts existing in the images reconstructed by the 3D weighted axial CB-BPF/DBPF algorithm located at off-central planes. Conclusion Integrated with orthogonal butterfly filtering, the 3D weighted CB-BPF/DBPF algorithm can perform at least as well as the 3D weighted CB-FBP algorithm in image reconstruction from axial CB scan data. Significance The proposed 3D weighted axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering can be an algorithmic solution for CT imaging in extensive clinical and preclinical applications. PMID:26660512

Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals

NASA Astrophysics Data System (ADS)

Meyer, Hanna; Kühnlein, Meike; Appelhans, Tim; Nauss, Thomas

2016-03-01

Machine learning (ML) algorithms have successfully been demonstrated to be valuable tools in satellite-based rainfall retrievals which show the practicability of using ML algorithms when faced with high dimensional and complex data. Moreover, recent developments in parallel computing with ML present new possibilities for training and prediction speed and therefore make their usage in real-time systems feasible. This study compares four ML algorithms - random forests (RF), neural networks (NNET), averaged neural networks (AVNNET) and support vector machines (SVM) - for rainfall area detection and rainfall rate assignment using MSG SEVIRI data over Germany. Satellite-based proxies for cloud top height, cloud top temperature, cloud phase and cloud water path serve as predictor variables. The results indicate an overestimation of rainfall area delineation regardless of the ML algorithm (averaged bias = 1.8) but a high probability of detection ranging from 81% (SVM) to 85% (NNET). On a 24-hour basis, the performance of the rainfall rate assignment yielded R2 values between 0.39 (SVM) and 0.44 (AVNNET). Though the differences in the algorithms' performance were rather small, NNET and AVNNET were identified as the most suitable algorithms. On average, they demonstrated the best performance in rainfall area delineation as well as in rainfall rate assignment. NNET's computational speed is an additional advantage in work with large datasets such as in remote sensing based rainfall retrievals. However, since no single algorithm performed considerably better than the others we conclude that further research in providing suitable predictors for rainfall is of greater necessity than an optimization through the choice of the ML algorithm.

Four-dimensional volume-of-interest reconstruction for cone-beam computed tomography-guided radiation therapy.

PubMed

Ahmad, Moiz; Balter, Peter; Pan, Tinsu

2011-10-01

Data sufficiency are a major problem in four-dimensional cone-beam computed tomography (4D-CBCT) on linear accelerator-integrated scanners for image-guided radiotherapy. Scan times must be in the range of 4-6 min to avoid undersampling artifacts. Various image reconstruction algorithms have been proposed to accommodate undersampled data acquisitions, but these algorithms are computationally expensive, may require long reconstruction times, and may require algorithm parameters to be optimized. The authors present a novel reconstruction method, 4D volume-of-interest (4D-VOI) reconstruction which suppresses undersampling artifacts and resolves lung tumor motion for undersampled 1-min scans. The 4D-VOI reconstruction is much less computationally expensive than other 4D-CBCT algorithms. The 4D-VOI method uses respiration-correlated projection data to reconstruct a four-dimensional (4D) image inside a VOI containing the moving tumor, and uncorrelated projection data to reconstruct a three-dimensional (3D) image outside the VOI. Anatomical motion is resolved inside the VOI and blurred outside the VOI. The authors acquired a 1-min. scan of an anthropomorphic chest phantom containing a moving water-filled sphere. The authors also used previously acquired 1-min scans for two lung cancer patients who had received CBCT-guided radiation therapy. The same raw data were used to test and compare the 4D-VOI reconstruction with the standard 4D reconstruction and the McKinnon-Bates (MB) reconstruction algorithms. Both the 4D-VOI and the MB reconstructions suppress nearly all the streak artifacts compared with the standard 4D reconstruction, but the 4D-VOI has 3-8 times greater contrast-to-noise ratio than the MB reconstruction. In the dynamic chest phantom study, the 4D-VOI and the standard 4D reconstructions both resolved a moving sphere with an 18 mm displacement. The 4D-VOI reconstruction shows a motion blur of only 3 mm, whereas the MB reconstruction shows a motion blur of 13 mm. With graphics processing unit hardware used to accelerate computations, the 4D-VOI reconstruction required a 40-s reconstruction time. 4D-VOI reconstruction effectively reduces undersampling artifacts and resolves lung tumor motion in 4D-CBCT. The 4D-VOI reconstruction is computationally inexpensive compared with more sophisticated iterative algorithms. Compared with these algorithms, our 4D-VOI reconstruction is an attractive alternative in 4D-CBCT for reconstructing target motion without generating numerous streak artifacts.

Four-dimensional volume-of-interest reconstruction for cone-beam computed tomography-guided radiation therapy

PubMed Central

Ahmad, Moiz; Balter, Peter; Pan, Tinsu

2011-01-01

Purpose: Data sufficiency are a major problem in four-dimensional cone-beam computed tomography (4D-CBCT) on linear accelerator-integrated scanners for image-guided radiotherapy. Scan times must be in the range of 4–6 min to avoid undersampling artifacts. Various image reconstruction algorithms have been proposed to accommodate undersampled data acquisitions, but these algorithms are computationally expensive, may require long reconstruction times, and may require algorithm parameters to be optimized. The authors present a novel reconstruction method, 4D volume-of-interest (4D-VOI) reconstruction which suppresses undersampling artifacts and resolves lung tumor motion for undersampled 1-min scans. The 4D-VOI reconstruction is much less computationally expensive than other 4D-CBCT algorithms. Methods: The 4D-VOI method uses respiration-correlated projection data to reconstruct a four-dimensional (4D) image inside a VOI containing the moving tumor, and uncorrelated projection data to reconstruct a three-dimensional (3D) image outside the VOI. Anatomical motion is resolved inside the VOI and blurred outside the VOI. The authors acquired a 1-min. scan of an anthropomorphic chest phantom containing a moving water-filled sphere. The authors also used previously acquired 1-min scans for two lung cancer patients who had received CBCT-guided radiation therapy. The same raw data were used to test and compare the 4D-VOI reconstruction with the standard 4D reconstruction and the McKinnon-Bates (MB) reconstruction algorithms. Results: Both the 4D-VOI and the MB reconstructions suppress nearly all the streak artifacts compared with the standard 4D reconstruction, but the 4D-VOI has 3–8 times greater contrast-to-noise ratio than the MB reconstruction. In the dynamic chest phantom study, the 4D-VOI and the standard 4D reconstructions both resolved a moving sphere with an 18 mm displacement. The 4D-VOI reconstruction shows a motion blur of only 3 mm, whereas the MB reconstruction shows a motion blur of 13 mm. With graphics processing unit hardware used to accelerate computations, the 4D-VOI reconstruction required a 40-s reconstruction time. Conclusions: 4D-VOI reconstruction effectively reduces undersampling artifacts and resolves lung tumor motion in 4D-CBCT. The 4D-VOI reconstruction is computationally inexpensive compared with more sophisticated iterative algorithms. Compared with these algorithms, our 4D-VOI reconstruction is an attractive alternative in 4D-CBCT for reconstructing target motion without generating numerous streak artifacts. PMID:21992381

Varying-energy CT imaging method based on EM-TV

NASA Astrophysics Data System (ADS)

Chen, Ping; Han, Yan

2016-11-01

For complicated structural components with wide x-ray attenuation ranges, conventional fixed-energy computed tomography (CT) imaging cannot obtain all the structural information. This limitation results in a shortage of CT information because the effective thickness of the components along the direction of x-ray penetration exceeds the limit of the dynamic range of the x-ray imaging system. To address this problem, a varying-energy x-ray CT imaging method is proposed. In this new method, the tube voltage is adjusted several times with the fixed lesser interval. Next, the fusion of grey consistency and logarithm demodulation are applied to obtain full and lower noise projection with a high dynamic range (HDR). In addition, for the noise suppression problem of the analytical method, EM-TV (expectation maximization-total Jvariation) iteration reconstruction is used. In the process of iteration, the reconstruction result obtained at one x-ray energy is used as the initial condition of the next iteration. An accompanying experiment demonstrates that this EM-TV reconstruction can also extend the dynamic range of x-ray imaging systems and provide a higher reconstruction quality relative to the fusion reconstruction method.

UV Reconstruction Algorithm And Diurnal Cycle Variability

NASA Astrophysics Data System (ADS)

Curylo, Aleksander; Litynska, Zenobia; Krzyscin, Janusz; Bogdanska, Barbara

2009-03-01

UV reconstruction is a method of estimation of surface UV with the use of available actinometrical and aerological measurements. UV reconstruction is necessary for the study of long-term UV change. A typical series of UV measurements is not longer than 15 years, which is too short for trend estimation. The essential problem in the reconstruction algorithm is the good parameterization of clouds. In our previous algorithm we used an empirical relation between Cloud Modification Factor (CMF) in global radiation and CMF in UV. The CMF is defined as the ratio between measured and modelled irradiances. Clear sky irradiance was calculated with a solar radiative transfer model. In the proposed algorithm, the time variability of global radiation during the diurnal cycle is used as an additional source of information. For elaborating an improved reconstruction algorithm relevant data from Legionowo [52.4 N, 21.0 E, 96 m a.s.l], Poland were collected with the following instruments: NILU-UV multi channel radiometer, Kipp&Zonen pyranometer, radiosonde profiles of ozone, humidity and temperature. The proposed algorithm has been used for reconstruction of UV at four Polish sites: Mikolajki, Kolobrzeg, Warszawa-Bielany and Zakopane since the early 1960s. Krzyscin's reconstruction of total ozone has been used in the calculations.

Reconstruction of three-dimensional ultrasound images based on cyclic Savitzky-Golay filters

NASA Astrophysics Data System (ADS)

Toonkum, Pollakrit; Suwanwela, Nijasri C.; Chinrungrueng, Chedsada

2011-01-01

We present a new algorithm for reconstructing a three-dimensional (3-D) ultrasound image from a series of two-dimensional B-scan ultrasound slices acquired in the mechanical linear scanning framework. Unlike most existing 3-D ultrasound reconstruction algorithms, which have been developed and evaluated in the freehand scanning framework, the new algorithm has been designed to capitalize the regularity pattern of the mechanical linear scanning, where all the B-scan slices are precisely parallel and evenly spaced. The new reconstruction algorithm, referred to as the cyclic Savitzky-Golay (CSG) reconstruction filter, is an improvement on the original Savitzky-Golay filter in two respects: First, it is extended to accept a 3-D array of data as the filter input instead of a one-dimensional data sequence. Second, it incorporates the cyclic indicator function in its least-squares objective function so that the CSG algorithm can simultaneously perform both smoothing and interpolating tasks. The performance of the CSG reconstruction filter compared to that of most existing reconstruction algorithms in generating a 3-D synthetic test image and a clinical 3-D carotid artery bifurcation in the mechanical linear scanning framework are also reported.

Patient-bounded extrapolation using low-dose priors for volume-of-interest imaging in C-arm CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xia, Y.; Maier, A.; Berger, M.

2015-04-15

Purpose: Three-dimensional (3D) volume-of-interest (VOI) imaging with C-arm systems provides anatomical information in a predefined 3D target region at a considerably low x-ray dose. However, VOI imaging involves laterally truncated projections from which conventional reconstruction algorithms generally yield images with severe truncation artifacts. Heuristic based extrapolation methods, e.g., water cylinder extrapolation, typically rely on techniques that complete the truncated data by means of a continuity assumption and thus appear to be ad-hoc. It is our goal to improve the image quality of VOI imaging by exploiting existing patient-specific prior information in the workflow. Methods: A necessary initial step prior tomore » a 3D acquisition is to isocenter the patient with respect to the target to be scanned. To this end, low-dose fluoroscopic x-ray acquisitions are usually applied from anterior–posterior (AP) and medio-lateral (ML) views. Based on this, the patient is isocentered by repositioning the table. In this work, we present a patient-bounded extrapolation method that makes use of these noncollimated fluoroscopic images to improve image quality in 3D VOI reconstruction. The algorithm first extracts the 2D patient contours from the noncollimated AP and ML fluoroscopic images. These 2D contours are then combined to estimate a volumetric model of the patient. Forward-projecting the shape of the model at the eventually acquired C-arm rotation views gives the patient boundary information in the projection domain. In this manner, we are in the position to substantially improve image quality by enforcing the extrapolated line profiles to end at the known patient boundaries, derived from the 3D shape model estimate. Results: The proposed method was evaluated on eight clinical datasets with different degrees of truncation. The proposed algorithm achieved a relative root mean square error (rRMSE) of about 1.0% with respect to the reference reconstruction on nontruncated data, even in the presence of severe truncation, compared to a rRMSE of 8.0% when applying a state-of-the-art heuristic extrapolation technique. Conclusions: The method we proposed in this paper leads to a major improvement in image quality for 3D C-arm based VOI imaging. It involves no additional radiation when using fluoroscopic images that are acquired during the patient isocentering process. The model estimation can be readily integrated into the existing interventional workflow without additional hardware.« less

Simulation of a small muon tomography station system based on RPCs

NASA Astrophysics Data System (ADS)

Chen, S.; Li, Q.; Ma, J.; Kong, H.; Ye, Y.; Gao, J.; Jiang, Y.

2014-10-01

In this work, Monte Carlo simulations were used to study the performance of a small muon Tomography Station based on four glass resistive plate chambers(RPCs) with a spatial resolution of approximately 1.0mm (FWHM). We developed a simulation code to generate cosmic ray muons with the appropriate distribution of energies and angles. PoCA and EM algorithm were used to rebuild the objects for comparison. We compared Z discrimination time with and without muon momentum measurement. The relation between Z discrimination time and spatial resolution was also studied. Simulation results suggest that mean scattering angle is a better Z indicator and upgrading to larger RPCs will improve reconstruction image quality.

Correlative Stochastic Optical Reconstruction Microscopy and Electron Microscopy

PubMed Central

Kim, Doory; Deerinck, Thomas J.; Sigal, Yaron M.; Babcock, Hazen P.; Ellisman, Mark H.; Zhuang, Xiaowei

2015-01-01

Correlative fluorescence light microscopy and electron microscopy allows the imaging of spatial distributions of specific biomolecules in the context of cellular ultrastructure. Recent development of super-resolution fluorescence microscopy allows the location of molecules to be determined with nanometer-scale spatial resolution. However, correlative super-resolution fluorescence microscopy and electron microscopy (EM) still remains challenging because the optimal specimen preparation and imaging conditions for super-resolution fluorescence microscopy and EM are often not compatible. Here, we have developed several experiment protocols for correlative stochastic optical reconstruction microscopy (STORM) and EM methods, both for un-embedded samples by applying EM-specific sample preparations after STORM imaging and for embedded and sectioned samples by optimizing the fluorescence under EM fixation, staining and embedding conditions. We demonstrated these methods using a variety of cellular targets. PMID:25874453

Brian Ball | NREL

Science.gov Websites

Integration program, developing inverse modeling algorithms to calibrate building energy models, and is part related equipment. This work included developing an engineering grade operator training simulator for an

Experimental scheme and restoration algorithm of block compression sensing

NASA Astrophysics Data System (ADS)

Zhang, Linxia; Zhou, Qun; Ke, Jun

2018-01-01

Compressed Sensing (CS) can use the sparseness of a target to obtain its image with much less data than that defined by the Nyquist sampling theorem. In this paper, we study the hardware implementation of a block compression sensing system and its reconstruction algorithms. Different block sizes are used. Two algorithms, the orthogonal matching algorithm (OMP) and the full variation minimum algorithm (TV) are used to obtain good reconstructions. The influence of block size on reconstruction is also discussed.

Bayesian Estimation of Multidimensional Item Response Models. A Comparison of Analytic and Simulation Algorithms

ERIC Educational Resources Information Center

Martin-Fernandez, Manuel; Revuelta, Javier

2017-01-01

This study compares the performance of two estimation algorithms of new usage, the Metropolis-Hastings Robins-Monro (MHRM) and the Hamiltonian MCMC (HMC), with two consolidated algorithms in the psychometric literature, the marginal likelihood via EM algorithm (MML-EM) and the Markov chain Monte Carlo (MCMC), in the estimation of multidimensional…

Optimization, evaluation, and comparison of standard algorithms for image reconstruction with the VIP-PET.

PubMed

Mikhaylova, E; Kolstein, M; De Lorenzo, G; Chmeissani, M

2014-07-01

A novel positron emission tomography (PET) scanner design based on a room-temperature pixelated CdTe solid-state detector is being developed within the framework of the Voxel Imaging PET (VIP) Pathfinder project [1]. The simulation results show a great potential of the VIP to produce high-resolution images even in extremely challenging conditions such as the screening of a human head [2]. With unprecedented high channel density (450 channels/cm 3 ) image reconstruction is a challenge. Therefore optimization is needed to find the best algorithm in order to exploit correctly the promising detector potential. The following reconstruction algorithms are evaluated: 2-D Filtered Backprojection (FBP), Ordered Subset Expectation Maximization (OSEM), List-Mode OSEM (LM-OSEM), and the Origin Ensemble (OE) algorithm. The evaluation is based on the comparison of a true image phantom with a set of reconstructed images obtained by each algorithm. This is achieved by calculation of image quality merit parameters such as the bias, the variance and the mean square error (MSE). A systematic optimization of each algorithm is performed by varying the reconstruction parameters, such as the cutoff frequency of the noise filters and the number of iterations. The region of interest (ROI) analysis of the reconstructed phantom is also performed for each algorithm and the results are compared. Additionally, the performance of the image reconstruction methods is compared by calculating the modulation transfer function (MTF). The reconstruction time is also taken into account to choose the optimal algorithm. The analysis is based on GAMOS [3] simulation including the expected CdTe and electronic specifics.

Development and Translation of Hybrid Optoacoustic/Ultrasonic Tomography for Early Breast Cancer Detection

DTIC Science & Technology

2014-09-01

to develop an optimized system design and associated image reconstruction algorithms for a hybrid three-dimensional (3D) breast imaging system that...research is to develop an optimized system design and associated image reconstruction algorithms for a hybrid three-dimensional (3D) breast imaging ...i) developed time-of- flight extraction algorithms to perform USCT, (ii) developing image reconstruction algorithms for USCT, (iii) developed

Reduced projection angles for binary tomography with particle aggregation.

PubMed

Al-Rifaie, Mohammad Majid; Blackwell, Tim

This paper extends particle aggregate reconstruction technique (PART), a reconstruction algorithm for binary tomography based on the movement of particles. PART supposes that pixel values are particles, and that particles diffuse through the image, staying together in regions of uniform pixel value known as aggregates. In this work, a variation of this algorithm is proposed and a focus is placed on reducing the number of projections and whether this impacts the reconstruction of images. The algorithm is tested on three phantoms of varying sizes and numbers of forward projections and compared to filtered back projection, a random search algorithm and to SART, a standard algebraic reconstruction method. It is shown that the proposed algorithm outperforms the aforementioned algorithms on small numbers of projections. This potentially makes the algorithm attractive in scenarios where collecting less projection data are inevitable.

Robust estimation for class averaging in cryo-EM Single Particle Reconstruction.

PubMed

Huang, Chenxi; Tagare, Hemant D

2014-01-01

Single Particle Reconstruction (SPR) for Cryogenic Electron Microscopy (cryo-EM) aligns and averages the images extracted from micrographs to improve the Signal-to-Noise ratio (SNR). Outliers compromise the fidelity of the averaging. We propose a robust cross-correlation-like w-estimator for combating the effect of outliers on the average images in cryo-EM. The estimator accounts for the natural variation of signal contrast among the images and eliminates the need for a threshold for outlier rejection. We show that the influence function of our estimator is asymptotically bounded. Evaluations of the estimator on simulated and real cryo-EM images show good performance in the presence of outliers.

Wind reconstruction algorithm for Viking Lander 1

NASA Astrophysics Data System (ADS)

Kynkäänniemi, Tuomas; Kemppinen, Osku; Harri, Ari-Matti; Schmidt, Walter

2017-06-01

The wind measurement sensors of Viking Lander 1 (VL1) were only fully operational for the first 45 sols of the mission. We have developed an algorithm for reconstructing the wind measurement data after the wind measurement sensor failures. The algorithm for wind reconstruction enables the processing of wind data during the complete VL1 mission. The heater element of the quadrant sensor, which provided auxiliary measurement for wind direction, failed during the 45th sol of the VL1 mission. Additionally, one of the wind sensors of VL1 broke down during sol 378. Regardless of the failures, it was still possible to reconstruct the wind measurement data, because the failed components of the sensors did not prevent the determination of the wind direction and speed, as some of the components of the wind measurement setup remained intact for the complete mission. This article concentrates on presenting the wind reconstruction algorithm and methods for validating the operation of the algorithm. The algorithm enables the reconstruction of wind measurements for the complete VL1 mission. The amount of available sols is extended from 350 to 2245 sols.

Edge-oriented dual-dictionary guided enrichment (EDGE) for MRI-CT image reconstruction.

PubMed

Li, Liang; Wang, Bigong; Wang, Ge

2016-01-01

In this paper, we formulate the joint/simultaneous X-ray CT and MRI image reconstruction. In particular, a novel algorithm is proposed for MRI image reconstruction from highly under-sampled MRI data and CT images. It consists of two steps. First, a training dataset is generated from a series of well-registered MRI and CT images on the same patients. Then, an initial MRI image of a patient can be reconstructed via edge-oriented dual-dictionary guided enrichment (EDGE) based on the training dataset and a CT image of the patient. Second, an MRI image is reconstructed using the dictionary learning (DL) algorithm from highly under-sampled k-space data and the initial MRI image. Our algorithm can establish a one-to-one correspondence between the two imaging modalities, and obtain a good initial MRI estimation. Both noise-free and noisy simulation studies were performed to evaluate and validate the proposed algorithm. The results with different under-sampling factors show that the proposed algorithm performed significantly better than those reconstructed using the DL algorithm from MRI data alone.

A Fast EM Algorithm for Fitting Joint Models of a Binary Response and Multiple Longitudinal Covariates Subject to Detection Limits

PubMed Central

Bernhardt, Paul W.; Zhang, Daowen; Wang, Huixia Judy

2014-01-01

Joint modeling techniques have become a popular strategy for studying the association between a response and one or more longitudinal covariates. Motivated by the GenIMS study, where it is of interest to model the event of survival using censored longitudinal biomarkers, a joint model is proposed for describing the relationship between a binary outcome and multiple longitudinal covariates subject to detection limits. A fast, approximate EM algorithm is developed that reduces the dimension of integration in the E-step of the algorithm to one, regardless of the number of random effects in the joint model. Numerical studies demonstrate that the proposed approximate EM algorithm leads to satisfactory parameter and variance estimates in situations with and without censoring on the longitudinal covariates. The approximate EM algorithm is applied to analyze the GenIMS data set. PMID:25598564

Battery Control Algorithms | Transportation Research | NREL

Science.gov Websites

publications. Accounting for Lithium-Ion Battery Degradation in Electric Vehicle Charging Optimization Advanced Reformulation of Lithium-Ion Battery Models for Enabling Electric Transportation Fail-Safe Design for Large Capacity Lithium-Ion Battery Systems Contact Ying Shi Email | 303-275-4240

Sparsity-constrained PET image reconstruction with learned dictionaries

NASA Astrophysics Data System (ADS)

Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie

2016-09-01

PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging.

Local ROI Reconstruction via Generalized FBP and BPF Algorithms along More Flexible Curves.

PubMed

Yu, Hengyong; Ye, Yangbo; Zhao, Shiying; Wang, Ge

2006-01-01

We study the local region-of-interest (ROI) reconstruction problem, also referred to as the local CT problem. Our scheme includes two steps: (a) the local truncated normal-dose projections are extended to global dataset by combining a few global low-dose projections; (b) the ROI are reconstructed by either the generalized filtered backprojection (FBP) or backprojection-filtration (BPF) algorithms. The simulation results show that both the FBP and BPF algorithms can reconstruct satisfactory results with image quality in the ROI comparable to that of the corresponding global CT reconstruction.

Fast algorithm for wavefront reconstruction in XAO/SCAO with pyramid wavefront sensor

NASA Astrophysics Data System (ADS)

Shatokhina, Iuliia; Obereder, Andreas; Ramlau, Ronny

2014-08-01

We present a fast wavefront reconstruction algorithm developed for an extreme adaptive optics system equipped with a pyramid wavefront sensor on a 42m telescope. The method is called the Preprocessed Cumulative Reconstructor with domain decomposition (P-CuReD). The algorithm is based on the theoretical relationship between pyramid and Shack-Hartmann wavefront sensor data. The algorithm consists of two consecutive steps - a data preprocessing, and an application of the CuReD algorithm, which is a fast method for wavefront reconstruction from Shack-Hartmann sensor data. The closed loop simulation results show that the P-CuReD method provides the same reconstruction quality and is significantly faster than an MVM.

The effect of 18F-FDG-PET image reconstruction algorithms on the expression of characteristic metabolic brain network in Parkinson's disease.

PubMed

Tomše, Petra; Jensterle, Luka; Rep, Sebastijan; Grmek, Marko; Zaletel, Katja; Eidelberg, David; Dhawan, Vijay; Ma, Yilong; Trošt, Maja

2017-09-01

To evaluate the reproducibility of the expression of Parkinson's Disease Related Pattern (PDRP) across multiple sets of 18F-FDG-PET brain images reconstructed with different reconstruction algorithms. 18F-FDG-PET brain imaging was performed in two independent cohorts of Parkinson's disease (PD) patients and normal controls (NC). Slovenian cohort (20 PD patients, 20 NC) was scanned with Siemens Biograph mCT camera and reconstructed using FBP, FBP+TOF, OSEM, OSEM+TOF, OSEM+PSF and OSEM+PSF+TOF. American Cohort (20 PD patients, 7 NC) was scanned with GE Advance camera and reconstructed using 3DRP, FORE-FBP and FORE-Iterative. Expressions of two previously-validated PDRP patterns (PDRP-Slovenia and PDRP-USA) were calculated. We compared the ability of PDRP to discriminate PD patients from NC, differences and correlation between the corresponding subject scores and ROC analysis results across the different reconstruction algorithms. The expression of PDRP-Slovenia and PDRP-USA networks was significantly elevated in PD patients compared to NC (p<0.0001), regardless of reconstruction algorithms. PDRP expression strongly correlated between all studied algorithms and the reference algorithm (r⩾0.993, p<0.0001). Average differences in the PDRP expression among different algorithms varied within 0.73 and 0.08 of the reference value for PDRP-Slovenia and PDRP-USA, respectively. ROC analysis confirmed high similarity in sensitivity, specificity and AUC among all studied reconstruction algorithms. These results show that the expression of PDRP is reproducible across a variety of reconstruction algorithms of 18F-FDG-PET brain images. PDRP is capable of providing a robust metabolic biomarker of PD for multicenter 18F-FDG-PET images acquired in the context of differential diagnosis or clinical trials. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

NOTE: A BPF-type algorithm for CT with a curved PI detector

NASA Astrophysics Data System (ADS)

Tang, Jie; Zhang, Li; Chen, Zhiqiang; Xing, Yuxiang; Cheng, Jianping

2006-08-01

Helical cone-beam CT is used widely nowadays because of its rapid scan speed and efficient utilization of x-ray dose. Recently, an exact reconstruction algorithm for helical cone-beam CT was proposed (Zou and Pan 2004a Phys. Med. Biol. 49 941 59). The algorithm is referred to as a backprojection-filtering (BPF) algorithm. This BPF algorithm for a helical cone-beam CT with a flat-panel detector (FPD-HCBCT) requires minimum data within the Tam Danielsson window and can naturally address the problem of ROI reconstruction from data truncated in both longitudinal and transversal directions. In practical CT systems, detectors are expensive and always take a very important position in the total cost. Hence, we work on an exact reconstruction algorithm for a CT system with a detector of the smallest size, i.e., a curved PI detector fitting the Tam Danielsson window. The reconstruction algorithm is derived following the framework of the BPF algorithm. Numerical simulations are done to validate our algorithm in this study.

A BPF-type algorithm for CT with a curved PI detector.

PubMed

Tang, Jie; Zhang, Li; Chen, Zhiqiang; Xing, Yuxiang; Cheng, Jianping

2006-08-21

Helical cone-beam CT is used widely nowadays because of its rapid scan speed and efficient utilization of x-ray dose. Recently, an exact reconstruction algorithm for helical cone-beam CT was proposed (Zou and Pan 2004a Phys. Med. Biol. 49 941-59). The algorithm is referred to as a backprojection-filtering (BPF) algorithm. This BPF algorithm for a helical cone-beam CT with a flat-panel detector (FPD-HCBCT) requires minimum data within the Tam-Danielsson window and can naturally address the problem of ROI reconstruction from data truncated in both longitudinal and transversal directions. In practical CT systems, detectors are expensive and always take a very important position in the total cost. Hence, we work on an exact reconstruction algorithm for a CT system with a detector of the smallest size, i.e., a curved PI detector fitting the Tam-Danielsson window. The reconstruction algorithm is derived following the framework of the BPF algorithm. Numerical simulations are done to validate our algorithm in this study.

Refraction corrected calibration for aquatic locomotion research: application of Snell's law improves spatial accuracy.

PubMed

Henrion, Sebastian; Spoor, Cees W; Pieters, Remco P M; Müller, Ulrike K; van Leeuwen, Johan L

2015-07-07

Images of underwater objects are distorted by refraction at the water-glass-air interfaces and these distortions can lead to substantial errors when reconstructing the objects' position and shape. So far, aquatic locomotion studies have minimized refraction in their experimental setups and used the direct linear transform algorithm (DLT) to reconstruct position information, which does not model refraction explicitly. Here we present a refraction corrected ray-tracing algorithm (RCRT) that reconstructs position information using Snell's law. We validated this reconstruction by calculating 3D reconstruction error-the difference between actual and reconstructed position of a marker. We found that reconstruction error is small (typically less than 1%). Compared with the DLT algorithm, the RCRT has overall lower reconstruction errors, especially outside the calibration volume, and errors are essentially insensitive to camera position and orientation and the number and position of the calibration points. To demonstrate the effectiveness of the RCRT, we tracked an anatomical marker on a seahorse recorded with four cameras to reconstruct the swimming trajectory for six different camera configurations. The RCRT algorithm is accurate and robust and it allows cameras to be oriented at large angles of incidence and facilitates the development of accurate tracking algorithms to quantify aquatic manoeuvers.

Toward automated assessment of health Web page quality using the DISCERN instrument.

PubMed

Allam, Ahmed; Schulz, Peter J; Krauthammer, Michael

2017-05-01

As the Internet becomes the number one destination for obtaining health-related information, there is an increasing need to identify health Web pages that convey an accurate and current view of medical knowledge. In response, the research community has created multicriteria instruments for reliably assessing online medical information quality. One such instrument is DISCERN, which measures health Web page quality by assessing an array of features. In order to scale up use of the instrument, there is interest in automating the quality evaluation process by building machine learning (ML)-based DISCERN Web page classifiers. The paper addresses 2 key issues that are essential before constructing automated DISCERN classifiers: (1) generation of a robust DISCERN training corpus useful for training classification algorithms, and (2) assessment of the usefulness of the current DISCERN scoring schema as a metric for evaluating the performance of these algorithms. Using DISCERN, 272 Web pages discussing treatment options in breast cancer, arthritis, and depression were evaluated and rated by trained coders. First, different consensus models were compared to obtain a robust aggregated rating among the coders, suitable for a DISCERN ML training corpus. Second, a new DISCERN scoring criterion was proposed (features-based score) as an ML performance metric that is more reflective of the score distribution across different DISCERN quality criteria. First, we found that a probabilistic consensus model applied to the DISCERN instrument was robust against noise (random ratings) and superior to other approaches for building a training corpus. Second, we found that the established DISCERN scoring schema (overall score) is ill-suited to measure ML performance for automated classifiers. Use of a probabilistic consensus model is advantageous for building a training corpus for the DISCERN instrument, and use of a features-based score is an appropriate ML metric for automated DISCERN classifiers. The code for the probabilistic consensus model is available at https://bitbucket.org/A_2/em_dawid/ . © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm

PubMed Central

Sidky, Emil Y.; Jørgensen, Jakob H.; Pan, Xiaochuan

2012-01-01

The primal-dual optimization algorithm developed in Chambolle and Pock (CP), 2011 is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction. This algorithm allows for rapid prototyping of optimization problems for the purpose of designing iterative image reconstruction algorithms for CT. The primal-dual algorithm is briefly summarized in the article, and its potential for prototyping is demonstrated by explicitly deriving CP algorithm instances for many optimization problems relevant to CT. An example application modeling breast CT with low-intensity X-ray illumination is presented. PMID:22538474

Reconstruction of electrical impedance tomography (EIT) images based on the expectation maximum (EM) method.

PubMed

Wang, Qi; Wang, Huaxiang; Cui, Ziqiang; Yang, Chengyi

2012-11-01

Electrical impedance tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. The image reconstruction for EIT is an inverse problem, which is both non-linear and ill-posed. The traditional regularization method cannot avoid introducing negative values in the solution. The negativity of the solution produces artifacts in reconstructed images in presence of noise. A statistical method, namely, the expectation maximization (EM) method, is used to solve the inverse problem for EIT in this paper. The mathematical model of EIT is transformed to the non-negatively constrained likelihood minimization problem. The solution is obtained by the gradient projection-reduced Newton (GPRN) iteration method. This paper also discusses the strategies of choosing parameters. Simulation and experimental results indicate that the reconstructed images with higher quality can be obtained by the EM method, compared with the traditional Tikhonov and conjugate gradient (CG) methods, even with non-negative processing. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Accounting for hardware imperfections in EIT image reconstruction algorithms.

PubMed

Hartinger, Alzbeta E; Gagnon, Hervé; Guardo, Robert

2007-07-01

Electrical impedance tomography (EIT) is a non-invasive technique for imaging the conductivity distribution of a body section. Different types of EIT images can be reconstructed: absolute, time difference and frequency difference. Reconstruction algorithms are sensitive to many errors which translate into image artefacts. These errors generally result from incorrect modelling or inaccurate measurements. Every reconstruction algorithm incorporates a model of the physical set-up which must be as accurate as possible since any discrepancy with the actual set-up will cause image artefacts. Several methods have been proposed in the literature to improve the model realism, such as creating anatomical-shaped meshes, adding a complete electrode model and tracking changes in electrode contact impedances and positions. Absolute and frequency difference reconstruction algorithms are particularly sensitive to measurement errors and generally assume that measurements are made with an ideal EIT system. Real EIT systems have hardware imperfections that cause measurement errors. These errors translate into image artefacts since the reconstruction algorithm cannot properly discriminate genuine measurement variations produced by the medium under study from those caused by hardware imperfections. We therefore propose a method for eliminating these artefacts by integrating a model of the system hardware imperfections into the reconstruction algorithms. The effectiveness of the method has been evaluated by reconstructing absolute, time difference and frequency difference images with and without the hardware model from data acquired on a resistor mesh phantom. Results have shown that artefacts are smaller for images reconstructed with the model, especially for frequency difference imaging.

A sparsity-based iterative algorithm for reconstruction of micro-CT images from highly undersampled projection datasets obtained with a synchrotron X-ray source

NASA Astrophysics Data System (ADS)

Melli, S. Ali; Wahid, Khan A.; Babyn, Paul; Cooper, David M. L.; Gopi, Varun P.

2016-12-01

Synchrotron X-ray Micro Computed Tomography (Micro-CT) is an imaging technique which is increasingly used for non-invasive in vivo preclinical imaging. However, it often requires a large number of projections from many different angles to reconstruct high-quality images leading to significantly high radiation doses and long scan times. To utilize this imaging technique further for in vivo imaging, we need to design reconstruction algorithms that reduce the radiation dose and scan time without reduction of reconstructed image quality. This research is focused on using a combination of gradient-based Douglas-Rachford splitting and discrete wavelet packet shrinkage image denoising methods to design an algorithm for reconstruction of large-scale reduced-view synchrotron Micro-CT images with acceptable quality metrics. These quality metrics are computed by comparing the reconstructed images with a high-dose reference image reconstructed from 1800 equally spaced projections spanning 180°. Visual and quantitative-based performance assessment of a synthetic head phantom and a femoral cortical bone sample imaged in the biomedical imaging and therapy bending magnet beamline at the Canadian Light Source demonstrates that the proposed algorithm is superior to the existing reconstruction algorithms. Using the proposed reconstruction algorithm to reduce the number of projections in synchrotron Micro-CT is an effective way to reduce the overall radiation dose and scan time which improves in vivo imaging protocols.

Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports.

PubMed

Chen, Po-Hao; Zafar, Hanna; Galperin-Aizenberg, Maya; Cook, Tessa

2018-04-01

A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports. However, the codependent effects of NLP and ML in this context have not been well-studied. Between April 1, 2015 and November 1, 2016, 9418 cross-sectional abdomen/pelvis CT and MR examinations containing our internal structured reporting element for cancer were separated into four categories: Progression, Stable Disease, Improvement, or No Cancer. We combined each of three NLP techniques with five ML algorithms to predict the assigned label using the unstructured report text and compared the performance of each combination. The three NLP algorithms included term frequency-inverse document frequency (TF-IDF), term frequency weighting (TF), and 16-bit feature hashing. The ML algorithms included logistic regression (LR), random decision forest (RDF), one-vs-all support vector machine (SVM), one-vs-all Bayes point machine (BPM), and fully connected neural network (NN). The best-performing NLP model consisted of tokenized unigrams and bigrams with TF-IDF. Increasing N-gram length yielded little to no added benefit for most ML algorithms. With all parameters optimized, SVM had the best performance on the test dataset, with 90.6 average accuracy and F score of 0.813. The interplay between ML and NLP algorithms and their effect on interpretation accuracy is complex. The best accuracy is achieved when both algorithms are optimized concurrently.

Parameter Estimation of Multiple Frequency-Hopping Signals with Two Sensors

PubMed Central

Pan, Jin; Ma, Boyuan

2018-01-01

This paper essentially focuses on parameter estimation of multiple wideband emitting sources with time-varying frequencies, such as two-dimensional (2-D) direction of arrival (DOA) and signal sorting, with a low-cost circular synthetic array (CSA) consisting of only two rotating sensors. Our basic idea is to decompose the received data, which is a superimposition of phase measurements from multiple sources into separated groups and separately estimate the DOA associated with each source. Motivated by joint parameter estimation, we propose to adopt the expectation maximization (EM) algorithm in this paper; our method involves two steps, namely, the expectation-step (E-step) and the maximization (M-step). In the E-step, the correspondence of each signal with its emitting source is found. Then, in the M-step, the maximum-likelihood (ML) estimates of the DOA parameters are obtained. These two steps are iteratively and alternatively executed to jointly determine the DOAs and sort multiple signals. Closed-form DOA estimation formulae are developed by ML estimation based on phase data, which also realize an optimal estimation. Directional ambiguity is also addressed by another ML estimation method based on received complex responses. The Cramer-Rao lower bound is derived for understanding the estimation accuracy and performance comparison. The verification of the proposed method is demonstrated with simulations. PMID:29617323

The PX-EM algorithm for fast stable fitting of Henderson's mixed model

PubMed Central

Foulley, Jean-Louis; Van Dyk, David A

2000-01-01

This paper presents procedures for implementing the PX-EM algorithm of Liu, Rubin and Wu to compute REML estimates of variance covariance components in Henderson's linear mixed models. The class of models considered encompasses several correlated random factors having the same vector length e.g., as in random regression models for longitudinal data analysis and in sire-maternal grandsire models for genetic evaluation. Numerical examples are presented to illustrate the procedures. Much better results in terms of convergence characteristics (number of iterations and time required for convergence) are obtained for PX-EM relative to the basic EM algorithm in the random regression. PMID:14736399

Marc Henry de Frahan | NREL

Science.gov Websites

Computing Project, Marc develops high-fidelity turbulence models to enhance simulation accuracy and efficient numerical algorithms for future high performance computing hardware architectures. Research Interests High performance computing High order numerical methods for computational fluid dynamics Fluid

Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data [Regularized maximum likelihood estimation of ground reflectivity from stripmap SAR data

DOE Office of Scientific and Technical Information (OSTI.GOV)

West, R. Derek; Gunther, Jacob H.; Moon, Todd K.

In this study, we derive a comprehensive forward model for the data collected by stripmap synthetic aperture radar (SAR) that is linear in the ground reflectivity parameters. It is also shown that if the noise model is additive, then the forward model fits into the linear statistical model framework, and the ground reflectivity parameters can be estimated by statistical methods. We derive the maximum likelihood (ML) estimates for the ground reflectivity parameters in the case of additive white Gaussian noise. Furthermore, we show that obtaining the ML estimates of the ground reflectivity requires two steps. The first step amounts tomore » a cross-correlation of the data with a model of the data acquisition parameters, and it is shown that this step has essentially the same processing as the so-called convolution back-projection algorithm. The second step is a complete system inversion that is capable of mitigating the sidelobes of the spatially variant impulse responses remaining after the correlation processing. We also state the Cramer-Rao lower bound (CRLB) for the ML ground reflectivity estimates.We show that the CRLB is linked to the SAR system parameters, the flight path of the SAR sensor, and the image reconstruction grid.We demonstrate the ML image formation and the CRLB bound for synthetically generated data.« less

Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data [Regularized maximum likelihood estimation of ground reflectivity from stripmap SAR data

DOE PAGES

West, R. Derek; Gunther, Jacob H.; Moon, Todd K.

2016-12-01

In this study, we derive a comprehensive forward model for the data collected by stripmap synthetic aperture radar (SAR) that is linear in the ground reflectivity parameters. It is also shown that if the noise model is additive, then the forward model fits into the linear statistical model framework, and the ground reflectivity parameters can be estimated by statistical methods. We derive the maximum likelihood (ML) estimates for the ground reflectivity parameters in the case of additive white Gaussian noise. Furthermore, we show that obtaining the ML estimates of the ground reflectivity requires two steps. The first step amounts tomore » a cross-correlation of the data with a model of the data acquisition parameters, and it is shown that this step has essentially the same processing as the so-called convolution back-projection algorithm. The second step is a complete system inversion that is capable of mitigating the sidelobes of the spatially variant impulse responses remaining after the correlation processing. We also state the Cramer-Rao lower bound (CRLB) for the ML ground reflectivity estimates.We show that the CRLB is linked to the SAR system parameters, the flight path of the SAR sensor, and the image reconstruction grid.We demonstrate the ML image formation and the CRLB bound for synthetically generated data.« less

Distributed Wind Research | Wind | NREL

Science.gov Websites

evaluation, and improve wind turbine and wind power plant performance. A photo of a snowy road leading to a single wind turbine surrounded by snow-covered pine trees against blue sky. Capabilities NREL's power plant and small wind turbine development. Algorithms and programs exist for simulating, designing

Energy Systems Integration News | Energy Systems Integration Facility |

Science.gov Websites

hierarchical control architecture that enables a hybrid control approach, where centralized control systems will be complemented by distributed control algorithms for solar inverters and autonomous control of ), involves developing a novel control scheme that provides system-wide monitoring and control using a small

Ultrasound-guided intrapleural positioning of pleural catheters: influence on immediate lung expansion and pleurodesis in patients with recurrent malignant pleural effusion.

PubMed

Araujo, Pedro Henrique Xavier Nabuco de; Terra, Ricardo Mingarini; Santos, Thiago da Silva; Chate, Rodrigo Caruso; Paiva, Antonio Fernando Lins de; Pêgo-Fernandes, Paulo Manuel

2017-01-01

To evaluate the role of intrapleural positioning of a pleural catheter in early lung expansion and pleurodesis success in patients with recurrent malignant pleural effusion (RMPE). This was a retrospective study nested into a larger prospective cohort study including patients with RMPE recruited from a tertiary university teaching hospital between June of 2009 and September of 2014. The patients underwent pleural catheter insertion followed by bedside pleurodesis. Chest CT scans were performed twice: immediately before pleurodesis (iCT) and 30 days after pleurodesis (CT30). Catheter positioning was categorized based on iCT scans as posterolateral, anterior, fissural, and subpulmonary. We used the pleural volume on iCT scans to estimate early lung expansion and the difference between the pleural volumes on CT30 and iCT scans to evaluate radiological success of pleurodesis. Clinical pleurodesis success was defined as no need for any other pleural procedure. Of the 131 eligible patients from the original study, 85 were included in this nested study (64 women; mean age: 60.74 years). Catheter tip positioning was subpulmonary in 35 patients (41%), anterior in 23 (27%), posterolateral in 17 (20%), and fissural in 10 (12%). No significant differences were found among the groups regarding early lung expansion (median residual pleural cavity = 377 mL; interquartile range: 171-722 mL; p = 0.645), radiological success of pleurodesis (median volume = 33 mL; interquartile range: -225 to 257 mL; p = 0.923), and clinical success of pleurodesis (85.8%; p = 0.676). Our results suggest that the position of the tip of the pleural catheter influences neither early lung expansion nor bedside pleurodesis success in patients with RMPE. Avaliar o papel do posicionamento intrapleural do cateter pleural na expansão pulmonar precoce e no sucesso da pleurodese em pacientes com derrame pleural maligno recorrente (DPMR). Trata-se de um estudo retrospectivo aninhado em um estudo prospectivo de coorte maior com pacientes com DPMR recrutados em um hospital-escola universitário terciário entre junho de 2009 e setembro de 2014. Os pacientes foram submetidos a inserção de cateter pleural e, em seguida, pleurodese à beira do leito. A TC de tórax foi realizada duas vezes: imediatamente antes da pleurodese (TCi) e 30 dias após a pleurodese (TC30). Com base na TCi, a posição do cateter foi classificada em posterolateral, anterior, fissural e subpulmonar. Usamos o volume pleural na TCi para estimar a expansão pulmonar precoce e a diferença entre os volumes pleurais na TC30 e na TCi a fim de avaliar o sucesso radiológico da pleurodese. Considerou-se que a pleurodese teve êxito clínico quando não foi necessário realizar nenhum outro procedimento pleural. Dos 131 pacientes elegíveis do estudo original, 85 foram incluídos neste estudo aninhado (64 mulheres; média de idade: 60,74 anos). A posição da ponta do cateter foi subpulmonar em 35 pacientes (41%), anterior em 23 (27%), posterolateral em 17 (20%) e fissural em 10 (12%). Não houve diferenças significativas entre os grupos quanto à expansão pulmonar precoce (mediana da cavidade pleural residual = 377 ml; intervalo interquartil: 171-722 ml; p = 0,645), sucesso radiológico da pleurodese (mediana do volume = 33 ml; intervalo interquartil: -225 a 257 ml; p = 0,923) e sucesso clínico da pleurodese (85,8%; p = 0,676). Nossos resultados sugerem que a posição da ponta do cateter pleural não influencia nem a expansão pulmonar precoce nem o sucesso da pleurodese à beira do leito em pacientes com DPMR.

Interior tomography in microscopic CT with image reconstruction constrained by full field of view scan at low spatial resolution

NASA Astrophysics Data System (ADS)

Luo, Shouhua; Shen, Tao; Sun, Yi; Li, Jing; Li, Guang; Tang, Xiangyang

2018-04-01

In high resolution (microscopic) CT applications, the scan field of view should cover the entire specimen or sample to allow complete data acquisition and image reconstruction. However, truncation may occur in projection data and results in artifacts in reconstructed images. In this study, we propose a low resolution image constrained reconstruction algorithm (LRICR) for interior tomography in microscopic CT at high resolution. In general, the multi-resolution acquisition based methods can be employed to solve the data truncation problem if the project data acquired at low resolution are utilized to fill up the truncated projection data acquired at high resolution. However, most existing methods place quite strict restrictions on the data acquisition geometry, which greatly limits their utility in practice. In the proposed LRICR algorithm, full and partial data acquisition (scan) at low and high resolutions, respectively, are carried out. Using the image reconstructed from sparse projection data acquired at low resolution as the prior, a microscopic image at high resolution is reconstructed from the truncated projection data acquired at high resolution. Two synthesized digital phantoms, a raw bamboo culm and a specimen of mouse femur, were utilized to evaluate and verify performance of the proposed LRICR algorithm. Compared with the conventional TV minimization based algorithm and the multi-resolution scout-reconstruction algorithm, the proposed LRICR algorithm shows significant improvement in reduction of the artifacts caused by data truncation, providing a practical solution for high quality and reliable interior tomography in microscopic CT applications. The proposed LRICR algorithm outperforms the multi-resolution scout-reconstruction method and the TV minimization based reconstruction for interior tomography in microscopic CT.

A 3D reconstruction algorithm for magneto-acoustic tomography with magnetic induction based on ultrasound transducer characteristics.

PubMed

Ma, Ren; Zhou, Xiaoqing; Zhang, Shunqi; Yin, Tao; Liu, Zhipeng

2016-12-21

In this study we present a three-dimensional (3D) reconstruction algorithm for magneto-acoustic tomography with magnetic induction (MAT-MI) based on the characteristics of the ultrasound transducer. The algorithm is investigated to solve the blur problem of the MAT-MI acoustic source image, which is caused by the ultrasound transducer and the scanning geometry. First, we established a transducer model matrix using measured data from the real transducer. With reference to the S-L model used in the computed tomography algorithm, a 3D phantom model of electrical conductivity is set up. Both sphere scanning and cylinder scanning geometries are adopted in the computer simulation. Then, using finite element analysis, the distribution of the eddy current and the acoustic source as well as the acoustic pressure can be obtained with the transducer model matrix. Next, using singular value decomposition, the inverse transducer model matrix together with the reconstruction algorithm are worked out. The acoustic source and the conductivity images are reconstructed using the proposed algorithm. Comparisons between an ideal point transducer and the realistic transducer are made to evaluate the algorithms. Finally, an experiment is performed using a graphite phantom. We found that images of the acoustic source reconstructed using the proposed algorithm are a better match than those using the previous one, the correlation coefficient of sphere scanning geometry is 98.49% and that of cylinder scanning geometry is 94.96%. Comparison between the ideal point transducer and the realistic transducer shows that the correlation coefficients are 90.2% in sphere scanning geometry and 86.35% in cylinder scanning geometry. The reconstruction of the graphite phantom experiment also shows a higher resolution using the proposed algorithm. We conclude that the proposed reconstruction algorithm, which considers the characteristics of the transducer, can obviously improve the resolution of the reconstructed image. This study can be applied to analyse the effect of the position of the transducer and the scanning geometry on imaging. It may provide a more precise method to reconstruct the conductivity distribution in MAT-MI.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Chao

Sparx, a new environment for Cryo-EM image processing; Cryo-EM, Single particle reconstruction, principal component analysis; Hardware Req.: PC, MAC, Supercomputer, Mainframe, Multiplatform, Workstation. Software Req.: operating system is Unix; Compiler C++; type of files: source code, object library, executable modules, compilation instructions; sample problem input data. Location/transmission: http://sparx-em.org; User manual & paper: http://sparx-em.org;

99aa/99ac data sets

Science.gov Websites

-redshifted), Observed Flux, Statistical Error (Based on the optimal extraction algorithm of the IRAF packages were acquired using different instrumental settings for the blue and red parts of the spectrum to avoid extracted for systematics checks of the wavelength calibration. Wavelength and flux calibration were applied

Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities

PubMed Central

2011-01-01

Background Gene regulatory networks play essential roles in living organisms to control growth, keep internal metabolism running and respond to external environmental changes. Understanding the connections and the activity levels of regulators is important for the research of gene regulatory networks. While relevance score based algorithms that reconstruct gene regulatory networks from transcriptome data can infer genome-wide gene regulatory networks, they are unfortunately prone to false positive results. Transcription factor activities (TFAs) quantitatively reflect the ability of the transcription factor to regulate target genes. However, classic relevance score based gene regulatory network reconstruction algorithms use models do not include the TFA layer, thus missing a key regulatory element. Results This work integrates TFA prediction algorithms with relevance score based network reconstruction algorithms to reconstruct gene regulatory networks with improved accuracy over classic relevance score based algorithms. This method is called Gene expression and Transcription factor activity based Relevance Network (GTRNetwork). Different combinations of TFA prediction algorithms and relevance score functions have been applied to find the most efficient combination. When the integrated GTRNetwork method was applied to E. coli data, the reconstructed genome-wide gene regulatory network predicted 381 new regulatory links. This reconstructed gene regulatory network including the predicted new regulatory links show promising biological significances. Many of the new links are verified by known TF binding site information, and many other links can be verified from the literature and databases such as EcoCyc. The reconstructed gene regulatory network is applied to a recent transcriptome analysis of E. coli during isobutanol stress. In addition to the 16 significantly changed TFAs detected in the original paper, another 7 significantly changed TFAs have been detected by using our reconstructed network. Conclusions The GTRNetwork algorithm introduces the hidden layer TFA into classic relevance score-based gene regulatory network reconstruction processes. Integrating the TFA biological information with regulatory network reconstruction algorithms significantly improves both detection of new links and reduces that rate of false positives. The application of GTRNetwork on E. coli gene transcriptome data gives a set of potential regulatory links with promising biological significance for isobutanol stress and other conditions. PMID:21668997

Simulation and performance of an artificial retina for 40 MHz track reconstruction

DOE PAGES

Abba, A.; Bedeschi, F.; Citterio, M.; ...

2015-03-05

We present the results of a detailed simulation of the artificial retina pattern-recognition algorithm, designed to reconstruct events with hundreds of charged-particle tracks in pixel and silicon detectors at LHCb with LHC crossing frequency of 40 MHz. Performances of the artificial retina algorithm are assessed using the official Monte Carlo samples of the LHCb experiment. We found performances for the retina pattern-recognition algorithm comparable with the full LHCb reconstruction algorithm.

A Survey of the Use of Iterative Reconstruction Algorithms in Electron Microscopy

PubMed Central

Otón, J.; Vilas, J. L.; Kazemi, M.; Melero, R.; del Caño, L.; Cuenca, J.; Conesa, P.; Gómez-Blanco, J.; Marabini, R.; Carazo, J. M.

2017-01-01

One of the key steps in Electron Microscopy is the tomographic reconstruction of a three-dimensional (3D) map of the specimen being studied from a set of two-dimensional (2D) projections acquired at the microscope. This tomographic reconstruction may be performed with different reconstruction algorithms that can be grouped into several large families: direct Fourier inversion methods, back-projection methods, Radon methods, or iterative algorithms. In this review, we focus on the latter family of algorithms, explaining the mathematical rationale behind the different algorithms in this family as they have been introduced in the field of Electron Microscopy. We cover their use in Single Particle Analysis (SPA) as well as in Electron Tomography (ET). PMID:29312997

Local ROI Reconstruction via Generalized FBP and BPF Algorithms along More Flexible Curves

PubMed Central

Ye, Yangbo; Zhao, Shiying; Wang, Ge

2006-01-01

We study the local region-of-interest (ROI) reconstruction problem, also referred to as the local CT problem. Our scheme includes two steps: (a) the local truncated normal-dose projections are extended to global dataset by combining a few global low-dose projections; (b) the ROI are reconstructed by either the generalized filtered backprojection (FBP) or backprojection-filtration (BPF) algorithms. The simulation results show that both the FBP and BPF algorithms can reconstruct satisfactory results with image quality in the ROI comparable to that of the corresponding global CT reconstruction. PMID:23165018

Material Interface Reconstruction in VisIt

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meredith, J S

In this paper, we first survey a variety of approaches to material interface reconstruction and their applicability to visualization, and we investigate the details of the current reconstruction algorithm in the VisIt scientific analysis and visualization tool. We then provide a novel implementation of the original VisIt algorithm that makes use of a wide range of the finite element zoo during reconstruction. This approach results in dramatic improvements in quality and performance without sacrificing the strengths of the VisIt algorithm as it relates to visualization.

Entropy-aware projected Landweber reconstruction for quantized block compressive sensing of aerial imagery

NASA Astrophysics Data System (ADS)

Liu, Hao; Li, Kangda; Wang, Bing; Tang, Hainie; Gong, Xiaohui

2017-01-01

A quantized block compressive sensing (QBCS) framework, which incorporates the universal measurement, quantization/inverse quantization, entropy coder/decoder, and iterative projected Landweber reconstruction, is summarized. Under the QBCS framework, this paper presents an improved reconstruction algorithm for aerial imagery, QBCS, with entropy-aware projected Landweber (QBCS-EPL), which leverages the full-image sparse transform without Wiener filter and an entropy-aware thresholding model for wavelet-domain image denoising. Through analyzing the functional relation between the soft-thresholding factors and entropy-based bitrates for different quantization methods, the proposed model can effectively remove wavelet-domain noise of bivariate shrinkage and achieve better image reconstruction quality. For the overall performance of QBCS reconstruction, experimental results demonstrate that the proposed QBCS-EPL algorithm significantly outperforms several existing algorithms. With the experiment-driven methodology, the QBCS-EPL algorithm can obtain better reconstruction quality at a relatively moderate computational cost, which makes it more desirable for aerial imagery applications.

A novel algorithm of super-resolution image reconstruction based on multi-class dictionaries for natural scene

NASA Astrophysics Data System (ADS)

Wu, Wei; Zhao, Dewei; Zhang, Huan

2015-12-01

Super-resolution image reconstruction is an effective method to improve the image quality. It has important research significance in the field of image processing. However, the choice of the dictionary directly affects the efficiency of image reconstruction. A sparse representation theory is introduced into the problem of the nearest neighbor selection. Based on the sparse representation of super-resolution image reconstruction method, a super-resolution image reconstruction algorithm based on multi-class dictionary is analyzed. This method avoids the redundancy problem of only training a hyper complete dictionary, and makes the sub-dictionary more representatives, and then replaces the traditional Euclidean distance computing method to improve the quality of the whole image reconstruction. In addition, the ill-posed problem is introduced into non-local self-similarity regularization. Experimental results show that the algorithm is much better results than state-of-the-art algorithm in terms of both PSNR and visual perception.

Golden Rays - March 2017 | Solar Research | NREL

Science.gov Websites

, test and deploy a data enhanced hierarchical control architecture that adopts a hybrid approach to grid control. A centralized control layer will be complemented by distributed control algorithms for solar inverters and autonomous control of grid edge devices. The other NREL project will develop a novel control

Evaluation of a Machine-Learning Algorithm for Treatment Planning in Prostate Low-Dose-Rate Brachytherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nicolae, Alexandru; Department of Medical Physics, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario; Morton, Gerard

Purpose: This work presents the application of a machine learning (ML) algorithm to automatically generate high-quality, prostate low-dose-rate (LDR) brachytherapy treatment plans. The ML algorithm can mimic characteristics of preoperative treatment plans deemed clinically acceptable by brachytherapists. The planning efficiency, dosimetry, and quality (as assessed by experts) of preoperative plans generated with an ML planning approach was retrospectively evaluated in this study. Methods and Materials: Preimplantation and postimplantation treatment plans were extracted from 100 high-quality LDR treatments and stored within a training database. The ML training algorithm matches similar features from a new LDR case to those within the trainingmore » database to rapidly obtain an initial seed distribution; plans were then further fine-tuned using stochastic optimization. Preimplantation treatment plans generated by the ML algorithm were compared with brachytherapist (BT) treatment plans in terms of planning time (Wilcoxon rank sum, α = 0.05) and dosimetry (1-way analysis of variance, α = 0.05). Qualitative preimplantation plan quality was evaluated by expert LDR radiation oncologists using a Likert scale questionnaire. Results: The average planning time for the ML approach was 0.84 ± 0.57 minutes, compared with 17.88 ± 8.76 minutes for the expert planner (P=.020). Preimplantation plans were dosimetrically equivalent to the BT plans; the average prostate V150% was 4% lower for ML plans (P=.002), although the difference was not clinically significant. Respondents ranked the ML-generated plans as equivalent to expert BT treatment plans in terms of target coverage, normal tissue avoidance, implant confidence, and the need for plan modifications. Respondents had difficulty differentiating between plans generated by a human or those generated by the ML algorithm. Conclusions: Prostate LDR preimplantation treatment plans that have equivalent quality to plans created by brachytherapists can be rapidly generated using ML. The adoption of ML in the brachytherapy workflow is expected to improve LDR treatment plan uniformity while reducing planning time and resources.« less

Software-Based Real-Time Acquisition and Processing of PET Detector Raw Data.

PubMed

Goldschmidt, Benjamin; Schug, David; Lerche, Christoph W; Salomon, André; Gebhardt, Pierre; Weissler, Bjoern; Wehner, Jakob; Dueppenbecker, Peter M; Kiessling, Fabian; Schulz, Volkmar

2016-02-01

In modern positron emission tomography (PET) readout architectures, the position and energy estimation of scintillation events (singles) and the detection of coincident events (coincidences) are typically carried out on highly integrated, programmable printed circuit boards. The implementation of advanced singles and coincidence processing (SCP) algorithms for these architectures is often limited by the strict constraints of hardware-based data processing. In this paper, we present a software-based data acquisition and processing architecture (DAPA) that offers a high degree of flexibility for advanced SCP algorithms through relaxed real-time constraints and an easily extendible data processing framework. The DAPA is designed to acquire detector raw data from independent (but synchronized) detector modules and process the data for singles and coincidences in real-time using a center-of-gravity (COG)-based, a least-squares (LS)-based, or a maximum-likelihood (ML)-based crystal position and energy estimation approach (CPEEA). To test the DAPA, we adapted it to a preclinical PET detector that outputs detector raw data from 60 independent digital silicon photomultiplier (dSiPM)-based detector stacks and evaluated it with a [(18)F]-fluorodeoxyglucose-filled hot-rod phantom. The DAPA is highly reliable with less than 0.1% of all detector raw data lost or corrupted. For high validation thresholds (37.1 ± 12.8 photons per pixel) of the dSiPM detector tiles, the DAPA is real time capable up to 55 MBq for the COG-based CPEEA, up to 31 MBq for the LS-based CPEEA, and up to 28 MBq for the ML-based CPEEA. Compared to the COG-based CPEEA, the rods in the image reconstruction of the hot-rod phantom are only slightly better separable and less blurred for the LS- and ML-based CPEEA. While the coincidence time resolution (∼ 500 ps) and energy resolution (∼12.3%) are comparable for all three CPEEA, the system sensitivity is up to 2.5 × higher for the LS- and ML-based CPEEA.

Volume reconstruction optimization for tomo-PIV algorithms applied to experimental data

NASA Astrophysics Data System (ADS)

Martins, Fabio J. W. A.; Foucaut, Jean-Marc; Thomas, Lionel; Azevedo, Luis F. A.; Stanislas, Michel

2015-08-01

Tomographic PIV is a three-component volumetric velocity measurement technique based on the tomographic reconstruction of a particle distribution imaged by multiple camera views. In essence, the performance and accuracy of this technique is highly dependent on the parametric adjustment and the reconstruction algorithm used. Although synthetic data have been widely employed to optimize experiments, the resulting reconstructed volumes might not have optimal quality. The purpose of the present study is to offer quality indicators that can be applied to data samples in order to improve the quality of velocity results obtained by the tomo-PIV technique. The methodology proposed can potentially lead to significantly reduction in the time required to optimize a tomo-PIV reconstruction, also leading to better quality velocity results. Tomo-PIV data provided by a six-camera turbulent boundary-layer experiment were used to optimize the reconstruction algorithms according to this methodology. Velocity statistics measurements obtained by optimized BIMART, SMART and MART algorithms were compared with hot-wire anemometer data and velocity measurement uncertainties were computed. Results indicated that BIMART and SMART algorithms produced reconstructed volumes with equivalent quality as the standard MART with the benefit of reduced computational time.

Superiorized algorithm for reconstruction of CT images from sparse-view and limited-angle polyenergetic data

NASA Astrophysics Data System (ADS)

Humphries, T.; Winn, J.; Faridani, A.

2017-08-01

Recent work in CT image reconstruction has seen increasing interest in the use of total variation (TV) and related penalties to regularize problems involving reconstruction from undersampled or incomplete data. Superiorization is a recently proposed heuristic which provides an automatic procedure to ‘superiorize’ an iterative image reconstruction algorithm with respect to a chosen objective function, such as TV. Under certain conditions, the superiorized algorithm is guaranteed to find a solution that is as satisfactory as any found by the original algorithm with respect to satisfying the constraints of the problem; this solution is also expected to be superior with respect to the chosen objective. Most work on superiorization has used reconstruction algorithms which assume a linear measurement model, which in the case of CT corresponds to data generated from a monoenergetic x-ray beam. Many CT systems generate x-rays from a polyenergetic spectrum, however, in which the measured data represent an integral of object attenuation over all energies in the spectrum. This inconsistency with the linear model produces the well-known beam hardening artifacts, which impair analysis of CT images. In this work we superiorize an iterative algorithm for reconstruction from polyenergetic data, using both TV and an anisotropic TV (ATV) penalty. We apply the superiorized algorithm in numerical phantom experiments modeling both sparse-view and limited-angle scenarios. In our experiments, the superiorized algorithm successfully finds solutions which are as constraints-compatible as those found by the original algorithm, with significantly reduced TV and ATV values. The superiorized algorithm thus produces images with greatly reduced sparse-view and limited angle artifacts, which are also largely free of the beam hardening artifacts that would be present if a superiorized version of a monoenergetic algorithm were used.

A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT

PubMed Central

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A.; Pan, Xiaochuan

2010-01-01

Purpose: Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. Methods: The proposed approach comprises of two reconstruction steps. In the first step, a chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack–Noo-formula-based filteredbackprojection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. Results: The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. Conclusions: They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories. PMID:20175463

A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT.

PubMed

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A; Pan, Xiaochuan

2010-01-01

Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. The proposed approach comprises of two reconstruction steps. In the first step, a chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack-Noo-formula-based filteredback-projection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories.

GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation.

PubMed

Jia, Xun; Lou, Yifei; Li, Ruijiang; Song, William Y; Jiang, Steve B

2010-04-01

Cone-beam CT (CBCT) plays an important role in image guided radiation therapy (IGRT). However, the large radiation dose from serial CBCT scans in most IGRT procedures raises a clinical concern, especially for pediatric patients who are essentially excluded from receiving IGRT for this reason. The goal of this work is to develop a fast GPU-based algorithm to reconstruct CBCT from undersampled and noisy projection data so as to lower the imaging dose. The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. The authors developed a GPU-friendly version of the forward-backward splitting algorithm to solve this model. A multigrid technique is also employed. It is found that 20-40 x-ray projections are sufficient to reconstruct images with satisfactory quality for IGRT. The reconstruction time ranges from 77 to 130 s on an NVIDIA Tesla C1060 (NVIDIA, Santa Clara, CA) GPU card, depending on the number of projections used, which is estimated about 100 times faster than similar iterative reconstruction approaches. Moreover, phantom studies indicate that the algorithm enables the CBCT to be reconstructed under a scanning protocol with as low as 0.1 mA s/projection. Comparing with currently widely used full-fan head and neck scanning protocol of approximately 360 projections with 0.4 mA s/projection, it is estimated that an overall 36-72 times dose reduction has been achieved in our fast CBCT reconstruction algorithm. This work indicates that the developed GPU-based CBCT reconstruction algorithm is capable of lowering imaging dose considerably. The high computation efficiency in this algorithm makes the iterative CBCT reconstruction approach applicable in real clinical environments.

The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.

PubMed

Cui, Zaixu; Gong, Gaolang

2018-06-02

Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accuracies. However, the effects of the ML regression algorithm and sample size on individualized behavioral/cognitive prediction performance have not been comprehensively assessed. To address this issue, the present study included six commonly used ML regression algorithms: ordinary least squares (OLS) regression, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic-net regression, linear support vector regression (LSVR), and relevance vector regression (RVR), to perform specific behavioral/cognitive predictions based on different sample sizes. Specifically, the publicly available resting-state functional MRI (rs-fMRI) dataset from the Human Connectome Project (HCP) was used, and whole-brain resting-state functional connectivity (rsFC) or rsFC strength (rsFCS) were extracted as prediction features. Twenty-five sample sizes (ranged from 20 to 700) were studied by sub-sampling from the entire HCP cohort. The analyses showed that rsFC-based LASSO regression performed remarkably worse than the other algorithms, and rsFCS-based OLS regression performed markedly worse than the other algorithms. Regardless of the algorithm and feature type, both the prediction accuracy and its stability exponentially increased with increasing sample size. The specific patterns of the observed algorithm and sample size effects were well replicated in the prediction using re-testing fMRI data, data processed by different imaging preprocessing schemes, and different behavioral/cognitive scores, thus indicating excellent robustness/generalization of the effects. The current findings provide critical insight into how the selected ML regression algorithm and sample size influence individualized predictions of behavior/cognition and offer important guidance for choosing the ML regression algorithm or sample size in relevant investigations. Copyright © 2018 Elsevier Inc. All rights reserved.

Speech Enhancement Using Gaussian Scale Mixture Models

PubMed Central

Hao, Jiucang; Lee, Te-Won; Sejnowski, Terrence J.

2011-01-01

This paper presents a novel probabilistic approach to speech enhancement. Instead of a deterministic logarithmic relationship, we assume a probabilistic relationship between the frequency coefficients and the log-spectra. The speech model in the log-spectral domain is a Gaussian mixture model (GMM). The frequency coefficients obey a zero-mean Gaussian whose covariance equals to the exponential of the log-spectra. This results in a Gaussian scale mixture model (GSMM) for the speech signal in the frequency domain, since the log-spectra can be regarded as scaling factors. The probabilistic relation between frequency coefficients and log-spectra allows these to be treated as two random variables, both to be estimated from the noisy signals. Expectation-maximization (EM) was used to train the GSMM and Bayesian inference was used to compute the posterior signal distribution. Because exact inference of this full probabilistic model is computationally intractable, we developed two approaches to enhance the efficiency: the Laplace method and a variational approximation. The proposed methods were applied to enhance speech corrupted by Gaussian noise and speech-shaped noise (SSN). For both approximations, signals reconstructed from the estimated frequency coefficients provided higher signal-to-noise ratio (SNR) and those reconstructed from the estimated log-spectra produced lower word recognition error rate because the log-spectra fit the inputs to the recognizer better. Our algorithms effectively reduced the SSN, which algorithms based on spectral analysis were not able to suppress. PMID:21359139

Optimization of view weighting in tilted-plane-based reconstruction algorithms to minimize helical artifacts in multi-slice helical CT

NASA Astrophysics Data System (ADS)

Tang, Xiangyang

2003-05-01

In multi-slice helical CT, the single-tilted-plane-based reconstruction algorithm has been proposed to combat helical and cone beam artifacts by tilting a reconstruction plane to fit a helical source trajectory optimally. Furthermore, to improve the noise characteristics or dose efficiency of the single-tilted-plane-based reconstruction algorithm, the multi-tilted-plane-based reconstruction algorithm has been proposed, in which the reconstruction plane deviates from the pose globally optimized due to an extra rotation along the 3rd axis. As a result, the capability of suppressing helical and cone beam artifacts in the multi-tilted-plane-based reconstruction algorithm is compromised. An optomized tilted-plane-based reconstruction algorithm is proposed in this paper, in which a matched view weighting strategy is proposed to optimize the capability of suppressing helical and cone beam artifacts and noise characteristics. A helical body phantom is employed to quantitatively evaluate the imaging performance of the matched view weighting approach by tabulating artifact index and noise characteristics, showing that the matched view weighting improves both the helical artifact suppression and noise characteristics or dose efficiency significantly in comparison to the case in which non-matched view weighting is applied. Finally, it is believed that the matched view weighting approach is of practical importance in the development of multi-slive helical CT, because it maintains the computational structure of fan beam filtered backprojection and demands no extra computational services.

Deterministic quantum annealing expectation-maximization algorithm

NASA Astrophysics Data System (ADS)

Miyahara, Hideyuki; Tsumura, Koji; Sughiyama, Yuki

2017-11-01

Maximum likelihood estimation (MLE) is one of the most important methods in machine learning, and the expectation-maximization (EM) algorithm is often used to obtain maximum likelihood estimates. However, EM heavily depends on initial configurations and fails to find the global optimum. On the other hand, in the field of physics, quantum annealing (QA) was proposed as a novel optimization approach. Motivated by QA, we propose a quantum annealing extension of EM, which we call the deterministic quantum annealing expectation-maximization (DQAEM) algorithm. We also discuss its advantage in terms of the path integral formulation. Furthermore, by employing numerical simulations, we illustrate how DQAEM works in MLE and show that DQAEM moderate the problem of local optima in EM.

Ultra-low dose quantitative CT myocardial perfusion imaging with sparse-view dynamic acquisition and image reconstruction: A feasibility study.

PubMed

Enjilela, Esmaeil; Lee, Ting-Yim; Hsieh, Jiang; Wisenberg, Gerald; Teefy, Patrick; Yadegari, Andrew; Bagur, Rodrigo; Islam, Ali; Branch, Kelley; So, Aaron

2018-03-01

We implemented and validated a compressed sensing (CS) based algorithm for reconstructing dynamic contrast-enhanced (DCE) CT images of the heart from sparsely sampled X-ray projections. DCE CT imaging of the heart was performed on five normal and ischemic pigs after contrast injection. DCE images were reconstructed with filtered backprojection (FBP) and CS from all projections (984-view) and 1/3 of all projections (328-view), and with CS from 1/4 of all projections (246-view). Myocardial perfusion (MP) measurements with each protocol were compared to those with the reference 984-view FBP protocol. Both the 984-view CS and 328-view CS protocols were in good agreements with the reference protocol. The Pearson correlation coefficients of 984-view CS and 328-view CS determined from linear regression analyses were 0.98 and 0.99 respectively. The corresponding mean biases of MP measurement determined from Bland-Altman analyses were 2.7 and 1.2ml/min/100g. When only 328 projections were used for image reconstruction, CS was more accurate than FBP for MP measurement with respect to 984-view FBP. However, CS failed to generate MP maps comparable to those with 984-view FBP when only 246 projections were used for image reconstruction. DCE heart images reconstructed from one-third of a full projection set with CS were minimally affected by aliasing artifacts, leading to accurate MP measurements with the effective dose reduced to just 33% of conventional full-view FBP method. The proposed CS sparse-view image reconstruction method could facilitate the implementation of sparse-view dynamic acquisition for ultra-low dose CT MP imaging. Copyright © 2017 Elsevier B.V. All rights reserved.

Plane-dependent ML scatter scaling: 3D extension of the 2D simulated single scatter (SSS) estimate.

PubMed

Rezaei, Ahmadreza; Salvo, Koen; Vahle, Thomas; Panin, Vladimir; Casey, Michael; Boada, Fernando; Defrise, Michel; Nuyts, Johan

2017-07-24

Scatter correction is typically done using a simulation of the single scatter, which is then scaled to account for multiple scatters and other possible model mismatches. This scaling factor is determined by fitting the simulated scatter sinogram to the measured sinogram, using only counts measured along LORs that do not intersect the patient body, i.e. 'scatter-tails'. Extending previous work, we propose to scale the scatter with a plane dependent factor, which is determined as an additional unknown in the maximum likelihood (ML) reconstructions, using counts in the entire sinogram rather than only the 'scatter-tails'. The ML-scaled scatter estimates are validated using a Monte-Carlo simulation of a NEMA-like phantom, a phantom scan with typical contrast ratios of a 68 Ga-PSMA scan, and 23 whole-body 18 F-FDG patient scans. On average, we observe a 12.2% change in the total amount of tracer activity of the MLEM reconstructions of our whole-body patient database when the proposed ML scatter scales are used. Furthermore, reconstructions using the ML-scaled scatter estimates are found to eliminate the typical 'halo' artifacts that are often observed in the vicinity of high focal uptake regions.

Liposomal Bupivacaine for Pain Control After Anterior Cruciate Ligament Reconstruction: A Prospective, Double-Blinded, Randomized, Positive-Controlled Trial.

PubMed

Premkumar, Ajay; Samady, Heather; Slone, Harris; Hash, Regina; Karas, Spero; Xerogeanes, John

2016-07-01

Local anesthetics are commonly administered into surgical sites as a part of multimodal pain control regimens. Liposomal bupivacaine is a novel formulation of bupivacaine designed for slow diffusion of a single dose of local anesthetic over a 72-hour period. While early results are promising in various settings, no studies have compared pain management regimens containing liposomal bupivacaine to traditional regimens in patients undergoing anterior cruciate ligament (ACL) reconstruction. To evaluate liposomal bupivacaine in comparison with 0.25% bupivacaine hydrochloride (HCl) for pain control after ACL reconstruction. Randomized controlled trial; Level of evidence, 1. A total of 32 adult patients undergoing primary ACL reconstruction with a soft tissue quadriceps tendon autograft between July 2014 and March 2015 were enrolled. All patients received a femoral nerve block immediately before surgery. Patients then received either a 40-mL suspension of 20 mL Exparel (1 vial of bupivacaine liposome injectable suspension) and 20 mL 0.9% injectable saline or 20 mL 0.5% bupivacaine HCl and 20 mL 0.9% injectable saline, which was administered into the graft harvest site and portal sites during surgery. Patients were given either a postoperative smartphone application or paper-based journal to record data for 1 week after ACL reconstruction. Of the 32 patients recruited, 29 patients were analyzed (90.6%). Two patients were lost to follow-up, and 1 was excluded because of a postoperative hematoma. There were no statistically significant differences in postoperative pain, medication use, pain location, recovery room time, or mobility between the 2 study groups. There were comparable outcomes with 0.25% bupivacaine HCl at a 200-fold lower cost than liposomal bupivacaine. This study does not support the widespread use of liposomal bupivacaine for pain control after ACL reconstruction in the setting of a femoral nerve block. ClinicalTrials.gov NCT02189317. © 2016 The Author(s).

Solar Energy Innovation Network | Solar Research | NREL

Science.gov Websites

Coordinated Control Algorithms for Distributed Battery Energy Storage Systems to Provide Grid Support Services local governments, nonprofits, innovative companies, and system operators-with analytical support from a Affordability of Renewable Energy through Options Analysis and Systems Design (or "Options Analysis"

Validation of Ionosonde Electron Density Reconstruction Algorithms with IONOLAB-RAY in Central Europe

NASA Astrophysics Data System (ADS)

Gok, Gokhan; Mosna, Zbysek; Arikan, Feza; Arikan, Orhan; Erdem, Esra

2016-07-01

Ionospheric observation is essentially accomplished by specialized radar systems called ionosondes. The time delay between the transmitted and received signals versus frequency is measured by the ionosondes and the received signals are processed to generate ionogram plots, which show the time delay or reflection height of signals with respect to transmitted frequency. The critical frequencies of ionospheric layers and virtual heights, that provide useful information about ionospheric structurecan be extracted from ionograms . Ionograms also indicate the amount of variability or disturbances in the ionosphere. With special inversion algorithms and tomographical methods, electron density profiles can also be estimated from the ionograms. Although structural pictures of ionosphere in the vertical direction can be observed from ionosonde measurements, some errors may arise due to inaccuracies that arise from signal propagation, modeling, data processing and tomographic reconstruction algorithms. Recently IONOLAB group (www.ionolab.org) developed a new algorithm for effective and accurate extraction of ionospheric parameters and reconstruction of electron density profile from ionograms. The electron density reconstruction algorithm applies advanced optimization techniques to calculate parameters of any existing analytical function which defines electron density with respect to height using ionogram measurement data. The process of reconstructing electron density with respect to height is known as the ionogram scaling or true height analysis. IONOLAB-RAY algorithm is a tool to investigate the propagation path and parameters of HF wave in the ionosphere. The algorithm models the wave propagation using ray representation under geometrical optics approximation. In the algorithm , the structural ionospheric characteristics arerepresented as realistically as possible including anisotropicity, inhomogenity and time dependence in 3-D voxel structure. The algorithm is also used for various purposes including calculation of actual height and generation of ionograms. In this study, the performance of electron density reconstruction algorithm of IONOLAB group and standard electron density profile algorithms of ionosondes are compared with IONOLAB-RAY wave propagation simulation in near vertical incidence. The electron density reconstruction and parameter extraction algorithms of ionosondes are validated with the IONOLAB-RAY results both for quiet anddisturbed ionospheric states in Central Europe using ionosonde stations such as Pruhonice and Juliusruh . It is observed that IONOLAB ionosonde parameter extraction and electron density reconstruction algorithm performs significantly better compared to standard algorithms especially for disturbed ionospheric conditions. IONOLAB-RAY provides an efficient and reliable tool to investigate and validate ionosonde electron density reconstruction algorithms, especially in determination of reflection height (true height) of signals and critical parameters of ionosphere. This study is supported by TUBITAK 114E541, 115E915 and Joint TUBITAK 114E092 and AS CR 14/001 projects.

More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server

PubMed Central

Ho, Qirong; Cipar, James; Cui, Henggang; Kim, Jin Kyu; Lee, Seunghak; Gibbons, Phillip B.; Gibson, Garth A.; Ganger, Gregory R.; Xing, Eric P.

2014-01-01

We propose a parameter server system for distributed ML, which follows a Stale Synchronous Parallel (SSP) model of computation that maximizes the time computational workers spend doing useful work on ML algorithms, while still providing correctness guarantees. The parameter server provides an easy-to-use shared interface for read/write access to an ML model’s values (parameters and variables), and the SSP model allows distributed workers to read older, stale versions of these values from a local cache, instead of waiting to get them from a central storage. This significantly increases the proportion of time workers spend computing, as opposed to waiting. Furthermore, the SSP model ensures ML algorithm correctness by limiting the maximum age of the stale values. We provide a proof of correctness under SSP, as well as empirical results demonstrating that the SSP model achieves faster algorithm convergence on several different ML problems, compared to fully-synchronous and asynchronous schemes. PMID:25400488

Tunable output-frequency filter algorithm for imaging through scattering media under LED illumination

NASA Astrophysics Data System (ADS)

Zhou, Meiling; Singh, Alok Kumar; Pedrini, Giancarlo; Osten, Wolfgang; Min, Junwei; Yao, Baoli

2018-03-01

We present a tunable output-frequency filter (TOF) algorithm to reconstruct the object from noisy experimental data under low-power partially coherent illumination, such as LED, when imaging through scattering media. In the iterative algorithm, we employ Gaussian functions with different filter windows at different stages of iteration process to reduce corruption from experimental noise to search for a global minimum in the reconstruction. In comparison with the conventional iterative phase retrieval algorithm, we demonstrate that the proposed TOF algorithm achieves consistent and reliable reconstruction in the presence of experimental noise. Moreover, the spatial resolution and distinctive features are retained in the reconstruction since the filter is applied only to the region outside the object. The feasibility of the proposed method is proved by experimental results.

Three-dimensional dictionary-learning reconstruction of (23)Na MRI data.

PubMed

Behl, Nicolas G R; Gnahm, Christine; Bachert, Peter; Ladd, Mark E; Nagel, Armin M

2016-04-01

To reduce noise and artifacts in (23)Na MRI with a Compressed Sensing reconstruction and a learned dictionary as sparsifying transform. A three-dimensional dictionary-learning compressed sensing reconstruction algorithm (3D-DLCS) for the reconstruction of undersampled 3D radial (23)Na data is presented. The dictionary used as the sparsifying transform is learned with a K-singular-value-decomposition (K-SVD) algorithm. The reconstruction parameters are optimized on simulated data, and the quality of the reconstructions is assessed with peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The performance of the algorithm is evaluated in phantom and in vivo (23)Na MRI data of seven volunteers and compared with nonuniform fast Fourier transform (NUFFT) and other Compressed Sensing reconstructions. The reconstructions of simulated data have maximal PSNR and SSIM for an undersampling factor (USF) of 10 with numbers of averages equal to the USF. For 10-fold undersampling, the PSNR is increased by 5.1 dB compared with the NUFFT reconstruction, and the SSIM by 24%. These results are confirmed by phantom and in vivo (23)Na measurements in the volunteers that show markedly reduced noise and undersampling artifacts in the case of 3D-DLCS reconstructions. The 3D-DLCS algorithm enables precise reconstruction of undersampled (23)Na MRI data with markedly reduced noise and artifact levels compared with NUFFT reconstruction. Small structures are well preserved. © 2015 Wiley Periodicals, Inc.

Hormonal responses to resistance exercise during different menstrual cycle states.

PubMed

Nakamura, Yuki; Aizawa, Katsuji; Imai, Tomoko; Kono, Ichiro; Mesaki, Noboru

2011-06-01

To investigate the effect of menstrual cycle states on ovarian and anabolic hormonal responses to acute resistance exercise in young women. Eight healthy women (eumenorrhea; EM) and eight women with menstrual disorders including oligomenorrhea and amenorrhea (OAM) participated in this study. The EM group performed acute resistance exercises during the early follicular (EF) and midluteal (ML) phases, and the OAM group performed the same exercises. All subjects performed three sets each of lat pull-downs, leg curls, bench presses, leg extensions, and squats at 75%-80% of one-repetition maximum with a 1-min rest between sets. Blood samples were obtained before exercise, immediately after, 30 min after, and 60 min after the exercise. In the EM group, resting serum levels of estradiol and progesterone in the ML phase were higher than those in the EF phase and higher than those in the OAM group. Serum estradiol and progesterone in the ML phase increased after the exercise but did not change in the EF phase or in the OAM group. In contrast, resting levels of testosterone in the OAM group were higher than those in both the ML and EF phases of the EM group. After the exercise, serum growth hormone increased in both the ML and EF phases but did not change in the OAM group. The responses of anabolic hormones to acute resistance exercise are different among the menstrual cycle states in young women. Women with menstrual disturbances with low estradiol and progesterone serum levels have an attenuated anabolic hormone response to acute resistance exercise, suggesting that menstrual disorders accompanying low ovarian hormone levels may affect exercise-induced change in anabolic hormones in women.

Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm*

PubMed Central

Bian, Junguo; Xia, Dan; Sidky, Emil Y; Pan, Xiaochuan

2010-01-01

The back-projection-filtration (BPF) algorithm has been applied to image reconstruction for cone-beam configurations with general source trajectories. The BPF algorithm can reconstruct 3-D region-of-interest (ROI) images from data containing truncations. However, like many other existing algorithms for cone-beam configurations, the BPF algorithm involves a back-projection with a spatially varying weighting factor, which can result in the non-uniform noise levels in reconstructed images and increased computation time. In this work, we propose a BPF algorithm to eliminate the spatially varying weighting factor by using a rebinned geometry for a general scanning trajectory. This proposed BPF algorithm has an improved noise property, while retaining the advantages of the original BPF algorithm such as minimum data requirement. PMID:20617122

Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm.

PubMed

Bian, Junguo; Xia, Dan; Sidky, Emil Y; Pan, Xiaochuan

2010-02-01

The back-projection-filtration (BPF) algorithm has been applied to image reconstruction for cone-beam configurations with general source trajectories. The BPF algorithm can reconstruct 3-D region-of-interest (ROI) images from data containing truncations. However, like many other existing algorithms for cone-beam configurations, the BPF algorithm involves a back-projection with a spatially varying weighting factor, which can result in the non-uniform noise levels in reconstructed images and increased computation time. In this work, we propose a BPF algorithm to eliminate the spatially varying weighting factor by using a rebinned geometry for a general scanning trajectory. This proposed BPF algorithm has an improved noise property, while retaining the advantages of the original BPF algorithm such as minimum data requirement.

Objective performance assessment of five computed tomography iterative reconstruction algorithms.

PubMed

Omotayo, Azeez; Elbakri, Idris

2016-11-22

Iterative algorithms are gaining clinical acceptance in CT. We performed objective phantom-based image quality evaluation of five commercial iterative reconstruction algorithms available on four different multi-detector CT (MDCT) scanners at different dose levels as well as the conventional filtered back-projection (FBP) reconstruction. Using the Catphan500 phantom, we evaluated image noise, contrast-to-noise ratio (CNR), modulation transfer function (MTF) and noise-power spectrum (NPS). The algorithms were evaluated over a CTDIvol range of 0.75-18.7 mGy on four major MDCT scanners: GE DiscoveryCT750HD (algorithms: ASIR™ and VEO™); Siemens Somatom Definition AS+ (algorithm: SAFIRE™); Toshiba Aquilion64 (algorithm: AIDR3D™); and Philips Ingenuity iCT256 (algorithm: iDose4™). Images were reconstructed using FBP and the respective iterative algorithms on the four scanners. Use of iterative algorithms decreased image noise and increased CNR, relative to FBP. In the dose range of 1.3-1.5 mGy, noise reduction using iterative algorithms was in the range of 11%-51% on GE DiscoveryCT750HD, 10%-52% on Siemens Somatom Definition AS+, 49%-62% on Toshiba Aquilion64, and 13%-44% on Philips Ingenuity iCT256. The corresponding CNR increase was in the range 11%-105% on GE, 11%-106% on Siemens, 85%-145% on Toshiba and 13%-77% on Philips respectively. Most algorithms did not affect the MTF, except for VEO™ which produced an increase in the limiting resolution of up to 30%. A shift in the peak of the NPS curve towards lower frequencies and a decrease in NPS amplitude were obtained with all iterative algorithms. VEO™ required long reconstruction times, while all other algorithms produced reconstructions in real time. Compared to FBP, iterative algorithms reduced image noise and increased CNR. The iterative algorithms available on different scanners achieved different levels of noise reduction and CNR increase while spatial resolution improvements were obtained only with VEO™. This study is useful in that it provides performance assessment of the iterative algorithms available from several mainstream CT manufacturers.

PI-line-based image reconstruction in helical cone-beam computed tomography with a variable pitch.

PubMed

Zou, Yu; Pan, Xiaochuan; Xia, Dan; Wang, Ge

2005-08-01

Current applications of helical cone-beam computed tomography (CT) involve primarily a constant pitch where the translating speed of the table and the rotation speed of the source-detector remain constant. However, situations do exist where it may be more desirable to use a helical scan with a variable translating speed of the table, leading a variable pitch. One of such applications could arise in helical cone-beam CT fluoroscopy for the determination of vascular structures through real-time imaging of contrast bolus arrival. Most of the existing reconstruction algorithms have been developed only for helical cone-beam CT with constant pitch, including the backprojection-filtration (BPF) and filtered-backprojection (FBP) algorithms that we proposed previously. It is possible to generalize some of these algorithms to reconstruct images exactly for helical cone-beam CT with a variable pitch. In this work, we generalize our BPF and FBP algorithms to reconstruct images directly from data acquired in helical cone-beam CT with a variable pitch. We have also performed a preliminary numerical study to demonstrate and verify the generalization of the two algorithms. The results of the study confirm that our generalized BPF and FBP algorithms can yield exact reconstruction in helical cone-beam CT with a variable pitch. It should be pointed out that our generalized BPF algorithm is the only algorithm that is capable of reconstructing exactly region-of-interest image from data containing transverse truncations.

A Novel Image Compression Algorithm for High Resolution 3D Reconstruction

NASA Astrophysics Data System (ADS)

Siddeq, M. M.; Rodrigues, M. A.

2014-06-01

This research presents a novel algorithm to compress high-resolution images for accurate structured light 3D reconstruction. Structured light images contain a pattern of light and shadows projected on the surface of the object, which are captured by the sensor at very high resolutions. Our algorithm is concerned with compressing such images to a high degree with minimum loss without adversely affecting 3D reconstruction. The Compression Algorithm starts with a single level discrete wavelet transform (DWT) for decomposing an image into four sub-bands. The sub-band LL is transformed by DCT yielding a DC-matrix and an AC-matrix. The Minimize-Matrix-Size Algorithm is used to compress the AC-matrix while a DWT is applied again to the DC-matrix resulting in LL2, HL2, LH2 and HH2 sub-bands. The LL2 sub-band is transformed by DCT, while the Minimize-Matrix-Size Algorithm is applied to the other sub-bands. The proposed algorithm has been tested with images of different sizes within a 3D reconstruction scenario. The algorithm is demonstrated to be more effective than JPEG2000 and JPEG concerning higher compression rates with equivalent perceived quality and the ability to more accurately reconstruct the 3D models.

Region-of-interest image reconstruction in circular cone-beam microCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cho, Seungryong; Bian, Junguo; Pelizzari, Charles A.

2007-12-15

Cone-beam microcomputed tomography (microCT) is one of the most popular choices for small animal imaging which is becoming an important tool for studying animal models with transplanted diseases. Region-of-interest (ROI) imaging techniques in CT, which can reconstruct an ROI image from the projection data set of the ROI, can be used not only for reducing imaging-radiation exposure to the subject and scatters to the detector but also for potentially increasing spatial resolution of the reconstructed images. Increasing spatial resolution in microCT images can facilitate improved accuracy in many assessment tasks. A method proposed previously for increasing CT image spatial resolutionmore » entails the exploitation of the geometric magnification in cone-beam CT. Due to finite detector size, however, this method can lead to data truncation for a large geometric magnification. The Feldkamp-Davis-Kress (FDK) algorithm yields images with artifacts when truncated data are used, whereas the recently developed backprojection filtration (BPF) algorithm is capable of reconstructing ROI images without truncation artifacts from truncated cone-beam data. We apply the BPF algorithm to reconstructing ROI images from truncated data of three different objects acquired by our circular cone-beam microCT system. Reconstructed images by use of the FDK and BPF algorithms from both truncated and nontruncated cone-beam data are compared. The results of the experimental studies demonstrate that, from certain truncated data, the BPF algorithm can reconstruct ROI images with quality comparable to that reconstructed from nontruncated data. In contrast, the FDK algorithm yields ROI images with truncation artifacts. Therefore, an implication of the studies is that, when truncated data are acquired with a configuration of a large geometric magnification, the BPF algorithm can be used for effective enhancement of the spatial resolution of a ROI image.« less

A high-resolution core-level photoemission study of the Au/4H-SiC(0001)-([Formula: see text]) interface.

PubMed

Stoltz, D; Stoltz, S E; Johansson, L S O

2007-07-04

We present a systematic study of different reconstructions obtained after deposition of Au on the [Formula: see text]-4H-SiC(0001) surface. For 1-2 monolayers (ML) Au and annealing temperature T(anneal)∼675 °C, a 3 × 3 reconstruction was observed. For 4 ML Au and T(anneal)∼650 °C, a [Formula: see text] reconstruction appeared, while 5 ML Au annealed at 700 °C reconstructed to give a [Formula: see text] pattern. From the Si 2p and Au 4f core-level components, we propose interface models, depending on the amount of Au on the surface and the annealing temperature. For 1-4 ML Au annealed at 650-675 °C, gold diffuses under the topmost Si into the SiC and forms a silicide. An additional Si component in our Si 2p spectra is related to the interface between the silicide and SiC. For 5 ML Au annealed at 700 °C, silicide is also formed at the surface, covering unreacted Au on top of the SiC substrate. The interface Si component is also observed in the Si 2p spectra of this surface. The key role in [Formula: see text]-4H-SiC(0001) interface formation is played by diffusion and the silicon-richness of the surface.

Incomplete projection reconstruction of computed tomography based on the modified discrete algebraic reconstruction technique

NASA Astrophysics Data System (ADS)

Yang, Fuqiang; Zhang, Dinghua; Huang, Kuidong; Gao, Zongzhao; Yang, YaFei

2018-02-01

Based on the discrete algebraic reconstruction technique (DART), this study aims to address and test a new improved algorithm applied to incomplete projection data to generate a high quality reconstruction image by reducing the artifacts and noise in computed tomography. For the incomplete projections, an augmented Lagrangian based on compressed sensing is first used in the initial reconstruction for segmentation of the DART to get higher contrast graphics for boundary and non-boundary pixels. Then, the block matching 3D filtering operator was used to suppress the noise and to improve the gray distribution of the reconstructed image. Finally, simulation studies on the polychromatic spectrum were performed to test the performance of the new algorithm. Study results show a significant improvement in the signal-to-noise ratios (SNRs) and average gradients (AGs) of the images reconstructed from incomplete data. The SNRs and AGs of the new images reconstructed by DART-ALBM were on average 30%-40% and 10% higher than the images reconstructed by DART algorithms. Since the improved DART-ALBM algorithm has a better robustness to limited-view reconstruction, which not only makes the edge of the image clear but also makes the gray distribution of non-boundary pixels better, it has the potential to improve image quality from incomplete projections or sparse projections.

Convex Accelerated Maximum Entropy Reconstruction

PubMed Central

Worley, Bradley

2016-01-01

Maximum entropy (MaxEnt) spectral reconstruction methods provide a powerful framework for spectral estimation of nonuniformly sampled datasets. Many methods exist within this framework, usually defined based on the magnitude of a Lagrange multiplier in the MaxEnt objective function. An algorithm is presented here that utilizes accelerated first-order convex optimization techniques to rapidly and reliably reconstruct nonuniformly sampled NMR datasets using the principle of maximum entropy. This algorithm – called CAMERA for Convex Accelerated Maximum Entropy Reconstruction Algorithm – is a new approach to spectral reconstruction that exhibits fast, tunable convergence in both constant-aim and constant-lambda modes. A high-performance, open source NMR data processing tool is described that implements CAMERA, and brief comparisons to existing reconstruction methods are made on several example spectra. PMID:26894476

Cultural Psychology of Differences and EMS; a New Theoretical Framework for Understanding and Reconstructing Culture.

PubMed

Yamamoto, Toshiya

2017-09-01

In this paper I introduce the outlines of our new type of theoretical framework named 'Cultural psychology of Differences' for understanding cultural others and dialogically reconstructing interactions among cultural others. In order to understand cultural others, it is necessary for us to reconstruct a new concept which enables us to analyze dynamic generation processes of culture. We propose the concept of Expanded Mediational Structure, EMS, as an elementary unit for understanding human social interactions. EMS is composed of subjects who interacts each other using objects of some kind as mediators, and a normative mediator, NM, which mediates their interactions. It is necessary to generate, share and adjust a NM to keep social interactions stable, and culture will appear when interaction malfunction is attributed to a gaps of NMs. The concept of EMS helps us to understand how culture is functionally substantialized in the plane of collective (or communal) intersubjectivity and how cultural conflicts develop and intensify. Focusing on the generation process of culture through interactions provides us with another option to understand cultural others through dialogical interactions with them.

Axial 3D region of interest reconstruction using weighted cone beam BPF/DBPF algorithm cascaded with adequately oriented orthogonal butterfly filtering

NASA Astrophysics Data System (ADS)

Tang, Shaojie; Tang, Xiangyang

2016-03-01

Axial cone beam (CB) computed tomography (CT) reconstruction is still the most desirable in clinical applications. As the potential candidates with analytic form for the task, the back projection-filtration (BPF) and the derivative backprojection filtered (DBPF) algorithms, in which Hilbert filtering is the common algorithmic feature, are originally derived for exact helical and axial reconstruction from CB and fan beam projection data, respectively. These two algorithms have been heuristically extended for axial CB reconstruction via adoption of virtual PI-line segments. Unfortunately, however, streak artifacts are induced along the Hilbert filtering direction, since these algorithms are no longer accurate on the virtual PI-line segments. We have proposed to cascade the extended BPF/DBPF algorithm with orthogonal butterfly filtering for image reconstruction (namely axial CB-BPP/DBPF cascaded with orthogonal butterfly filtering), in which the orientation-specific artifacts caused by post-BP Hilbert transform can be eliminated, at a possible expense of losing the BPF/DBPF's capability of dealing with projection data truncation. Our preliminary results have shown that this is not the case in practice. Hence, in this work, we carry out an algorithmic analysis and experimental study to investigate the performance of the axial CB-BPP/DBPF cascaded with adequately oriented orthogonal butterfly filtering for three-dimensional (3D) reconstruction in region of interest (ROI).

Short-term reproducibility of computed tomography-based lung density measurements in alpha-1 antitrypsin deficiency and smokers with emphysema.

PubMed

Shaker, S B; Dirksen, A; Laursen, L C; Maltbaek, N; Christensen, L; Sander, U; Seersholm, N; Skovgaard, L T; Nielsen, L; Kok-Jensen, A

2004-07-01

To study the short-term reproducibility of lung density measurements by multi-slice computed tomography (CT) using three different radiation doses and three reconstruction algorithms. Twenty-five patients with smoker's emphysema and 25 patients with alpha1-antitrypsin deficiency underwent 3 scans at 2-week intervals. Low-dose protocol was applied, and images were reconstructed with bone, detail, and soft algorithms. Total lung volume (TLV), 15th percentile density (PD-15), and relative area at -910 Hounsfield units (RA-910) were obtained from the images using Pulmo-CMS software. Reproducibility of PD-15 and RA-910 and the influence of radiation dose, reconstruction algorithm, and type of emphysema were then analysed. The overall coefficient of variation of volume adjusted PD-15 for all combinations of radiation dose and reconstruction algorithm was 3.7%. The overall standard deviation of volume-adjusted RA-910 was 1.7% (corresponding to a coefficient of variation of 6.8%). Radiation dose, reconstruction algorithm, and type of emphysema had no significant influence on the reproducibility of PD-15 and RA-910. However, bone algorithm and very low radiation dose result in overestimation of the extent of emphysema. Lung density measurement by CT is a sensitive marker for quantitating both subtypes of emphysema. A CT-protocol with radiation dose down to 16 mAs and soft or detail reconstruction algorithm is recommended.

DIY Solar Market Analysis Webinar Series: PVWatts® | State, Local, and

Science.gov Websites

, and updates the energy prediction algorithms to be in line with the actual performance of modern the latest update." In this webinar, one of the tool's developers explains how the new version of ® Wednesday, July 9, 2014 As part of a Do-It-Yourself Solar Market Analysis summer series, NREL's Solar

Exact BPF and FBP algorithms for nonstandard saddle curves.

PubMed

Yu, Hengyong; Zhao, Shiying; Ye, Yangbo; Wang, Ge

2005-11-01

A hot topic in cone-beam CT research is exact cone-beam reconstruction from a general scanning trajectory. Particularly, a nonstandard saddle curve attracts attention, as this construct allows the continuous periodic scanning of a volume-of-interest (VOI). Here we evaluate two algorithms for reconstruction from data collected along a nonstandard saddle curve, which are in the filtered backprojection (FBP) and backprojection filtration (BPF) formats, respectively. Both the algorithms are implemented in a chord-based coordinate system. Then, a rebinning procedure is utilized to transform the reconstructed results into the natural coordinate system. The simulation results demonstrate that the FBP algorithm produces better image quality than the BPF algorithm, while both the algorithms exhibit similar noise characteristics.

Use of the Hotelling observer to optimize image reconstruction in digital breast tomosynthesis

PubMed Central

Sánchez, Adrian A.; Sidky, Emil Y.; Pan, Xiaochuan

2015-01-01

Abstract. We propose an implementation of the Hotelling observer that can be applied to the optimization of linear image reconstruction algorithms in digital breast tomosynthesis. The method is based on considering information within a specific region of interest, and it is applied to the optimization of algorithms for detectability of microcalcifications. Several linear algorithms are considered: simple back-projection, filtered back-projection, back-projection filtration, and Λ-tomography. The optimized algorithms are then evaluated through the reconstruction of phantom data. The method appears robust across algorithms and parameters and leads to the generation of algorithm implementations which subjectively appear optimized for the task of interest. PMID:26702408

EM algorithm applied for estimating non-stationary region boundaries using electrical impedance tomography

NASA Astrophysics Data System (ADS)

Khambampati, A. K.; Rashid, A.; Kim, B. S.; Liu, Dong; Kim, S.; Kim, K. Y.

2010-04-01

EIT has been used for the dynamic estimation of organ boundaries. One specific application in this context is the estimation of lung boundaries during pulmonary circulation. This would help track the size and shape of lungs of the patients suffering from diseases like pulmonary edema and acute respiratory failure (ARF). The dynamic boundary estimation of the lungs can also be utilized to set and control the air volume and pressure delivered to the patients during artificial ventilation. In this paper, the expectation-maximization (EM) algorithm is used as an inverse algorithm to estimate the non-stationary lung boundary. The uncertainties caused in Kalman-type filters due to inaccurate selection of model parameters are overcome using EM algorithm. Numerical experiments using chest shaped geometry are carried out with proposed method and the performance is compared with extended Kalman filter (EKF). Results show superior performance of EM in estimation of the lung boundary.

Michael F. Crowley | NREL

Science.gov Websites

architectures. Crowlely's group has designed and implemented new methods and algorithms specifically for biomass , Crowley developed highly parallel methods for simulations of bio-macromolecules. Affiliated Research advanced sampling methods, Crowley and his team determine free energies such as binding of substrates

Materials Discovery | Photovoltaic Research | NREL

Science.gov Websites

and specialized analysis algorithms. The Center for Next Generation of Materials by Design (CNGMD) is , incorporating metastable materials into predictive design, and developing theory to guide materials synthesis design, accuracy and relevance, metastability, and synthesizability-to make computational materials

Formulation and implementation of nonstationary adaptive estimation algorithm with applications to air-data reconstruction

NASA Technical Reports Server (NTRS)

Whitmore, S. A.

1985-01-01

The dynamics model and data sources used to perform air-data reconstruction are discussed, as well as the Kalman filter. The need for adaptive determination of the noise statistics of the process is indicated. The filter innovations are presented as a means of developing the adaptive criterion, which is based on the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of the filter innovations is presented. The algorithm as developed is applied to air-data reconstruction for the space shuttle, and data obtained from the third landing are presented. To verify the performance of the adaptive algorithm, the reconstruction is also performed using a constant covariance Kalman filter. The results of the reconstructions are compared, and the adaptive algorithm exhibits better performance.

Explicit Filtering Based Low-Dose Differential Phase Reconstruction Algorithm with the Grating Interferometry.

PubMed

Jiang, Xiaolei; Zhang, Li; Zhang, Ran; Yin, Hongxia; Wang, Zhenchang

2015-01-01

X-ray grating interferometry offers a novel framework for the study of weakly absorbing samples. Three kinds of information, that is, the attenuation, differential phase contrast (DPC), and dark-field images, can be obtained after a single scanning, providing additional and complementary information to the conventional attenuation image. Phase shifts of X-rays are measured by the DPC method; hence, DPC-CT reconstructs refraction indexes rather than attenuation coefficients. In this work, we propose an explicit filtering based low-dose differential phase reconstruction algorithm, which enables reconstruction from reduced scanning without artifacts. The algorithm adopts a differential algebraic reconstruction technique (DART) with the explicit filtering based sparse regularization rather than the commonly used total variation (TV) method. Both the numerical simulation and the biological sample experiment demonstrate the feasibility of the proposed algorithm.

Explicit Filtering Based Low-Dose Differential Phase Reconstruction Algorithm with the Grating Interferometry

PubMed Central

Zhang, Li; Zhang, Ran; Yin, Hongxia; Wang, Zhenchang

2015-01-01

X-ray grating interferometry offers a novel framework for the study of weakly absorbing samples. Three kinds of information, that is, the attenuation, differential phase contrast (DPC), and dark-field images, can be obtained after a single scanning, providing additional and complementary information to the conventional attenuation image. Phase shifts of X-rays are measured by the DPC method; hence, DPC-CT reconstructs refraction indexes rather than attenuation coefficients. In this work, we propose an explicit filtering based low-dose differential phase reconstruction algorithm, which enables reconstruction from reduced scanning without artifacts. The algorithm adopts a differential algebraic reconstruction technique (DART) with the explicit filtering based sparse regularization rather than the commonly used total variation (TV) method. Both the numerical simulation and the biological sample experiment demonstrate the feasibility of the proposed algorithm. PMID:26089971

Materials Discovery | Materials Science | NREL

Science.gov Websites

measurement methods and specialized analysis algorithms. Projects Basic Research The basic research projects applications using high-throughput combinatorial research methods. Email | 303-384-6467 Photo of John Perkins

WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bai, T; UT Southwestern Medical Center, Dallas, TX; Yan, H

2014-06-15

Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm inmore » a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application. A high zresolution is preferred to stabilize statistical iterative reconstruction. This work was supported in part by NIH(1R01CA154747-01), NSFC((No. 61172163), Research Fund for the Doctoral Program of Higher Education of China (No. 20110201110011), China Scholarship Council.« less

Staff | Computational Science | NREL

Science.gov Websites

develops and leads laboratory-wide efforts in high-performance computing and energy-efficient data centers Professional IV-High Perf Computing Jim.Albin@nrel.gov 303-275-4069 Ananthan, Shreyas Senior Scientist - High -Performance Algorithms and Modeling Shreyas.Ananthan@nrel.gov 303-275-4807 Bendl, Kurt IT Professional IV-High

Power Systems Design and Studies | Grid Modernization | NREL

Science.gov Websites

Design and Studies Power Systems Design and Studies NREL develops new tools, algorithms, and market design and performance evaluations; and planning, operations, and protection studies. Photo of two researchers looking at a screen showing a distribution grid map Current design and planning tools for the

Performance improvements in temperature reconstructions of 2-D tunable diode laser absorption spectroscopy (TDLAS)

NASA Astrophysics Data System (ADS)

Choi, Doo-Won; Jeon, Min-Gyu; Cho, Gyeong-Rae; Kamimoto, Takahiro; Deguchi, Yoshihiro; Doh, Deog-Hee

2016-02-01

Performance improvement was attained in data reconstructions of 2-dimensional tunable diode laser absorption spectroscopy (TDLAS). Multiplicative Algebraic Reconstruction Technique (MART) algorithm was adopted for data reconstruction. The data obtained in an experiment for the measurement of temperature and concentration fields of gas flows were used. The measurement theory is based upon the Beer-Lambert law, and the measurement system consists of a tunable laser, collimators, detectors, and an analyzer. Methane was used as a fuel for combustion with air in the Bunsen-type burner. The data used for the reconstruction are from the optical signals of 8-laser beams passed on a cross-section of the methane flame. The performances of MART algorithm in data reconstruction were validated and compared with those obtained by Algebraic Reconstruction Technique (ART) algorithm.

A multiresolution approach to iterative reconstruction algorithms in X-ray computed tomography.

PubMed

De Witte, Yoni; Vlassenbroeck, Jelle; Van Hoorebeke, Luc

2010-09-01

In computed tomography, the application of iterative reconstruction methods in practical situations is impeded by their high computational demands. Especially in high resolution X-ray computed tomography, where reconstruction volumes contain a high number of volume elements (several giga voxels), this computational burden prevents their actual breakthrough. Besides the large amount of calculations, iterative algorithms require the entire volume to be kept in memory during reconstruction, which quickly becomes cumbersome for large data sets. To overcome this obstacle, we present a novel multiresolution reconstruction, which greatly reduces the required amount of memory without significantly affecting the reconstructed image quality. It is shown that, combined with an efficient implementation on a graphical processing unit, the multiresolution approach enables the application of iterative algorithms in the reconstruction of large volumes at an acceptable speed using only limited resources.

An Expectation-Maximization Algorithm for Amplitude Estimation of Saturated Optical Transient Signals.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kagie, Matthew J.; Lanterman, Aaron D.

2017-12-01

This paper addresses parameter estimation for an optical transient signal when the received data has been right-censored. We develop an expectation-maximization (EM) algorithm to estimate the amplitude of a Poisson intensity with a known shape in the presence of additive background counts, where the measurements are subject to saturation effects. We compare the results of our algorithm with those of an EM algorithm that is unaware of the censoring.

Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants.

PubMed

Navarro, Pedro J; Pérez, Fernando; Weiss, Julia; Egea-Cortines, Marcos

2016-05-05

Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We present a system based on machine learning (ML) algorithms and computer vision intended to solve the automatic phenotype data analysis in plant material. We developed a growth-chamber able to accommodate species of various sizes. Night image acquisition requires near infrared lightning. For the ML process, we tested three different algorithms: k-nearest neighbour (kNN), Naive Bayes Classifier (NBC), and Support Vector Machine. Each ML algorithm was executed with different kernel functions and they were trained with raw data and two types of data normalisation. Different metrics were computed to determine the optimal configuration of the machine learning algorithms. We obtained a performance of 99.31% in kNN for RGB images and a 99.34% in SVM for NIR. Our results show that ML techniques can speed up phenomic data analysis. Furthermore, both RGB and NIR images can be segmented successfully but may require different ML algorithms for segmentation.

Ill-posed problem and regularization in reconstruction of radiobiological parameters from serial tumor imaging data

NASA Astrophysics Data System (ADS)

Chvetsov, Alevei V.; Sandison, George A.; Schwartz, Jeffrey L.; Rengan, Ramesh

2015-11-01

The main objective of this article is to improve the stability of reconstruction algorithms for estimation of radiobiological parameters using serial tumor imaging data acquired during radiation therapy. Serial images of tumor response to radiation therapy represent a complex summation of several exponential processes as treatment induced cell inactivation, tumor growth rates, and the rate of cell loss. Accurate assessment of treatment response would require separation of these processes because they define radiobiological determinants of treatment response and, correspondingly, tumor control probability. However, the estimation of radiobiological parameters using imaging data can be considered an inverse ill-posed problem because a sum of several exponentials would produce the Fredholm integral equation of the first kind which is ill posed. Therefore, the stability of reconstruction of radiobiological parameters presents a problem even for the simplest models of tumor response. To study stability of the parameter reconstruction problem, we used a set of serial CT imaging data for head and neck cancer and a simplest case of a two-level cell population model of tumor response. Inverse reconstruction was performed using a simulated annealing algorithm to minimize a least squared objective function. Results show that the reconstructed values of cell surviving fractions and cell doubling time exhibit significant nonphysical fluctuations if no stabilization algorithms are applied. However, after applying a stabilization algorithm based on variational regularization, the reconstruction produces statistical distributions for survival fractions and doubling time that are comparable to published in vitro data. This algorithm is an advance over our previous work where only cell surviving fractions were reconstructed. We conclude that variational regularization allows for an increase in the number of free parameters in our model which enables development of more-advanced parameter reconstruction algorithms.

Regularization Parameter Selection for Nonlinear Iterative Image Restoration and MRI Reconstruction Using GCV and SURE-Based Methods

PubMed Central

Ramani, Sathish; Liu, Zhihao; Rosen, Jeffrey; Nielsen, Jon-Fredrik; Fessler, Jeffrey A.

2012-01-01

Regularized iterative reconstruction algorithms for imaging inverse problems require selection of appropriate regularization parameter values. We focus on the challenging problem of tuning regularization parameters for nonlinear algorithms for the case of additive (possibly complex) Gaussian noise. Generalized cross-validation (GCV) and (weighted) mean-squared error (MSE) approaches (based on Stein's Unbiased Risk Estimate— SURE) need the Jacobian matrix of the nonlinear reconstruction operator (representative of the iterative algorithm) with respect to the data. We derive the desired Jacobian matrix for two types of nonlinear iterative algorithms: a fast variant of the standard iterative reweighted least-squares method and the contemporary split-Bregman algorithm, both of which can accommodate a wide variety of analysis- and synthesis-type regularizers. The proposed approach iteratively computes two weighted SURE-type measures: Predicted-SURE and Projected-SURE (that require knowledge of noise variance σ2), and GCV (that does not need σ2) for these algorithms. We apply the methods to image restoration and to magnetic resonance image (MRI) reconstruction using total variation (TV) and an analysis-type ℓ1-regularization. We demonstrate through simulations and experiments with real data that minimizing Predicted-SURE and Projected-SURE consistently lead to near-MSE-optimal reconstructions. We also observed that minimizing GCV yields reconstruction results that are near-MSE-optimal for image restoration and slightly sub-optimal for MRI. Theoretical derivations in this work related to Jacobian matrix evaluations can be extended, in principle, to other types of regularizers and reconstruction algorithms. PMID:22531764

Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction.

PubMed

Huang, Hsuan-Ming; Hsiao, Ing-Tsung

2017-01-01

Over the past decade, image quality in low-dose computed tomography has been greatly improved by various compressive sensing- (CS-) based reconstruction methods. However, these methods have some disadvantages including high computational cost and slow convergence rate. Many different speed-up techniques for CS-based reconstruction algorithms have been developed. The purpose of this paper is to propose a fast reconstruction framework that combines a CS-based reconstruction algorithm with several speed-up techniques. First, total difference minimization (TDM) was implemented using the soft-threshold filtering (STF). Second, we combined TDM-STF with the ordered subsets transmission (OSTR) algorithm for accelerating the convergence. To further speed up the convergence of the proposed method, we applied the power factor and the fast iterative shrinkage thresholding algorithm to OSTR and TDM-STF, respectively. Results obtained from simulation and phantom studies showed that many speed-up techniques could be combined to greatly improve the convergence speed of a CS-based reconstruction algorithm. More importantly, the increased computation time (≤10%) was minor as compared to the acceleration provided by the proposed method. In this paper, we have presented a CS-based reconstruction framework that combines several acceleration techniques. Both simulation and phantom studies provide evidence that the proposed method has the potential to satisfy the requirement of fast image reconstruction in practical CT.

Estimation of multiple sound sources with data and model uncertainties using the EM and evidential EM algorithms

NASA Astrophysics Data System (ADS)

Wang, Xun; Quost, Benjamin; Chazot, Jean-Daniel; Antoni, Jérôme

2016-01-01

This paper considers the problem of identifying multiple sound sources from acoustical measurements obtained by an array of microphones. The problem is solved via maximum likelihood. In particular, an expectation-maximization (EM) approach is used to estimate the sound source locations and strengths, the pressure measured by a microphone being interpreted as a mixture of latent signals emitted by the sources. This work also considers two kinds of uncertainties pervading the sound propagation and measurement process: uncertain microphone locations and uncertain wavenumber. These uncertainties are transposed to the data in the belief functions framework. Then, the source locations and strengths can be estimated using a variant of the EM algorithm, known as the Evidential EM (E2M) algorithm. Eventually, both simulation and real experiments are shown to illustrate the advantage of using the EM in the case without uncertainty and the E2M in the case of uncertain measurement.

Comparison of SeaWinds Backscatter Imaging Algorithms

PubMed Central

Long, David G.

2017-01-01

This paper compares the performance and tradeoffs of various backscatter imaging algorithms for the SeaWinds scatterometer when multiple passes over a target are available. Reconstruction methods are compared with conventional gridding algorithms. In particular, the performance and tradeoffs in conventional ‘drop in the bucket’ (DIB) gridding at the intrinsic sensor resolution are compared to high-spatial-resolution imaging algorithms such as fine-resolution DIB and the scatterometer image reconstruction (SIR) that generate enhanced-resolution backscatter images. Various options for each algorithm are explored, including considering both linear and dB computation. The effects of sampling density and reconstruction quality versus time are explored. Both simulated and actual data results are considered. The results demonstrate the effectiveness of high-resolution reconstruction using SIR as well as its limitations and the limitations of DIB and fDIB. PMID:28828143

A modified sparse reconstruction method for three-dimensional synthetic aperture radar image

NASA Astrophysics Data System (ADS)

Zhang, Ziqiang; Ji, Kefeng; Song, Haibo; Zou, Huanxin

2018-03-01

There is an increasing interest in three-dimensional Synthetic Aperture Radar (3-D SAR) imaging from observed sparse scattering data. However, the existing 3-D sparse imaging method requires large computing times and storage capacity. In this paper, we propose a modified method for the sparse 3-D SAR imaging. The method processes the collection of noisy SAR measurements, usually collected over nonlinear flight paths, and outputs 3-D SAR imagery. Firstly, the 3-D sparse reconstruction problem is transformed into a series of 2-D slices reconstruction problem by range compression. Then the slices are reconstructed by the modified SL0 (smoothed l0 norm) reconstruction algorithm. The improved algorithm uses hyperbolic tangent function instead of the Gaussian function to approximate the l0 norm and uses the Newton direction instead of the steepest descent direction, which can speed up the convergence rate of the SL0 algorithm. Finally, numerical simulation results are given to demonstrate the effectiveness of the proposed algorithm. It is shown that our method, compared with existing 3-D sparse imaging method, performs better in reconstruction quality and the reconstruction time.

Preoperative implant selection for unilateral breast reconstruction using 3D imaging with the Microsoft Kinect sensor.

PubMed

Pöhlmann, Stefanie T L; Harkness, Elaine; Taylor, Christopher J; Gandhi, Ashu; Astley, Susan M

2017-08-01

This study aimed to investigate whether breast volume measured preoperatively using a Kinect 3D sensor could be used to determine the most appropriate implant size for reconstruction. Ten patients underwent 3D imaging before and after unilateral implant-based reconstruction. Imaging used seven configurations, varying patient pose and Kinect location, which were compared regarding suitability for volume measurement. Four methods of defining the breast boundary for automated volume calculation were compared, and repeatability assessed over five repetitions. The most repeatable breast boundary annotation used an ellipse to track the inframammary fold and a plane describing the chest wall (coefficient of repeatability: 70 ml). The most reproducible imaging position comparing pre- and postoperative volume measurement of the healthy breast was achieved for the sitting patient with elevated arms and Kinect centrally positioned (coefficient of repeatability: 141 ml). Optimal implant volume was calculated by correcting used implant volume by the observed postoperative asymmetry. It was possible to predict implant size using a linear model derived from preoperative volume measurement of the healthy breast (coefficient of determination R 2  = 0.78, standard error of prediction 120 ml). Mastectomy specimen weight and experienced surgeons' choice showed similar predictive ability (both: R 2  = 0.74, standard error: 141/142 ml). A leave one-out validation showed that in 61% of cases, 3D imaging could predict implant volume to within 10%; however for 17% of cases it was >30%. This technology has the potential to facilitate reconstruction surgery planning and implant procurement to maximise symmetry after unilateral reconstruction. Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

GREIT: a unified approach to 2D linear EIT reconstruction of lung images.

PubMed

Adler, Andy; Arnold, John H; Bayford, Richard; Borsic, Andrea; Brown, Brian; Dixon, Paul; Faes, Theo J C; Frerichs, Inéz; Gagnon, Hervé; Gärber, Yvo; Grychtol, Bartłomiej; Hahn, Günter; Lionheart, William R B; Malik, Anjum; Patterson, Robert P; Stocks, Janet; Tizzard, Andrew; Weiler, Norbert; Wolf, Gerhard K

2009-06-01

Electrical impedance tomography (EIT) is an attractive method for clinically monitoring patients during mechanical ventilation, because it can provide a non-invasive continuous image of pulmonary impedance which indicates the distribution of ventilation. However, most clinical and physiological research in lung EIT is done using older and proprietary algorithms; this is an obstacle to interpretation of EIT images because the reconstructed images are not well characterized. To address this issue, we develop a consensus linear reconstruction algorithm for lung EIT, called GREIT (Graz consensus Reconstruction algorithm for EIT). This paper describes the unified approach to linear image reconstruction developed for GREIT. The framework for the linear reconstruction algorithm consists of (1) detailed finite element models of a representative adult and neonatal thorax, (2) consensus on the performance figures of merit for EIT image reconstruction and (3) a systematic approach to optimize a linear reconstruction matrix to desired performance measures. Consensus figures of merit, in order of importance, are (a) uniform amplitude response, (b) small and uniform position error, (c) small ringing artefacts, (d) uniform resolution, (e) limited shape deformation and (f) high resolution. Such figures of merit must be attained while maintaining small noise amplification and small sensitivity to electrode and boundary movement. This approach represents the consensus of a large and representative group of experts in EIT algorithm design and clinical applications for pulmonary monitoring. All software and data to implement and test the algorithm have been made available under an open source license which allows free research and commercial use.

A combined reconstruction-classification method for diffuse optical tomography.

PubMed

Hiltunen, P; Prince, S J D; Arridge, S

2009-11-07

We present a combined classification and reconstruction algorithm for diffuse optical tomography (DOT). DOT is a nonlinear ill-posed inverse problem. Therefore, some regularization is needed. We present a mixture of Gaussians prior, which regularizes the DOT reconstruction step. During each iteration, the parameters of a mixture model are estimated. These associate each reconstructed pixel with one of several classes based on the current estimate of the optical parameters. This classification is exploited to form a new prior distribution to regularize the reconstruction step and update the optical parameters. The algorithm can be described as an iteration between an optimization scheme with zeroth-order variable mean and variance Tikhonov regularization and an expectation-maximization scheme for estimation of the model parameters. We describe the algorithm in a general Bayesian framework. Results from simulated test cases and phantom measurements show that the algorithm enhances the contrast of the reconstructed images with good spatial accuracy. The probabilistic classifications of each image contain only a few misclassified pixels.

Reconstruction of a digital core containing clay minerals based on a clustering algorithm.

PubMed

He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling

2017-10-01

It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K-means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.

Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data.

PubMed

Kim, Eun Young; Magnotta, Vincent A; Liu, Dawei; Johnson, Hans J

2014-09-01

Machine learning (ML)-based segmentation methods are a common technique in the medical image processing field. In spite of numerous research groups that have investigated ML-based segmentation frameworks, there remains unanswered aspects of performance variability for the choice of two key components: ML algorithm and intensity normalization. This investigation reveals that the choice of those elements plays a major part in determining segmentation accuracy and generalizability. The approach we have used in this study aims to evaluate relative benefits of the two elements within a subcortical MRI segmentation framework. Experiments were conducted to contrast eight machine-learning algorithm configurations and 11 normalization strategies for our brain MR segmentation framework. For the intensity normalization, a Stable Atlas-based Mapped Prior (STAMP) was utilized to take better account of contrast along boundaries of structures. Comparing eight machine learning algorithms on down-sampled segmentation MR data, it was obvious that a significant improvement was obtained using ensemble-based ML algorithms (i.e., random forest) or ANN algorithms. Further investigation between these two algorithms also revealed that the random forest results provided exceptionally good agreement with manual delineations by experts. Additional experiments showed that the effect of STAMP-based intensity normalization also improved the robustness of segmentation for multicenter data sets. The constructed framework obtained good multicenter reliability and was successfully applied on a large multicenter MR data set (n>3000). Less than 10% of automated segmentations were recommended for minimal expert intervention. These results demonstrate the feasibility of using the ML-based segmentation tools for processing large amount of multicenter MR images. We demonstrated dramatically different result profiles in segmentation accuracy according to the choice of ML algorithm and intensity normalization chosen. Copyright © 2014 Elsevier Inc. All rights reserved.

High-Performance Algorithms and Complex Fluids | Computational Science |

Science.gov Websites

only possible by combining experimental data with simulation. Capabilities Capabilities include: Block -laden, non-Newtonian, as well as traditional internal and external flows. Contact Ray Grout Group

Comparison Study of Three Different Image Reconstruction Algorithms for MAT-MI

PubMed Central

Xia, Rongmin; Li, Xu

2010-01-01

We report a theoretical study on magnetoacoustic tomography with magnetic induction (MAT-MI). According to the description of signal generation mechanism using Green’s function, the acoustic dipole model was proposed to describe acoustic source excited by the Lorentz force. Using Green’s function, three kinds of reconstruction algorithms based on different models of acoustic source (potential energy, vectored acoustic pressure, and divergence of Lorenz force) are deduced and compared, and corresponding numerical simulations were conducted to compare these three kinds of reconstruction algorithms. The computer simulation results indicate that the potential energy method and vectored pressure method can directly reconstruct the Lorentz force distribution and give a more accurate reconstruction of electrical conductivity. PMID:19846363

High-resolution reconstruction for terahertz imaging.

PubMed

Xu, Li-Min; Fan, Wen-Hui; Liu, Jia

2014-11-20

We present a high-resolution (HR) reconstruction model and algorithms for terahertz imaging, taking advantage of super-resolution methodology and algorithms. The algorithms used include projection onto a convex sets approach, iterative backprojection approach, Lucy-Richardson iteration, and 2D wavelet decomposition reconstruction. Using the first two HR reconstruction methods, we successfully obtain HR terahertz images with improved definition and lower noise from four low-resolution (LR) 22×24 terahertz images taken from our homemade THz-TDS system at the same experimental conditions with 1.0 mm pixel. Using the last two HR reconstruction methods, we transform one relatively LR terahertz image to a HR terahertz image with decreased noise. This indicates potential application of HR reconstruction methods in terahertz imaging with pulsed and continuous wave terahertz sources.

Bayesian reconstruction of projection reconstruction NMR (PR-NMR).

PubMed

Yoon, Ji Won

2014-11-01

Projection reconstruction nuclear magnetic resonance (PR-NMR) is a technique for generating multidimensional NMR spectra. A small number of projections from lower-dimensional NMR spectra are used to reconstruct the multidimensional NMR spectra. In our previous work, it was shown that multidimensional NMR spectra are efficiently reconstructed using peak-by-peak based reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. We propose an extended and generalized RJMCMC algorithm replacing a simple linear model with a linear mixed model to reconstruct close NMR spectra into true spectra. This statistical method generates samples in a Bayesian scheme. Our proposed algorithm is tested on a set of six projections derived from the three-dimensional 700 MHz HNCO spectrum of a protein HasA. Copyright © 2014 Elsevier Ltd. All rights reserved.

Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone-beam CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jing; Gu, Xuejun

2013-10-15

Purpose: Image reconstruction and motion model estimation in four-dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4D-CBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR).Methods: The proposed SMEIR algorithm consists of two alternating steps: (1) model-based iterative image reconstructionmore » to obtain a motion-compensated primary CBCT (m-pCBCT) and (2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction technique (SART) coupled with total variation minimization. During the forward- and backprojection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The quality of reconstructed 4D images and the accuracy of tumor motion trajectory are assessed by comparing with those resulting from conventional sequential 4D-CBCT reconstructions (FDK and total variation minimization) and motion estimation (demons algorithm). The performance of the SMEIR algorithm is further evaluated by reconstructing a lung cancer patient 4D-CBCT.Results: Image quality of 4D-CBCT is greatly improved by the SMEIR algorithm in both phantom and patient studies. When all projections are used to reconstruct a 3D-CBCT by FDK, motion-blurring artifacts are present, leading to a 24.4% relative reconstruction error in the NACT phantom. View aliasing artifacts are present in 4D-CBCT reconstructed by FDK from 20 projections, with a relative error of 32.1%. When total variation minimization is used to reconstruct 4D-CBCT, the relative error is 18.9%. Image quality of 4D-CBCT is substantially improved by using the SMEIR algorithm and relative error is reduced to 7.6%. The maximum error (MaxE) of tumor motion determined from the DVF obtained by demons registration on a FDK-reconstructed 4D-CBCT is 3.0, 2.3, and 7.1 mm along left–right (L-R), anterior–posterior (A-P), and superior–inferior (S-I) directions, respectively. From the DVF obtained by demons registration on 4D-CBCT reconstructed by total variation minimization, the MaxE of tumor motion is reduced to 1.5, 0.5, and 5.5 mm along L-R, A-P, and S-I directions. From the DVF estimated by SMEIR algorithm, the MaxE of tumor motion is further reduced to 0.8, 0.4, and 1.5 mm along L-R, A-P, and S-I directions, respectively.Conclusions: The proposed SMEIR algorithm is able to estimate a motion model and reconstruct motion-compensated 4D-CBCT. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation.« less

Regularization iteration imaging algorithm for electrical capacitance tomography

NASA Astrophysics Data System (ADS)

Tong, Guowei; Liu, Shi; Chen, Hongyan; Wang, Xueyao

2018-03-01

The image reconstruction method plays a crucial role in real-world applications of the electrical capacitance tomography technique. In this study, a new cost function that simultaneously considers the sparsity and low-rank properties of the imaging targets is proposed to improve the quality of the reconstruction images, in which the image reconstruction task is converted into an optimization problem. Within the framework of the split Bregman algorithm, an iterative scheme that splits a complicated optimization problem into several simpler sub-tasks is developed to solve the proposed cost function efficiently, in which the fast-iterative shrinkage thresholding algorithm is introduced to accelerate the convergence. Numerical experiment results verify the effectiveness of the proposed algorithm in improving the reconstruction precision and robustness.

Reconstructing cortical current density by exploring sparseness in the transform domain

NASA Astrophysics Data System (ADS)

Ding, Lei

2009-05-01

In the present study, we have developed a novel electromagnetic source imaging approach to reconstruct extended cortical sources by means of cortical current density (CCD) modeling and a novel EEG imaging algorithm which explores sparseness in cortical source representations through the use of L1-norm in objective functions. The new sparse cortical current density (SCCD) imaging algorithm is unique since it reconstructs cortical sources by attaining sparseness in a transform domain (the variation map of cortical source distributions). While large variations are expected to occur along boundaries (sparseness) between active and inactive cortical regions, cortical sources can be reconstructed and their spatial extents can be estimated by locating these boundaries. We studied the SCCD algorithm using numerous simulations to investigate its capability in reconstructing cortical sources with different extents and in reconstructing multiple cortical sources with different extent contrasts. The SCCD algorithm was compared with two L2-norm solutions, i.e. weighted minimum norm estimate (wMNE) and cortical LORETA. Our simulation data from the comparison study show that the proposed sparse source imaging algorithm is able to accurately and efficiently recover extended cortical sources and is promising to provide high-accuracy estimation of cortical source extents.

Image reconstruction and scan configurations enabled by optimization-based algorithms in multispectral CT

NASA Astrophysics Data System (ADS)

Chen, Buxin; Zhang, Zheng; Sidky, Emil Y.; Xia, Dan; Pan, Xiaochuan

2017-11-01

Optimization-based algorithms for image reconstruction in multispectral (or photon-counting) computed tomography (MCT) remains a topic of active research. The challenge of optimization-based image reconstruction in MCT stems from the inherently non-linear data model that can lead to a non-convex optimization program for which no mathematically exact solver seems to exist for achieving globally optimal solutions. In this work, based upon a non-linear data model, we design a non-convex optimization program, derive its first-order-optimality conditions, and propose an algorithm to solve the program for image reconstruction in MCT. In addition to consideration of image reconstruction for the standard scan configuration, the emphasis is on investigating the algorithm’s potential for enabling non-standard scan configurations with no or minimum hardware modification to existing CT systems, which has potential practical implications for lowered hardware cost, enhanced scanning flexibility, and reduced imaging dose/time in MCT. Numerical studies are carried out for verification of the algorithm and its implementation, and for a preliminary demonstration and characterization of the algorithm in reconstructing images and in enabling non-standard configurations with varying scanning angular range and/or x-ray illumination coverage in MCT.

A general Bayesian image reconstruction algorithm with entropy prior: Preliminary application to HST data

NASA Astrophysics Data System (ADS)

Nunez, Jorge; Llacer, Jorge

1993-10-01

This paper describes a general Bayesian iterative algorithm with entropy prior for image reconstruction. It solves the cases of both pure Poisson data and Poisson data with Gaussian readout noise. The algorithm maintains positivity of the solution; it includes case-specific prior information (default map) and flatfield corrections; it removes background and can be accelerated to be faster than the Richardson-Lucy algorithm. In order to determine the hyperparameter that balances the entropy and liklihood terms in the Bayesian approach, we have used a liklihood cross-validation technique. Cross-validation is more robust than other methods because it is less demanding in terms of the knowledge of exact data characteristics and of the point-spread function. We have used the algorithm to reconstruct successfully images obtained in different space-and ground-based imaging situations. It has been possible to recover most of the original intended capabilities of the Hubble Space Telescope (HST) wide field and planetary camera (WFPC) and faint object camera (FOC) from images obtained in their present state. Semireal simulations for the future wide field planetary camera 2 show that even after the repair of the spherical abberration problem, image reconstruction can play a key role in improving the resolution of the cameras, well beyond the design of the Hubble instruments. We also show that ground-based images can be reconstructed successfully with the algorithm. A technique which consists of dividing the CCD observations into two frames, with one-half the exposure time each, emerges as a recommended procedure for the utilization of the described algorithms. We have compared our technique with two commonly used reconstruction algorithms: the Richardson-Lucy and the Cambridge maximum entropy algorithms.

Optimization of Stereo Matching in 3D Reconstruction Based on Binocular Vision

NASA Astrophysics Data System (ADS)

Gai, Qiyang

2018-01-01

Stereo matching is one of the key steps of 3D reconstruction based on binocular vision. In order to improve the convergence speed and accuracy in 3D reconstruction based on binocular vision, this paper adopts the combination method of polar constraint and ant colony algorithm. By using the line constraint to reduce the search range, an ant colony algorithm is used to optimize the stereo matching feature search function in the proposed search range. Through the establishment of the stereo matching optimization process analysis model of ant colony algorithm, the global optimization solution of stereo matching in 3D reconstruction based on binocular vision system is realized. The simulation results show that by the combining the advantage of polar constraint and ant colony algorithm, the stereo matching range of 3D reconstruction based on binocular vision is simplified, and the convergence speed and accuracy of this stereo matching process are improved.

Exact BPF and FBP algorithms for nonstandard saddle curves

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu Hengyong; Zhao Shiying; Ye Yangbo

2005-11-15

A hot topic in cone-beam CT research is exact cone-beam reconstruction from a general scanning trajectory. Particularly, a nonstandard saddle curve attracts attention, as this construct allows the continuous periodic scanning of a volume-of-interest (VOI). Here we evaluate two algorithms for reconstruction from data collected along a nonstandard saddle curve, which are in the filtered backprojection (FBP) and backprojection filtration (BPF) formats, respectively. Both the algorithms are implemented in a chord-based coordinate system. Then, a rebinning procedure is utilized to transform the reconstructed results into the natural coordinate system. The simulation results demonstrate that the FBP algorithm produces better imagemore » quality than the BPF algorithm, while both the algorithms exhibit similar noise characteristics.« less

Ionospheric-thermospheric UV tomography: 1. Image space reconstruction algorithms

NASA Astrophysics Data System (ADS)

Dymond, K. F.; Budzien, S. A.; Hei, M. A.

2017-03-01

We present and discuss two algorithms of the class known as Image Space Reconstruction Algorithms (ISRAs) that we are applying to the solution of large-scale ionospheric tomography problems. ISRAs have several desirable features that make them useful for ionospheric tomography. In addition to producing nonnegative solutions, ISRAs are amenable to sparse-matrix formulations and are fast, stable, and robust. We present the results of our studies of two types of ISRA: the Least Squares Positive Definite and the Richardson-Lucy algorithms. We compare their performance to the Multiplicative Algebraic Reconstruction and Conjugate Gradient Least Squares algorithms. We then discuss the use of regularization in these algorithms and present our new approach based on regularization to a partial differential equation.

Performance evaluation of the whole-body PET scanner ECAT EXACT HR/sup +/ following the IEC standard

NASA Astrophysics Data System (ADS)

Adam, L.-E.; Zaers, J.; Ostertag, H.; Trojan, H.; Bellemann, M. E.; Brix, G.

1997-06-01

The performance parameters of the whole-body PET scanner ECAT EXACT HR/sup +/ (CTI/Siemens, Knoxville, TN) were determined following the standard proposed by the International Electrotechnical Commission (IEC). The tests were expanded by some measurements concerning the accuracy of the correction algorithms and the geometric fidelity of the reconstructed images. The scanner consists of 32 rings, each with 576 BGO detectors (4.05/spl times/4.39/spl times/30 mm/sup 3/), covering an axial field-of-view of 15.5 cm and a patient port of 56.2 cm. The transaxial FWHM determined by a Gaussian fit in the 2D (3D) mode is 4.5 (4.3) mm at the center. It increases to 8.9 (8.3) mm radially and to 5.8 (5.2) mm tangentially at a radial distance of r=20 cm. The average axial resolution varies between 4.9 (4.1) mm FWHM at the center and 8.8 (8.1) mm at r=20 cm. The system sensitivity for unscattered true events is 5.85 (26.4) cps/Bq/ml (measured with a 20 cm cylinder). The 50% dead-time losses were reached for a true event count rate (including scatter) of 286 (500) kcps at an activity concentration of 74 (25) kBq/ml. The system scatter fraction is 0.24 (0.35). With the exception of the 3D attenuation correction algorithm, all correction algorithms work reliably. The results reveal that the ECAT EXACT HR/sup +/ has a good and nearly isotropic spatial resolution. Due to the small detector elements, however, it has a low slice sensitivity which is a limiting factor for image quality.

3-dimensional electron microscopic imaging of the zebrafish olfactory bulb and dense reconstruction of neurons.

PubMed

Wanner, Adrian A; Genoud, Christel; Friedrich, Rainer W

2016-11-08

Large-scale reconstructions of neuronal populations are critical for structural analyses of neuronal cell types and circuits. Dense reconstructions of neurons from image data require ultrastructural resolution throughout large volumes, which can be achieved by automated volumetric electron microscopy (EM) techniques. We used serial block face scanning EM (SBEM) and conductive sample embedding to acquire an image stack from an olfactory bulb (OB) of a zebrafish larva at a voxel resolution of 9.25×9.25×25 nm 3 . Skeletons of 1,022 neurons, 98% of all neurons in the OB, were reconstructed by manual tracing and efficient error correction procedures. An ergonomic software package, PyKNOSSOS, was created in Python for data browsing, neuron tracing, synapse annotation, and visualization. The reconstructions allow for detailed analyses of morphology, projections and subcellular features of different neuron types. The high density of reconstructions enables geometrical and topological analyses of the OB circuitry. Image data can be accessed and viewed through the neurodata web services (http://www.neurodata.io). Raw data and reconstructions can be visualized in PyKNOSSOS.

3-dimensional electron microscopic imaging of the zebrafish olfactory bulb and dense reconstruction of neurons

PubMed Central

Wanner, Adrian A.; Genoud, Christel; Friedrich, Rainer W.

2016-01-01

Large-scale reconstructions of neuronal populations are critical for structural analyses of neuronal cell types and circuits. Dense reconstructions of neurons from image data require ultrastructural resolution throughout large volumes, which can be achieved by automated volumetric electron microscopy (EM) techniques. We used serial block face scanning EM (SBEM) and conductive sample embedding to acquire an image stack from an olfactory bulb (OB) of a zebrafish larva at a voxel resolution of 9.25×9.25×25 nm3. Skeletons of 1,022 neurons, 98% of all neurons in the OB, were reconstructed by manual tracing and efficient error correction procedures. An ergonomic software package, PyKNOSSOS, was created in Python for data browsing, neuron tracing, synapse annotation, and visualization. The reconstructions allow for detailed analyses of morphology, projections and subcellular features of different neuron types. The high density of reconstructions enables geometrical and topological analyses of the OB circuitry. Image data can be accessed and viewed through the neurodata web services (http://www.neurodata.io). Raw data and reconstructions can be visualized in PyKNOSSOS. PMID:27824337

Third-dimension information retrieval from a single convergent-beam transmission electron diffraction pattern using an artificial neural network

NASA Astrophysics Data System (ADS)

Pennington, Robert S.; Van den Broek, Wouter; Koch, Christoph T.

2014-05-01

We have reconstructed third-dimension specimen information from convergent-beam electron diffraction (CBED) patterns simulated using the stacked-Bloch-wave method. By reformulating the stacked-Bloch-wave formalism as an artificial neural network and optimizing with resilient back propagation, we demonstrate specimen orientation reconstructions with depth resolutions down to 5 nm. To show our algorithm's ability to analyze realistic data, we also discuss and demonstrate our algorithm reconstructing from noisy data and using a limited number of CBED disks. Applicability of this reconstruction algorithm to other specimen parameters is discussed.

A Two-Stage Reconstruction Processor for Human Detection in Compressive Sensing CMOS Radar.

PubMed

Tsao, Kuei-Chi; Lee, Ling; Chu, Ta-Shun; Huang, Yuan-Hao

2018-04-05

Complementary metal-oxide-semiconductor (CMOS) radar has recently gained much research attraction because small and low-power CMOS devices are very suitable for deploying sensing nodes in a low-power wireless sensing system. This study focuses on the signal processing of a wireless CMOS impulse radar system that can detect humans and objects in the home-care internet-of-things sensing system. The challenges of low-power CMOS radar systems are the weakness of human signals and the high computational complexity of the target detection algorithm. The compressive sensing-based detection algorithm can relax the computational costs by avoiding the utilization of matched filters and reducing the analog-to-digital converter bandwidth requirement. The orthogonal matching pursuit (OMP) is one of the popular signal reconstruction algorithms for compressive sensing radar; however, the complexity is still very high because the high resolution of human respiration leads to high-dimension signal reconstruction. Thus, this paper proposes a two-stage reconstruction algorithm for compressive sensing radar. The proposed algorithm not only has lower complexity than the OMP algorithm by 75% but also achieves better positioning performance than the OMP algorithm especially in noisy environments. This study also designed and implemented the algorithm by using Vertex-7 FPGA chip (Xilinx, San Jose, CA, USA). The proposed reconstruction processor can support the 256 × 13 real-time radar image display with a throughput of 28.2 frames per second.

DART: a practical reconstruction algorithm for discrete tomography.

PubMed

Batenburg, Kees Joost; Sijbers, Jan

2011-09-01

In this paper, we present an iterative reconstruction algorithm for discrete tomography, called discrete algebraic reconstruction technique (DART). DART can be applied if the scanned object is known to consist of only a few different compositions, each corresponding to a constant gray value in the reconstruction. Prior knowledge of the gray values for each of the compositions is exploited to steer the current reconstruction towards a reconstruction that contains only these gray values. Based on experiments with both simulated CT data and experimental μCT data, it is shown that DART is capable of computing more accurate reconstructions from a small number of projection images, or from a small angular range, than alternative methods. It is also shown that DART can deal effectively with noisy projection data and that the algorithm is robust with respect to errors in the estimation of the gray values.

Filtered-backprojection reconstruction for a cone-beam computed tomography scanner with independent source and detector rotations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rit, Simon, E-mail: simon.rit@creatis.insa-lyon.fr; Clackdoyle, Rolf; Keuschnigg, Peter

Purpose: A new cone-beam CT scanner for image-guided radiotherapy (IGRT) can independently rotate the source and the detector along circular trajectories. Existing reconstruction algorithms are not suitable for this scanning geometry. The authors propose and evaluate a three-dimensional (3D) filtered-backprojection reconstruction for this situation. Methods: The source and the detector trajectories are tuned to image a field-of-view (FOV) that is offset with respect to the center-of-rotation. The new reconstruction formula is derived from the Feldkamp algorithm and results in a similar three-step algorithm: projection weighting, ramp filtering, and weighted backprojection. Simulations of a Shepp Logan digital phantom were used tomore » evaluate the new algorithm with a 10 cm-offset FOV. A real cone-beam CT image with an 8.5 cm-offset FOV was also obtained from projections of an anthropomorphic head phantom. Results: The quality of the cone-beam CT images reconstructed using the new algorithm was similar to those using the Feldkamp algorithm which is used in conventional cone-beam CT. The real image of the head phantom exhibited comparable image quality to that of existing systems. Conclusions: The authors have proposed a 3D filtered-backprojection reconstruction for scanners with independent source and detector rotations that is practical and effective. This algorithm forms the basis for exploiting the scanner’s unique capabilities in IGRT protocols.« less

Accurate 3D reconstruction by a new PDS-OSEM algorithm for HRRT

NASA Astrophysics Data System (ADS)

Chen, Tai-Been; Horng-Shing Lu, Henry; Kim, Hang-Keun; Son, Young-Don; Cho, Zang-Hee

2014-03-01

State-of-the-art high resolution research tomography (HRRT) provides high resolution PET images with full 3D human brain scanning. But, a short time frame in dynamic study causes many problems related to the low counts in the acquired data. The PDS-OSEM algorithm was proposed to reconstruct the HRRT image with a high signal-to-noise ratio that provides accurate information for dynamic data. The new algorithm was evaluated by simulated image, empirical phantoms, and real human brain data. Meanwhile, the time activity curve was adopted to validate a reconstructed performance of dynamic data between PDS-OSEM and OP-OSEM algorithms. According to simulated and empirical studies, the PDS-OSEM algorithm reconstructs images with higher quality, higher accuracy, less noise, and less average sum of square error than those of OP-OSEM. The presented algorithm is useful to provide quality images under the condition of low count rates in dynamic studies with a short scan time.

Rapid execution of fan beam image reconstruction algorithms using efficient computational techniques and special-purpose processors

NASA Astrophysics Data System (ADS)

Gilbert, B. K.; Robb, R. A.; Chu, A.; Kenue, S. K.; Lent, A. H.; Swartzlander, E. E., Jr.

1981-02-01

Rapid advances during the past ten years of several forms of computer-assisted tomography (CT) have resulted in the development of numerous algorithms to convert raw projection data into cross-sectional images. These reconstruction algorithms are either 'iterative,' in which a large matrix algebraic equation is solved by successive approximation techniques; or 'closed form'. Continuing evolution of the closed form algorithms has allowed the newest versions to produce excellent reconstructed images in most applications. This paper will review several computer software and special-purpose digital hardware implementations of closed form algorithms, either proposed during the past several years by a number of workers or actually implemented in commercial or research CT scanners. The discussion will also cover a number of recently investigated algorithmic modifications which reduce the amount of computation required to execute the reconstruction process, as well as several new special-purpose digital hardware implementations under development in laboratories at the Mayo Clinic.

An accelerated photo-magnetic imaging reconstruction algorithm based on an analytical forward solution and a fast Jacobian assembly method

NASA Astrophysics Data System (ADS)

Nouizi, F.; Erkol, H.; Luk, A.; Marks, M.; Unlu, M. B.; Gulsen, G.

2016-10-01

We previously introduced photo-magnetic imaging (PMI), an imaging technique that illuminates the medium under investigation with near-infrared light and measures the induced temperature increase using magnetic resonance thermometry (MRT). Using a multiphysics solver combining photon migration and heat diffusion, PMI models the spatiotemporal distribution of temperature variation and recovers high resolution optical absorption images using these temperature maps. In this paper, we present a new fast non-iterative reconstruction algorithm for PMI. This new algorithm uses analytic methods during the resolution of the forward problem and the assembly of the sensitivity matrix. We validate our new analytic-based algorithm with the first generation finite element method (FEM) based reconstruction algorithm previously developed by our team. The validation is performed using, first synthetic data and afterwards, real MRT measured temperature maps. Our new method accelerates the reconstruction process 30-fold when compared to a single iteration of the FEM-based algorithm.

A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A.

2010-01-15

Purpose: Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. Methods: The proposed approach comprises of two reconstruction steps. In the first step, amore » chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack-Noo-formula-based filteredbackprojection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. Results: The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. Conclusions: They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories.« less

Resolution recovery for Compton camera using origin ensemble algorithm.

PubMed

Andreyev, A; Celler, A; Ozsahin, I; Sitek, A

2016-08-01

Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR. To validate the proposed algorithm we used Monte Carlo simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background. Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2-3 orders of magnitude per iteration. The results of our tests demonstrate the improvement of image resolution provided by the OE reconstructions with resolution recovery. The quality of images and their contrast are similar to those obtained from the OE reconstructions from scans simulated with perfect energy and spatial resolutions.

Functional validation and comparison framework for EIT lung imaging.

PubMed

Grychtol, Bartłomiej; Elke, Gunnar; Meybohm, Patrick; Weiler, Norbert; Frerichs, Inéz; Adler, Andy

2014-01-01

Electrical impedance tomography (EIT) is an emerging clinical tool for monitoring ventilation distribution in mechanically ventilated patients, for which many image reconstruction algorithms have been suggested. We propose an experimental framework to assess such algorithms with respect to their ability to correctly represent well-defined physiological changes. We defined a set of clinically relevant ventilation conditions and induced them experimentally in 8 pigs by controlling three ventilator settings (tidal volume, positive end-expiratory pressure and the fraction of inspired oxygen). In this way, large and discrete shifts in global and regional lung air content were elicited. We use the framework to compare twelve 2D EIT reconstruction algorithms, including backprojection (the original and still most frequently used algorithm), GREIT (a more recent consensus algorithm for lung imaging), truncated singular value decomposition (TSVD), several variants of the one-step Gauss-Newton approach and two iterative algorithms. We consider the effects of using a 3D finite element model, assuming non-uniform background conductivity, noise modeling, reconstructing for electrode movement, total variation (TV) reconstruction, robust error norms, smoothing priors, and using difference vs. normalized difference data. Our results indicate that, while variation in appearance of images reconstructed from the same data is not negligible, clinically relevant parameters do not vary considerably among the advanced algorithms. Among the analysed algorithms, several advanced algorithms perform well, while some others are significantly worse. Given its vintage and ad-hoc formulation backprojection works surprisingly well, supporting the validity of previous studies in lung EIT.

Plane-dependent ML scatter scaling: 3D extension of the 2D simulated single scatter (SSS) estimate

NASA Astrophysics Data System (ADS)

Rezaei, Ahmadreza; Salvo, Koen; Vahle, Thomas; Panin, Vladimir; Casey, Michael; Boada, Fernando; Defrise, Michel; Nuyts, Johan

2017-08-01

Scatter correction is typically done using a simulation of the single scatter, which is then scaled to account for multiple scatters and other possible model mismatches. This scaling factor is determined by fitting the simulated scatter sinogram to the measured sinogram, using only counts measured along LORs that do not intersect the patient body, i.e. ‘scatter-tails’. Extending previous work, we propose to scale the scatter with a plane dependent factor, which is determined as an additional unknown in the maximum likelihood (ML) reconstructions, using counts in the entire sinogram rather than only the ‘scatter-tails’. The ML-scaled scatter estimates are validated using a Monte-Carlo simulation of a NEMA-like phantom, a phantom scan with typical contrast ratios of a 68Ga-PSMA scan, and 23 whole-body 18F-FDG patient scans. On average, we observe a 12.2% change in the total amount of tracer activity of the MLEM reconstructions of our whole-body patient database when the proposed ML scatter scales are used. Furthermore, reconstructions using the ML-scaled scatter estimates are found to eliminate the typical ‘halo’ artifacts that are often observed in the vicinity of high focal uptake regions.

Spectral unmixing of urban land cover using a generic library approach

NASA Astrophysics Data System (ADS)

Degerickx, Jeroen; Lordache, Marian-Daniel; Okujeni, Akpona; Hermy, Martin; van der Linden, Sebastian; Somers, Ben

2016-10-01

Remote sensing based land cover classification in urban areas generally requires the use of subpixel classification algorithms to take into account the high spatial heterogeneity. These spectral unmixing techniques often rely on spectral libraries, i.e. collections of pure material spectra (endmembers, EM), which ideally cover the large EM variability typically present in urban scenes. Despite the advent of several (semi-) automated EM detection algorithms, the collection of such image-specific libraries remains a tedious and time-consuming task. As an alternative, we suggest the use of a generic urban EM library, containing material spectra under varying conditions, acquired from different locations and sensors. This approach requires an efficient EM selection technique, capable of only selecting those spectra relevant for a specific image. In this paper, we evaluate and compare the potential of different existing library pruning algorithms (Iterative Endmember Selection and MUSIC) using simulated hyperspectral (APEX) data of the Brussels metropolitan area. In addition, we develop a new hybrid EM selection method which is shown to be highly efficient in dealing with both imagespecific and generic libraries, subsequently yielding more robust land cover classification results compared to existing methods. Future research will include further optimization of the proposed algorithm and additional tests on both simulated and real hyperspectral data.

CORRIGENDUM: A new algorithm for the shape reconstruction of perfectly conducting objects A new algorithm for the shape reconstruction of perfectly conducting objects

NASA Astrophysics Data System (ADS)

Çayören, M.; Akduman, I.; Yapar, A.; Crocco, L.

2010-03-01

The reference list should have included the conference communications [1] and [2], wherein we introduced the algorithm described in this paper. Note that a less complete description of the algorithm was given in [1]. However, the example considering a bean-shaped target is the same in the two papers and it is reused in this paper by kind permission of the Applied Computational Electromagnetics Society. References [1] Crocco L, Akduman I, Çayören M and Yapar A 2007 A new method for shape reconstruction of perfectly conducting targets The 23rd Annual Review of Progress in Applied Computational Electromagnetics (Verona, Italy) [2] Çayören M, Akduman I, Yapar A and Crocco L 2007 A new algorithm for the shape reconstruction of perfectly conducting objects Progress in Electromagnetics Research Symposium (PIERS) (Beijing, PRC)

Maximum likelihood positioning algorithm for high-resolution PET scanners

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gross-Weege, Nicolas, E-mail: nicolas.gross-weege@pmi.rwth-aachen.de, E-mail: schulz@pmi.rwth-aachen.de; Schug, David; Hallen, Patrick

2016-06-15

Purpose: In high-resolution positron emission tomography (PET), lightsharing elements are incorporated into typical detector stacks to read out scintillator arrays in which one scintillator element (crystal) is smaller than the size of the readout channel. In order to identify the hit crystal by means of the measured light distribution, a positioning algorithm is required. One commonly applied positioning algorithm uses the center of gravity (COG) of the measured light distribution. The COG algorithm is limited in spatial resolution by noise and intercrystal Compton scatter. The purpose of this work is to develop a positioning algorithm which overcomes this limitation. Methods:more » The authors present a maximum likelihood (ML) algorithm which compares a set of expected light distributions given by probability density functions (PDFs) with the measured light distribution. Instead of modeling the PDFs by using an analytical model, the PDFs of the proposed ML algorithm are generated assuming a single-gamma-interaction model from measured data. The algorithm was evaluated with a hot-rod phantom measurement acquired with the preclinical HYPERION II {sup D} PET scanner. In order to assess the performance with respect to sensitivity, energy resolution, and image quality, the ML algorithm was compared to a COG algorithm which calculates the COG from a restricted set of channels. The authors studied the energy resolution of the ML and the COG algorithm regarding incomplete light distributions (missing channel information caused by detector dead time). Furthermore, the authors investigated the effects of using a filter based on the likelihood values on sensitivity, energy resolution, and image quality. Results: A sensitivity gain of up to 19% was demonstrated in comparison to the COG algorithm for the selected operation parameters. Energy resolution and image quality were on a similar level for both algorithms. Additionally, the authors demonstrated that the performance of the ML algorithm is less prone to missing channel information. A likelihood filter visually improved the image quality, i.e., the peak-to-valley increased up to a factor of 3 for 2-mm-diameter phantom rods by rejecting 87% of the coincidences. A relative improvement of the energy resolution of up to 12.8% was also measured rejecting 91% of the coincidences. Conclusions: The developed ML algorithm increases the sensitivity by correctly handling missing channel information without influencing energy resolution or image quality. Furthermore, the authors showed that energy resolution and image quality can be improved substantially by rejecting events that do not comply well with the single-gamma-interaction model, such as Compton-scattered events.« less

Parallelizable 3D statistical reconstruction for C-arm tomosynthesis system

NASA Astrophysics Data System (ADS)

Wang, Beilei; Barner, Kenneth; Lee, Denny

2005-04-01

Clinical diagnosis and security detection tasks increasingly require 3D information which is difficult or impossible to obtain from 2D (two dimensional) radiographs. As a 3D (three dimensional) radiographic and non-destructive imaging technique, digital tomosynthesis is especially fit for cases where 3D information is required while a complete projection data is not available. Nowadays, FBP (filtered back projection) is extensively used in industry for its fast speed and simplicity. However, it is hard to deal with situations where only a limited number of projections from constrained directions are available, or the SNR (signal to noises ratio) of the projections is low. In order to deal with noise and take into account a priori information of the object, a statistical image reconstruction method is described based on the acquisition model of X-ray projections. We formulate a ML (maximum likelihood) function for this model and develop an ordered-subsets iterative algorithm to estimate the unknown attenuation of the object. Simulations show that satisfied results can be obtained after 1 to 2 iterations, and after that there is no significant improvement of the image quality. An adaptive wiener filter is also applied to the reconstructed image to remove its noise. Some approximations to speed up the reconstruction computation are also considered. Applying this method to computer generated projections of a revised Shepp phantom and true projections from diagnostic radiographs of a patient"s hand and mammography images yields reconstructions with impressive quality. Parallel programming is also implemented and tested. The quality of the reconstructed object is conserved, while the computation time is considerably reduced by almost the number of threads used.

WE-G-18A-08: Axial Cone Beam DBPF Reconstruction with Three-Dimensional Weighting and Butterfly Filtering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, S; Wang, W; Tang, X

2014-06-15

Purpose: With the major benefit in dealing with data truncation for ROI reconstruction, the algorithm of differentiated backprojection followed by Hilbert filtering (DBPF) is originally derived for image reconstruction from parallel- or fan-beam data. To extend its application for axial CB scan, we proposed the integration of the DBPF algorithm with 3-D weighting. In this work, we further propose the incorporation of Butterfly filtering into the 3-D weighted axial CB-DBPF algorithm and conduct an evaluation to verify its performance. Methods: Given an axial scan, tomographic images are reconstructed by the DBPF algorithm with 3-D weighting, in which streak artifacts existmore » along the direction of Hilbert filtering. Recognizing this orientation-specific behavior, a pair of orthogonal Butterfly filtering is applied on the reconstructed images with the horizontal and vertical Hilbert filtering correspondingly. In addition, the Butterfly filtering can also be utilized for streak artifact suppression in the scenarios wherein only partial scan data with an angular range as small as 270° are available. Results: Preliminary data show that, with the correspondingly applied Butterfly filtering, the streak artifacts existing in the images reconstructed by the 3-D weighted DBPF algorithm can be suppressed to an unnoticeable level. Moreover, the Butterfly filtering also works at the scenarios of partial scan, though the 3-D weighting scheme may have to be dropped because of no sufficient projection data are available. Conclusion: As an algorithmic step, the incorporation of Butterfly filtering enables the DBPF algorithm for CB image reconstruction from data acquired along either a full or partial axial scan.« less

Needs, Pains, and Motivations in Autonomous Agents.

PubMed

Starzyk, Janusz A; Graham, James; Puzio, Leszek

This paper presents the development of a motivated learning (ML) agent with symbolic I/O. Our earlier work on the ML agent was enhanced, giving it autonomy for interaction with other agents. Specifically, we equipped the agent with drives and pains that establish its motivations to learn how to respond to desired and undesired events and create related abstract goals. The purpose of this paper is to explore the autonomous development of motivations and memory in agents within a simulated environment. The ML agent has been implemented in a virtual environment created within the NeoAxis game engine. Additionally, to illustrate the benefits of an ML-based agent, we compared the performance of our algorithm against various reinforcement learning (RL) algorithms in a dynamic test scenario, and demonstrated that our ML agent learns better than any of the tested RL agents.This paper presents the development of a motivated learning (ML) agent with symbolic I/O. Our earlier work on the ML agent was enhanced, giving it autonomy for interaction with other agents. Specifically, we equipped the agent with drives and pains that establish its motivations to learn how to respond to desired and undesired events and create related abstract goals. The purpose of this paper is to explore the autonomous development of motivations and memory in agents within a simulated environment. The ML agent has been implemented in a virtual environment created within the NeoAxis game engine. Additionally, to illustrate the benefits of an ML-based agent, we compared the performance of our algorithm against various reinforcement learning (RL) algorithms in a dynamic test scenario, and demonstrated that our ML agent learns better than any of the tested RL agents.

SU-E-I-01: Iterative CBCT Reconstruction with a Feature-Preserving Penalty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lyu, Q; Li, B; Southern Medical University, Guangzhou

2015-06-15

Purpose: Low-dose CBCT is desired in various clinical applications. Iterative image reconstruction algorithms have shown advantages in suppressing noise in low-dose CBCT. However, due to the smoothness constraint enforced during the reconstruction process, edges may be blurred and image features may lose in the reconstructed image. In this work, we proposed a new penalty design to preserve image features in the image reconstructed by iterative algorithms. Methods: Low-dose CBCT is reconstructed by minimizing the penalized weighted least-squares (PWLS) objective function. Binary Robust Independent Elementary Features (BRIEF) of the image were integrated into the penalty of PWLS. BRIEF is a generalmore » purpose point descriptor that can be used to identify important features of an image. In this work, BRIEF distance of two neighboring pixels was used to weigh the smoothing parameter in PWLS. For pixels of large BRIEF distance, weaker smooth constraint will be enforced. Image features will be better preserved through such a design. The performance of the PWLS algorithm with BRIEF penalty was evaluated by a CatPhan 600 phantom. Results: The image quality reconstructed by the proposed PWLS-BRIEF algorithm is superior to that by the conventional PWLS method and the standard FDK method. At matched noise level, edges in PWLS-BRIEF reconstructed image are better preserved. Conclusion: This study demonstrated that the proposed PWLS-BRIEF algorithm has great potential on preserving image features in low-dose CBCT.« less

Regularization of nonlinear decomposition of spectral x-ray projection images.

PubMed

Ducros, Nicolas; Abascal, Juan Felipe Perez-Juste; Sixou, Bruno; Rit, Simon; Peyrin, Françoise

2017-09-01

Exploiting the x-ray measurements obtained in different energy bins, spectral computed tomography (CT) has the ability to recover the 3-D description of a patient in a material basis. This may be achieved solving two subproblems, namely the material decomposition and the tomographic reconstruction problems. In this work, we address the material decomposition of spectral x-ray projection images, which is a nonlinear ill-posed problem. Our main contribution is to introduce a material-dependent spatial regularization in the projection domain. The decomposition problem is solved iteratively using a Gauss-Newton algorithm that can benefit from fast linear solvers. A Matlab implementation is available online. The proposed regularized weighted least squares Gauss-Newton algorithm (RWLS-GN) is validated on numerical simulations of a thorax phantom made of up to five materials (soft tissue, bone, lung, adipose tissue, and gadolinium), which is scanned with a 120 kV source and imaged by a 4-bin photon counting detector. To evaluate the method performance of our algorithm, different scenarios are created by varying the number of incident photons, the concentration of the marker and the configuration of the phantom. The RWLS-GN method is compared to the reference maximum likelihood Nelder-Mead algorithm (ML-NM). The convergence of the proposed method and its dependence on the regularization parameter are also studied. We show that material decomposition is feasible with the proposed method and that it converges in few iterations. Material decomposition with ML-NM was very sensitive to noise, leading to decomposed images highly affected by noise, and artifacts even for the best case scenario. The proposed method was less sensitive to noise and improved contrast-to-noise ratio of the gadolinium image. Results were superior to those provided by ML-NM in terms of image quality and decomposition was 70 times faster. For the assessed experiments, material decomposition was possible with the proposed method when the number of incident photons was equal or larger than 10 5 and when the marker concentration was equal or larger than 0.03 g·cm -3 . The proposed method efficiently solves the nonlinear decomposition problem for spectral CT, which opens up new possibilities such as material-specific regularization in the projection domain and a parallelization framework, in which projections are solved in parallel. © 2017 American Association of Physicists in Medicine.

Evaluation of noise and blur effects with SIRT-FISTA-TV reconstruction algorithm: Application to fast environmental transmission electron tomography.

PubMed

Banjak, Hussein; Grenier, Thomas; Epicier, Thierry; Koneti, Siddardha; Roiban, Lucian; Gay, Anne-Sophie; Magnin, Isabelle; Peyrin, Françoise; Maxim, Voichita

2018-06-01

Fast tomography in Environmental Transmission Electron Microscopy (ETEM) is of a great interest for in situ experiments where it allows to observe 3D real-time evolution of nanomaterials under operating conditions. In this context, we are working on speeding up the acquisition step to a few seconds mainly with applications on nanocatalysts. In order to accomplish such rapid acquisitions of the required tilt series of projections, a modern 4K high-speed camera is used, that can capture up to 100 images per second in a 2K binning mode. However, due to the fast rotation of the sample during the tilt procedure, noise and blur effects may occur in many projections which in turn would lead to poor quality reconstructions. Blurred projections make classical reconstruction algorithms inappropriate and require the use of prior information. In this work, a regularized algebraic reconstruction algorithm named SIRT-FISTA-TV is proposed. The performance of this algorithm using blurred data is studied by means of a numerical blur introduced into simulated images series to mimic possible mechanical instabilities/drifts during fast acquisitions. We also present reconstruction results from noisy data to show the robustness of the algorithm to noise. Finally, we show reconstructions with experimental datasets and we demonstrate the interest of fast tomography with an ultra-fast acquisition performed under environmental conditions, i.e. gas and temperature, in the ETEM. Compared to classically used SIRT and SART approaches, our proposed SIRT-FISTA-TV reconstruction algorithm provides higher quality tomograms allowing easier segmentation of the reconstructed volume for a better final processing and analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

Wavelet-based edge correlation incorporated iterative reconstruction for undersampled MRI.

PubMed

Hu, Changwei; Qu, Xiaobo; Guo, Di; Bao, Lijun; Chen, Zhong

2011-09-01

Undersampling k-space is an effective way to decrease acquisition time for MRI. However, aliasing artifacts introduced by undersampling may blur the edges of magnetic resonance images, which often contain important information for clinical diagnosis. Moreover, k-space data is often contaminated by the noise signals of unknown intensity. To better preserve the edge features while suppressing the aliasing artifacts and noises, we present a new wavelet-based algorithm for undersampled MRI reconstruction. The algorithm solves the image reconstruction as a standard optimization problem including a ℓ(2) data fidelity term and ℓ(1) sparsity regularization term. Rather than manually setting the regularization parameter for the ℓ(1) term, which is directly related to the threshold, an automatic estimated threshold adaptive to noise intensity is introduced in our proposed algorithm. In addition, a prior matrix based on edge correlation in wavelet domain is incorporated into the regularization term. Compared with nonlinear conjugate gradient descent algorithm, iterative shrinkage/thresholding algorithm, fast iterative soft-thresholding algorithm and the iterative thresholding algorithm using exponentially decreasing threshold, the proposed algorithm yields reconstructions with better edge recovery and noise suppression. Copyright © 2011 Elsevier Inc. All rights reserved.

M-AMST: an automatic 3D neuron tracing method based on mean shift and adapted minimum spanning tree.

PubMed

Wan, Zhijiang; He, Yishan; Hao, Ming; Yang, Jian; Zhong, Ning

2017-03-29

Understanding the working mechanism of the brain is one of the grandest challenges for modern science. Toward this end, the BigNeuron project was launched to gather a worldwide community to establish a big data resource and a set of the state-of-the-art of single neuron reconstruction algorithms. Many groups contributed their own algorithms for the project, including our mean shift and minimum spanning tree (M-MST). Although M-MST is intuitive and easy to implement, the MST just considers spatial information of single neuron and ignores the shape information, which might lead to less precise connections between some neuron segments. In this paper, we propose an improved algorithm, namely M-AMST, in which a rotating sphere model based on coordinate transformation is used to improve the weight calculation method in M-MST. Two experiments are designed to illustrate the effect of adapted minimum spanning tree algorithm and the adoptability of M-AMST in reconstructing variety of neuron image datasets respectively. In the experiment 1, taking the reconstruction of APP2 as reference, we produce the four difference scores (entire structure average (ESA), different structure average (DSA), percentage of different structure (PDS) and max distance of neurons' nodes (MDNN)) by comparing the neuron reconstruction of the APP2 and the other 5 competing algorithm. The result shows that M-AMST gets lower difference scores than M-MST in ESA, PDS and MDNN. Meanwhile, M-AMST is better than N-MST in ESA and MDNN. It indicates that utilizing the adapted minimum spanning tree algorithm which took the shape information of neuron into account can achieve better neuron reconstructions. In the experiment 2, 7 neuron image datasets are reconstructed and the four difference scores are calculated by comparing the gold standard reconstruction and the reconstructions produced by 6 competing algorithms. Comparing the four difference scores of M-AMST and the other 5 algorithm, we can conclude that M-AMST is able to achieve the best difference score in 3 datasets and get the second-best difference score in the other 2 datasets. We develop a pathway extraction method using a rotating sphere model based on coordinate transformation to improve the weight calculation approach in MST. The experimental results show that M-AMST utilizes the adapted minimum spanning tree algorithm which takes the shape information of neuron into account can achieve better neuron reconstructions. Moreover, M-AMST is able to get good neuron reconstruction in variety of image datasets.

A Distributed Compressive Sensing Scheme for Event Capture in Wireless Visual Sensor Networks

NASA Astrophysics Data System (ADS)

Hou, Meng; Xu, Sen; Wu, Weiling; Lin, Fei

2018-01-01

Image signals which acquired by wireless visual sensor network can be used for specific event capture. This event capture is realized by image processing at the sink node. A distributed compressive sensing scheme is used for the transmission of these image signals from the camera nodes to the sink node. A measurement and joint reconstruction algorithm for these image signals are proposed in this paper. Make advantage of spatial correlation between images within a sensing area, the cluster head node which as the image decoder can accurately co-reconstruct these image signals. The subjective visual quality and the reconstruction error rate are used for the evaluation of reconstructed image quality. Simulation results show that the joint reconstruction algorithm achieves higher image quality at the same image compressive rate than the independent reconstruction algorithm.

Solar Accuracy to the 3/10000 Degree - Continuum Magazine | NREL

Science.gov Websites

Laboratory, where he has developed Solar Position Algorithm software. Photo by Dennis Schroeder, NREL Solar -Pyrheliometer Comparison (NPC), on the deck of NREL's Solar Radiation Research Laboratory. Photo by Dennis

Shading correction assisted iterative cone-beam CT reconstruction

NASA Astrophysics Data System (ADS)

Yang, Chunlin; Wu, Pengwei; Gong, Shutao; Wang, Jing; Lyu, Qihui; Tang, Xiangyang; Niu, Tianye

2017-11-01

Recent advances in total variation (TV) technology enable accurate CT image reconstruction from highly under-sampled and noisy projection data. The standard iterative reconstruction algorithms, which work well in conventional CT imaging, fail to perform as expected in cone beam CT (CBCT) applications, wherein the non-ideal physics issues, including scatter and beam hardening, are more severe. These physics issues result in large areas of shading artifacts and cause deterioration to the piecewise constant property assumed in reconstructed images. To overcome this obstacle, we incorporate a shading correction scheme into low-dose CBCT reconstruction and propose a clinically acceptable and stable three-dimensional iterative reconstruction method that is referred to as the shading correction assisted iterative reconstruction. In the proposed method, we modify the TV regularization term by adding a shading compensation image to the reconstructed image to compensate for the shading artifacts while leaving the data fidelity term intact. This compensation image is generated empirically, using image segmentation and low-pass filtering, and updated in the iterative process whenever necessary. When the compensation image is determined, the objective function is minimized using the fast iterative shrinkage-thresholding algorithm accelerated on a graphic processing unit. The proposed method is evaluated using CBCT projection data of the Catphan© 600 phantom and two pelvis patients. Compared with the iterative reconstruction without shading correction, the proposed method reduces the overall CT number error from around 200 HU to be around 25 HU and increases the spatial uniformity by a factor of 20 percent, given the same number of sparsely sampled projections. A clinically acceptable and stable iterative reconstruction algorithm for CBCT is proposed in this paper. Differing from the existing algorithms, this algorithm incorporates a shading correction scheme into the low-dose CBCT reconstruction and achieves more stable optimization path and more clinically acceptable reconstructed image. The method proposed by us does not rely on prior information and thus is practically attractive to the applications of low-dose CBCT imaging in the clinic.

Tomography by iterative convolution - Empirical study and application to interferometry

NASA Technical Reports Server (NTRS)

Vest, C. M.; Prikryl, I.

1984-01-01

An algorithm for computer tomography has been developed that is applicable to reconstruction from data having incomplete projections because an opaque object blocks some of the probing radiation as it passes through the object field. The algorithm is based on iteration between the object domain and the projection (Radon transform) domain. Reconstructions are computed during each iteration by the well-known convolution method. Although it is demonstrated that this algorithm does not converge, an empirically justified criterion for terminating the iteration when the most accurate estimate has been computed is presented. The algorithm has been studied by using it to reconstruct several different object fields with several different opaque regions. It also has been used to reconstruct aerodynamic density fields from interferometric data recorded in wind tunnel tests.

Research of centroiding algorithms for extended and elongated spot of sodium laser guide star

NASA Astrophysics Data System (ADS)

Shao, Yayun; Zhang, Yudong; Wei, Kai

2016-10-01

Laser guide stars (LGSs) increase the sky coverage of astronomical adaptive optics systems. But spot array obtained by Shack-Hartmann wave front sensors (WFSs) turns extended and elongated, due to the thickness and size limitation of sodium LGS, which affects the accuracy of the wave front reconstruction algorithm. In this paper, we compared three different centroiding algorithms , the Center-of-Gravity (CoG), weighted CoG (WCoG) and Intensity Weighted Centroid (IWC), as well as those accuracies for various extended and elongated spots. In addition, we compared the reconstructed image data from those three algorithms with theoretical results, and proved that WCoG and IWC are the best wave front reconstruction algorithms for extended and elongated spot among all the algorithms.

How many bootstrap replicates are necessary?

PubMed

Pattengale, Nicholas D; Alipour, Masoud; Bininda-Emonds, Olaf R P; Moret, Bernard M E; Stamatakis, Alexandros

2010-03-01

Phylogenetic bootstrapping (BS) is a standard technique for inferring confidence values on phylogenetic trees that is based on reconstructing many trees from minor variations of the input data, trees called replicates. BS is used with all phylogenetic reconstruction approaches, but we focus here on one of the most popular, maximum likelihood (ML). Because ML inference is so computationally demanding, it has proved too expensive to date to assess the impact of the number of replicates used in BS on the relative accuracy of the support values. For the same reason, a rather small number (typically 100) of BS replicates are computed in real-world studies. Stamatakis et al. recently introduced a BS algorithm that is 1 to 2 orders of magnitude faster than previous techniques, while yielding qualitatively comparable support values, making an experimental study possible. In this article, we propose stopping criteria--that is, thresholds computed at runtime to determine when enough replicates have been generated--and we report on the first large-scale experimental study to assess the effect of the number of replicates on the quality of support values, including the performance of our proposed criteria. We run our tests on 17 diverse real-world DNA--single-gene as well as multi-gene--datasets, which include 125-2,554 taxa. We find that our stopping criteria typically stop computations after 100-500 replicates (although the most conservative criterion may continue for several thousand replicates) while producing support values that correlate at better than 99.5% with the reference values on the best ML trees. Significantly, we also find that the stopping criteria can recommend very different numbers of replicates for different datasets of comparable sizes. Our results are thus twofold: (i) they give the first experimental assessment of the effect of the number of BS replicates on the quality of support values returned through BS, and (ii) they validate our proposals for stopping criteria. Practitioners will no longer have to enter a guess nor worry about the quality of support values; moreover, with most counts of replicates in the 100-500 range, robust BS under ML inference becomes computationally practical for most datasets. The complete test suite is available at http://lcbb.epfl.ch/BS.tar.bz2, and BS with our stopping criteria is included in the latest release of RAxML v7.2.5, available at http://wwwkramer.in.tum.de/exelixis/software.html.

The development of a line-scan imaging algorithm for the detection of fecal contamination on leafy geens

NASA Astrophysics Data System (ADS)

Yang, Chun-Chieh; Kim, Moon S.; Chuang, Yung-Kun; Lee, Hoyoung

2013-05-01

This paper reports the development of a multispectral algorithm, using the line-scan hyperspectral imaging system, to detect fecal contamination on leafy greens. Fresh bovine feces were applied to the surfaces of washed loose baby spinach leaves. A hyperspectral line-scan imaging system was used to acquire hyperspectral fluorescence images of the contaminated leaves. Hyperspectral image analysis resulted in the selection of the 666 nm and 688 nm wavebands for a multispectral algorithm to rapidly detect feces on leafy greens, by use of the ratio of fluorescence intensities measured at those two wavebands (666 nm over 688 nm). The algorithm successfully distinguished most of the lowly diluted fecal spots (0.05 g feces/ml water and 0.025 g feces/ml water) and some of the highly diluted spots (0.0125 g feces/ml water and 0.00625 g feces/ml water) from the clean spinach leaves. The results showed the potential of the multispectral algorithm with line-scan imaging system for application to automated food processing lines for food safety inspection of leafy green vegetables.

RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction.

PubMed

Abdel-Sayed, Michael M; Khattab, Ahmed; Abu-Elyazeed, Mohamed F

2016-11-01

Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Compressed sensing initially adopted [Formula: see text] minimization for signal reconstruction which is computationally expensive. Several greedy recovery algorithms have been recently proposed for signal reconstruction at a lower computational complexity compared to the optimal [Formula: see text] minimization, while maintaining a good reconstruction accuracy. In this paper, the Reduced-set Matching Pursuit (RMP) greedy recovery algorithm is proposed for compressed sensing. Unlike existing approaches which either select too many or too few values per iteration, RMP aims at selecting the most sufficient number of correlation values per iteration, which improves both the reconstruction time and error. Furthermore, RMP prunes the estimated signal, and hence, excludes the incorrectly selected values. The RMP algorithm achieves a higher reconstruction accuracy at a significantly low computational complexity compared to existing greedy recovery algorithms. It is even superior to [Formula: see text] minimization in terms of the normalized time-error product, a new metric introduced to measure the trade-off between the reconstruction time and error. RMP superior performance is illustrated with both noiseless and noisy samples.

Microgrids | Grid Modernization | NREL

Science.gov Websites

algorithms for microgrid integration Controller hardware-in-the-loop testing, where the physical controller interacts with a model of the microgrid and associated power devices Power hardware-in-the-loop testing of operation was validated in a power hardware-in-the-loop experiment using a programmable DC power supply to

Maximum likelihood estimates, from censored data, for mixed-Weibull distributions

NASA Astrophysics Data System (ADS)

Jiang, Siyuan; Kececioglu, Dimitri

1992-06-01

A new algorithm for estimating the parameters of mixed-Weibull distributions from censored data is presented. The algorithm follows the principle of maximum likelihood estimate (MLE) through the expectation and maximization (EM) algorithm, and it is derived for both postmortem and nonpostmortem time-to-failure data. It is concluded that the concept of the EM algorithm is easy to understand and apply (only elementary statistics and calculus are required). The log-likelihood function cannot decrease after an EM sequence; this important feature was observed in all of the numerical calculations. The MLEs of the nonpostmortem data were obtained successfully for mixed-Weibull distributions with up to 14 parameters in a 5-subpopulation, mixed-Weibull distribution. Numerical examples indicate that some of the log-likelihood functions of the mixed-Weibull distributions have multiple local maxima; therefore, the algorithm should start at several initial guesses of the parameter set.

Mapping chemicals in air using an environmental CAT scanning system: evaluation of algorithms

NASA Astrophysics Data System (ADS)

Samanta, A.; Todd, L. A.

A new technique is being developed which creates near real-time maps of chemical concentrations in air for environmental and occupational environmental applications. This technique, we call Environmental CAT Scanning, combines the real-time measuring technique of open-path Fourier transform infrared spectroscopy with the mapping capabilitites of computed tomography to produce two-dimensional concentration maps. With this system, a network of open-path measurements is obtained over an area; measurements are then processed using a tomographic algorithm to reconstruct the concentrations. This research focussed on the process of evaluating and selecting appropriate reconstruction algorithms, for use in the field, by using test concentration data from both computer simultation and laboratory chamber studies. Four algorithms were tested using three types of data: (1) experimental open-path data from studies that used a prototype opne-path Fourier transform/computed tomography system in an exposure chamber; (2) synthetic open-path data generated from maps created by kriging point samples taken in the chamber studies (in 1), and; (3) synthetic open-path data generated using a chemical dispersion model to create time seires maps. The iterative algorithms used to reconstruct the concentration data were: Algebraic Reconstruction Technique without Weights (ART1), Algebraic Reconstruction Technique with Weights (ARTW), Maximum Likelihood with Expectation Maximization (MLEM) and Multiplicative Algebraic Reconstruction Technique (MART). Maps were evaluated quantitatively and qualitatively. In general, MART and MLEM performed best, followed by ARTW and ART1. However, algorithm performance varied under different contaminant scenarios. This study showed the importance of using a variety of maps, particulary those generated using dispersion models. The time series maps provided a more rigorous test of the algorithms and allowed distinctions to be made among the algorithms. A comprehensive evaluation of algorithms, for the environmental application of tomography, requires the use of a battery of test concentration data before field implementation, which models reality and tests the limits of the algorithms.

LBP-based penalized weighted least-squares approach to low-dose cone-beam computed tomography reconstruction

NASA Astrophysics Data System (ADS)

Ma, Ming; Wang, Huafeng; Liu, Yan; Zhang, Hao; Gu, Xianfeng; Liang, Zhengrong

2014-03-01

Cone-beam computed tomography (CBCT) has attracted growing interest of researchers in image reconstruction. The mAs level of the X-ray tube current, in practical application of CBCT, is mitigated in order to reduce the CBCT dose. The lowering of the X-ray tube current, however, results in the degradation of image quality. Thus, low-dose CBCT image reconstruction is in effect a noise problem. To acquire clinically acceptable quality of image, and keep the X-ray tube current as low as achievable in the meanwhile, some penalized weighted least-squares (PWLS)-based image reconstruction algorithms have been developed. One representative strategy in previous work is to model the prior information for solution regularization using an anisotropic penalty term. To enhance the edge preserving and noise suppressing in a finer scale, a novel algorithm combining the local binary pattern (LBP) with penalized weighted leastsquares (PWLS), called LBP-PWLS-based image reconstruction algorithm, is proposed in this work. The proposed LBP-PWLS-based algorithm adaptively encourages strong diffusion on the local spot/flat region around a voxel and less diffusion on edge/corner ones by adjusting the penalty for cost function, after the LBP is utilized to detect the region around the voxel as spot, flat and edge ones. The LBP-PWLS-based reconstruction algorithm was evaluated using the sinogram data acquired by a clinical CT scanner from the CatPhan® 600 phantom. Experimental results on the noiseresolution tradeoff measurement and other quantitative measurements demonstrated its feasibility and effectiveness in edge preserving and noise suppressing in comparison with a previous PWLS reconstruction algorithm.

Evaluation of accelerated iterative x-ray CT image reconstruction using floating point graphics hardware.

PubMed

Kole, J S; Beekman, F J

2006-02-21

Statistical reconstruction methods offer possibilities to improve image quality as compared with analytical methods, but current reconstruction times prohibit routine application in clinical and micro-CT. In particular, for cone-beam x-ray CT, the use of graphics hardware has been proposed to accelerate the forward and back-projection operations, in order to reduce reconstruction times. In the past, wide application of this texture hardware mapping approach was hampered owing to limited intrinsic accuracy. Recently, however, floating point precision has become available in the latest generation commodity graphics cards. In this paper, we utilize this feature to construct a graphics hardware accelerated version of the ordered subset convex reconstruction algorithm. The aims of this paper are (i) to study the impact of using graphics hardware acceleration for statistical reconstruction on the reconstructed image accuracy and (ii) to measure the speed increase one can obtain by using graphics hardware acceleration. We compare the unaccelerated algorithm with the graphics hardware accelerated version, and for the latter we consider two different interpolation techniques. A simulation study of a micro-CT scanner with a mathematical phantom shows that at almost preserved reconstructed image accuracy, speed-ups of a factor 40 to 222 can be achieved, compared with the unaccelerated algorithm, and depending on the phantom and detector sizes. Reconstruction from physical phantom data reconfirms the usability of the accelerated algorithm for practical cases.

Optimization of digital breast tomosynthesis (DBT) acquisition parameters for human observers: effect of reconstruction algorithms

NASA Astrophysics Data System (ADS)

Zeng, Rongping; Badano, Aldo; Myers, Kyle J.

2017-04-01

We showed in our earlier work that the choice of reconstruction methods does not affect the optimization of DBT acquisition parameters (angular span and number of views) using simulated breast phantom images in detecting lesions with a channelized Hotelling observer (CHO). In this work we investigate whether the model-observer based conclusion is valid when using humans to interpret images. We used previously generated DBT breast phantom images and recruited human readers to find the optimal geometry settings associated with two reconstruction algorithms, filtered back projection (FBP) and simultaneous algebraic reconstruction technique (SART). The human reader results show that image quality trends as a function of the acquisition parameters are consistent between FBP and SART reconstructions. The consistent trends confirm that the optimization of DBT system geometry is insensitive to the choice of reconstruction algorithm. The results also show that humans perform better in SART reconstructed images than in FBP reconstructed images. In addition, we applied CHOs with three commonly used channel models, Laguerre-Gauss (LG) channels, square (SQR) channels and sparse difference-of-Gaussian (sDOG) channels. We found that LG channels predict human performance trends better than SQR and sDOG channel models for the task of detecting lesions in tomosynthesis backgrounds. Overall, this work confirms that the choice of reconstruction algorithm is not critical for optimizing DBT system acquisition parameters.

Reconstructing householder vectors from Tall-Skinny QR

DOE PAGES

Ballard, Grey Malone; Demmel, James; Grigori, Laura; ...

2015-08-05

The Tall-Skinny QR (TSQR) algorithm is more communication efficient than the standard Householder algorithm for QR decomposition of matrices with many more rows than columns. However, TSQR produces a different representation of the orthogonal factor and therefore requires more software development to support the new representation. Further, implicitly applying the orthogonal factor to the trailing matrix in the context of factoring a square matrix is more complicated and costly than with the Householder representation. We show how to perform TSQR and then reconstruct the Householder vector representation with the same asymptotic communication efficiency and little extra computational cost. We demonstratemore » the high performance and numerical stability of this algorithm both theoretically and empirically. The new Householder reconstruction algorithm allows us to design more efficient parallel QR algorithms, with significantly lower latency cost compared to Householder QR and lower bandwidth and latency costs compared with Communication-Avoiding QR (CAQR) algorithm. Experiments on supercomputers demonstrate the benefits of the communication cost improvements: in particular, our experiments show substantial improvements over tuned library implementations for tall-and-skinny matrices. Furthermore, we also provide algorithmic improvements to the Householder QR and CAQR algorithms, and we investigate several alternatives to the Householder reconstruction algorithm that sacrifice guarantees on numerical stability in some cases in order to obtain higher performance.« less

Interval-based reconstruction for uncertainty quantification in PET

NASA Astrophysics Data System (ADS)

Kucharczak, Florentin; Loquin, Kevin; Buvat, Irène; Strauss, Olivier; Mariano-Goulart, Denis

2018-02-01

A new directed interval-based tomographic reconstruction algorithm, called non-additive interval based expectation maximization (NIBEM) is presented. It uses non-additive modeling of the forward operator that provides intervals instead of single-valued projections. The detailed approach is an extension of the maximum likelihood—expectation maximization algorithm based on intervals. The main motivation for this extension is that the resulting intervals have appealing properties for estimating the statistical uncertainty associated with the reconstructed activity values. After reviewing previously published theoretical concepts related to interval-based projectors, this paper describes the NIBEM algorithm and gives examples that highlight the properties and advantages of this interval valued reconstruction.

Wind velocity profile reconstruction from intensity fluctuations of a plane wave propagating in a turbulent atmosphere.

PubMed

Banakh, V A; Marakasov, D A

2007-08-01

Reconstruction of a wind profile based on the statistics of plane-wave intensity fluctuations in a turbulent atmosphere is considered. The algorithm for wind profile retrieval from the spatiotemporal spectrum of plane-wave weak intensity fluctuations is described, and the results of end-to-end computer experiments on wind profiling based on the developed algorithm are presented. It is shown that the reconstructing algorithm allows retrieval of a wind profile from turbulent plane-wave intensity fluctuations with acceptable accuracy.

Evaluation of a high resolution silicon PET insert module

NASA Astrophysics Data System (ADS)

Grkovski, Milan; Brzezinski, Karol; Cindro, Vladimir; Clinthorne, Neal H.; Kagan, Harris; Lacasta, Carlos; Mikuž, Marko; Solaz, Carles; Studen, Andrej; Weilhammer, Peter; Žontar, Dejan

2015-07-01

Conventional PET systems can be augmented with additional detectors placed in close proximity of the region of interest. We developed a high resolution PET insert module to evaluate the added benefit of such a combination. The insert module consists of two back-to-back 1 mm thick silicon sensors, each segmented into 1040 1 mm2 pads arranged in a 40 by 26 array. A set of 16 VATAGP7.1 ASICs and a custom assembled data acquisition board were used to read out the signal from the insert module. Data were acquired in slice (2D) geometry with a Jaszczak phantom (rod diameters of 1.2-4.8 mm) filled with 18F-FDG and the images were reconstructed with ML-EM method. Both data with full and limited angular coverage from the insert module were considered and three types of coincidence events were combined. The ratio of high-resolution data that substantially improves quality of the reconstructed image for the region near the surface of the insert module was estimated to be about 4%. Results from our previous studies suggest that such ratio could be achieved at a moderate technological expense by using an equivalent of two insert modules (an effective sensor thickness of 4 mm).

Tissue Expansion Using Hyaluronic Acid Filler for Single-Stage Ear Reconstruction: A Novel Concept for Difficult Areas.

PubMed

Inbal, Amir; Lemelman, Benjamin T; Millet, Eran; Greensmith, Andrew

2017-10-16

Auricular reconstruction is one of the most challenging procedures in plastic surgery. An adequate skin envelope is essential for cartilage framework coverage, yet few good options exist without additional surgery. We propose a novel method for minimally invasive tissue expansion, using hyaluronic acid (HA) filler to allow for single-stage ear reconstruction. To introduce the novel concept of HA filler for tissue expansion in ear reconstruction, and as an alternative to traditional expansion techniques. Macrolane is a large particle HA gel developed for large volume restoration. Expansion of the non-hair-bearing mastoid skin was performed in our clinic weekly or every other week. Final expansion was completed one week prior to reconstructive surgery. Tissue from one patient's expanded pocket was sent for histological analysis. Ten patients underwent single-stage auricular reconstruction with preoperative expansion. Injection sessions ranged from 7 to 13 (mean, 9.7). Mean injected volume per session was 2.03 mL per patient, for an average total of 19.8 mL (range, 14.5-30 mL). There were no major complications. One minor complication required removal of exposed wire from the antihelix in the office. Hematoxylin and eosin stain revealed similar histology to that seen with traditional expanders. This novel expansion technique using serial HA injections allowed for optimized skin coverage in single-stage ear reconstruction. The concept of tissue expansion using HA filler is a new frontier for research that may be applicable to other arenas of reconstruction. 4. © 2017 The American Society for Aesthetic Plastic Surgery, Inc. Reprints and permission: journals.permissions@oup.com

Interleaved diffusion-weighted EPI improved by adaptive partial-Fourier and multi-band multiplexed sensitivity-encoding reconstruction

PubMed Central

Chang, Hing-Chiu; Guhaniyogi, Shayan; Chen, Nan-kuei

2014-01-01

Purpose We report a series of techniques to reliably eliminate artifacts in interleaved echo-planar imaging (EPI) based diffusion weighted imaging (DWI). Methods First, we integrate the previously reported multiplexed sensitivity encoding (MUSE) algorithm with a new adaptive Homodyne partial-Fourier reconstruction algorithm, so that images reconstructed from interleaved partial-Fourier DWI data are free from artifacts even in the presence of either a) motion-induced k-space energy peak displacement, or b) susceptibility field gradient induced fast phase changes. Second, we generalize the previously reported single-band MUSE framework to multi-band MUSE, so that both through-plane and in-plane aliasing artifacts in multi-band multi-shot interleaved DWI data can be effectively eliminated. Results The new adaptive Homodyne-MUSE reconstruction algorithm reliably produces high-quality and high-resolution DWI, eliminating residual artifacts in images reconstructed with previously reported methods. Furthermore, the generalized MUSE algorithm is compatible with multi-band and high-throughput DWI. Conclusion The integration of the multi-band and adaptive Homodyne-MUSE algorithms significantly improves the spatial-resolution, image quality, and scan throughput of interleaved DWI. We expect that the reported reconstruction framework will play an important role in enabling high-resolution DWI for both neuroscience research and clinical uses. PMID:24925000

The effects of navigator distortion and noise level on interleaved EPI DWI reconstruction: a comparison between image- and k-space-based method.

PubMed

Dai, Erpeng; Zhang, Zhe; Ma, Xiaodong; Dong, Zijing; Li, Xuesong; Xiong, Yuhui; Yuan, Chun; Guo, Hua

2018-03-23

To study the effects of 2D navigator distortion and noise level on interleaved EPI (iEPI) DWI reconstruction, using either the image- or k-space-based method. The 2D navigator acquisition was adjusted by reducing its echo spacing in the readout direction and undersampling in the phase encoding direction. A POCS-based reconstruction using image-space sampling function (IRIS) algorithm (POCSIRIS) was developed to reduce the impact of navigator distortion. POCSIRIS was then compared with the original IRIS algorithm and a SPIRiT-based k-space algorithm, under different navigator distortion and noise levels. Reducing the navigator distortion can improve the reconstruction of iEPI DWI. The proposed POCSIRIS and SPIRiT-based algorithms are more tolerable to different navigator distortion levels, compared to the original IRIS algorithm. SPIRiT may be hindered by low SNR of the navigator. Multi-shot iEPI DWI reconstruction can be improved by reducing the 2D navigator distortion. Different reconstruction methods show variable sensitivity to navigator distortion or noise levels. Furthermore, the findings can be valuable in applications such as simultaneous multi-slice accelerated iEPI DWI and multi-slab diffusion imaging. © 2018 International Society for Magnetic Resonance in Medicine.

Event-by-event PET image reconstruction using list-mode origin ensembles algorithm

NASA Astrophysics Data System (ADS)

Andreyev, Andriy

2016-03-01

There is a great demand for real time or event-by-event (EBE) image reconstruction in emission tomography. Ideally, as soon as event has been detected by the acquisition electronics, it needs to be used in the image reconstruction software. This would greatly speed up the image reconstruction since most of the data will be processed and reconstructed while the patient is still undergoing the scan. Unfortunately, the current industry standard is that the reconstruction of the image would not start until all the data for the current image frame would be acquired. Implementing an EBE reconstruction for MLEM family of algorithms is possible, but not straightforward as multiple (computationally expensive) updates to the image estimate are required. In this work an alternative Origin Ensembles (OE) image reconstruction algorithm for PET imaging is converted to EBE mode and is investigated whether it is viable alternative for real-time image reconstruction. In OE algorithm all acquired events are seen as points that are located somewhere along the corresponding line-of-responses (LORs), together forming a point cloud. Iteratively, with a multitude of quasi-random shifts following the likelihood function the point cloud converges to a reflection of an actual radiotracer distribution with the degree of accuracy that is similar to MLEM. New data can be naturally added into the point cloud. Preliminary results with simulated data show little difference between regular reconstruction and EBE mode, proving the feasibility of the proposed approach.

ECG-gated interventional cardiac reconstruction for non-periodic motion.

PubMed

Rohkohl, Christopher; Lauritsch, Günter; Biller, Lisa; Hornegger, Joachim

2010-01-01

The 3-D reconstruction of cardiac vasculature using C-arm CT is an active and challenging field of research. In interventional environments patients often do have arrhythmic heart signals or cannot hold breath during the complete data acquisition. This important group of patients cannot be reconstructed with current approaches that do strongly depend on a high degree of cardiac motion periodicity for working properly. In a last year's MICCAI contribution a first algorithm was presented that is able to estimate non-periodic 4-D motion patterns. However, to some degree that algorithm still depends on periodicity, as it requires a prior image which is obtained using a simple ECG-gated reconstruction. In this work we aim to provide a solution to this problem by developing a motion compensated ECG-gating algorithm. It is built upon a 4-D time-continuous affine motion model which is capable of compactly describing highly non-periodic motion patterns. A stochastic optimization scheme is derived which minimizes the error between the measured projection data and the forward projection of the motion compensated reconstruction. For evaluation, the algorithm is applied to 5 datasets of the left coronary arteries of patients that have ignored the breath hold command and/or had arrhythmic heart signals during the data acquisition. By applying the developed algorithm the average visibility of the vessel segments could be increased by 27%. The results show that the proposed algorithm provides excellent reconstruction quality in cases where classical approaches fail. The algorithm is highly parallelizable and a clinically feasible runtime of under 4 minutes is achieved using modern graphics card hardware.

A Two-Stage Reconstruction Processor for Human Detection in Compressive Sensing CMOS Radar

PubMed Central

Tsao, Kuei-Chi; Lee, Ling; Chu, Ta-Shun

2018-01-01

Complementary metal-oxide-semiconductor (CMOS) radar has recently gained much research attraction because small and low-power CMOS devices are very suitable for deploying sensing nodes in a low-power wireless sensing system. This study focuses on the signal processing of a wireless CMOS impulse radar system that can detect humans and objects in the home-care internet-of-things sensing system. The challenges of low-power CMOS radar systems are the weakness of human signals and the high computational complexity of the target detection algorithm. The compressive sensing-based detection algorithm can relax the computational costs by avoiding the utilization of matched filters and reducing the analog-to-digital converter bandwidth requirement. The orthogonal matching pursuit (OMP) is one of the popular signal reconstruction algorithms for compressive sensing radar; however, the complexity is still very high because the high resolution of human respiration leads to high-dimension signal reconstruction. Thus, this paper proposes a two-stage reconstruction algorithm for compressive sensing radar. The proposed algorithm not only has lower complexity than the OMP algorithm by 75% but also achieves better positioning performance than the OMP algorithm especially in noisy environments. This study also designed and implemented the algorithm by using Vertex-7 FPGA chip (Xilinx, San Jose, CA, USA). The proposed reconstruction processor can support the 256×13 real-time radar image display with a throughput of 28.2 frames per second. PMID:29621170

TU-A-12A-07: CT-Based Biomarkers to Characterize Lung Lesion: Effects of CT Dose, Slice Thickness and Reconstruction Algorithm Based Upon a Phantom Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, B; Tan, Y; Tsai, W

2014-06-15

Purpose: Radiogenomics promises the ability to study cancer tumor genotype from the phenotype obtained through radiographic imaging. However, little attention has been paid to the sensitivity of image features, the image-based biomarkers, to imaging acquisition techniques. This study explores the impact of CT dose, slice thickness and reconstruction algorithm on measuring image features using a thorax phantom. Methods: Twentyfour phantom lesions of known volume (1 and 2mm), shape (spherical, elliptical, lobular and spicular) and density (-630, -10 and +100 HU) were scanned on a GE VCT at four doses (25, 50, 100, and 200 mAs). For each scan, six imagemore » series were reconstructed at three slice thicknesses of 5, 2.5 and 1.25mm with continuous intervals, using the lung and standard reconstruction algorithms. The lesions were segmented with an in-house 3D algorithm. Fifty (50) image features representing lesion size, shape, edge, and density distribution/texture were computed. Regression method was employed to analyze the effect of CT dose, slice of thickness and reconstruction algorithm on these features adjusting 3 confounding factors (size, density and shape of phantom lesions). Results: The coefficients of CT dose, slice thickness and reconstruction algorithm are presented in Table 1 in the supplementary material. No significant difference was found between the image features calculated on low dose CT scans (25mAs and 50mAs). About 50% texture features were found statistically different between low doses and high doses (100 and 200mAs). Significant differences were found for almost all features when calculated on 1.25mm, 2.5mm, and 5mm slice thickness images. Reconstruction algorithms significantly affected all density-based image features, but not morphological features. Conclusions: There is a great need to standardize the CT imaging protocols for radiogenomics study because CT dose, slice thickness and reconstruction algorithm impact quantitative image features to various degrees as our study has shown.« less

Level-set-based reconstruction algorithm for EIT lung images: first clinical results.

PubMed

Rahmati, Peyman; Soleimani, Manuchehr; Pulletz, Sven; Frerichs, Inéz; Adler, Andy

2012-05-01

We show the first clinical results using the level-set-based reconstruction algorithm for electrical impedance tomography (EIT) data. The level-set-based reconstruction method (LSRM) allows the reconstruction of non-smooth interfaces between image regions, which are typically smoothed by traditional voxel-based reconstruction methods (VBRMs). We develop a time difference formulation of the LSRM for 2D images. The proposed reconstruction method is applied to reconstruct clinical EIT data of a slow flow inflation pressure-volume manoeuvre in lung-healthy and adult lung-injury patients. Images from the LSRM and the VBRM are compared. The results show comparable reconstructed images, but with an improved ability to reconstruct sharp conductivity changes in the distribution of lung ventilation using the LSRM.

4D-PET reconstruction using a spline-residue model with spatial and temporal roughness penalties

NASA Astrophysics Data System (ADS)

Ralli, George P.; Chappell, Michael A.; McGowan, Daniel R.; Sharma, Ricky A.; Higgins, Geoff S.; Fenwick, John D.

2018-05-01

4D reconstruction of dynamic positron emission tomography (dPET) data can improve the signal-to-noise ratio in reconstructed image sequences by fitting smooth temporal functions to the voxel time-activity-curves (TACs) during the reconstruction, though the optimal choice of function remains an open question. We propose a spline-residue model, which describes TACs as weighted sums of convolutions of the arterial input function with cubic B-spline basis functions. Convolution with the input function constrains the spline-residue model at early time-points, potentially enhancing noise suppression in early time-frames, while still allowing a wide range of TAC descriptions over the entire imaged time-course, thus limiting bias. Spline-residue based 4D-reconstruction is compared to that of a conventional (non-4D) maximum a posteriori (MAP) algorithm, and to 4D-reconstructions based on adaptive-knot cubic B-splines, the spectral model and an irreversible two-tissue compartment (‘2C3K’) model. 4D reconstructions were carried out using a nested-MAP algorithm including spatial and temporal roughness penalties. The algorithms were tested using Monte-Carlo simulated scanner data, generated for a digital thoracic phantom with uptake kinetics based on a dynamic [18F]-Fluromisonidazole scan of a non-small cell lung cancer patient. For every algorithm, parametric maps were calculated by fitting each voxel TAC within a sub-region of the reconstructed images with the 2C3K model. Compared to conventional MAP reconstruction, spline-residue-based 4D reconstruction achieved  >50% improvements for five of the eight combinations of the four kinetics parameters for which parametric maps were created with the bias and noise measures used to analyse them, and produced better results for 5/8 combinations than any of the other reconstruction algorithms studied, while spectral model-based 4D reconstruction produced the best results for 2/8. 2C3K model-based 4D reconstruction generated the most biased parametric maps. Inclusion of a temporal roughness penalty function improved the performance of 4D reconstruction based on the cubic B-spline, spectral and spline-residue models.

Improved Understanding of In Situ Chemical Oxidation. Technical Objective 2: Soil Reactivity

DTIC Science & Technology

2009-05-01

each reaction tube. The reagents ( sulfuric acid /potassium dichromate/mercury sulfate ) were prepared following standard methods (APHA, 1998). When...reaction tubes. A volume (~20 mL) of potassium dichromate digestion solution (potassium dichromate (BDH Laboratories), sulfuric acid (EM Science...and mercury sulphate (EM Science)) and a sulfuric acid reagent solution ( sulfuric acid (EM Science) and silver sulphate (Alfa Aesar)) were added to

Functional Validation and Comparison Framework for EIT Lung Imaging

PubMed Central

Meybohm, Patrick; Weiler, Norbert; Frerichs, Inéz; Adler, Andy

2014-01-01

Introduction Electrical impedance tomography (EIT) is an emerging clinical tool for monitoring ventilation distribution in mechanically ventilated patients, for which many image reconstruction algorithms have been suggested. We propose an experimental framework to assess such algorithms with respect to their ability to correctly represent well-defined physiological changes. We defined a set of clinically relevant ventilation conditions and induced them experimentally in 8 pigs by controlling three ventilator settings (tidal volume, positive end-expiratory pressure and the fraction of inspired oxygen). In this way, large and discrete shifts in global and regional lung air content were elicited. Methods We use the framework to compare twelve 2D EIT reconstruction algorithms, including backprojection (the original and still most frequently used algorithm), GREIT (a more recent consensus algorithm for lung imaging), truncated singular value decomposition (TSVD), several variants of the one-step Gauss-Newton approach and two iterative algorithms. We consider the effects of using a 3D finite element model, assuming non-uniform background conductivity, noise modeling, reconstructing for electrode movement, total variation (TV) reconstruction, robust error norms, smoothing priors, and using difference vs. normalized difference data. Results and Conclusions Our results indicate that, while variation in appearance of images reconstructed from the same data is not negligible, clinically relevant parameters do not vary considerably among the advanced algorithms. Among the analysed algorithms, several advanced algorithms perform well, while some others are significantly worse. Given its vintage and ad-hoc formulation backprojection works surprisingly well, supporting the validity of previous studies in lung EIT. PMID:25110887

Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion

PubMed Central

Bone, Daniel; Bishop, Somer; Black, Matthew P.; Goodwin, Matthew S.; Lord, Catherine; Narayanan, Shrikanth S.

2016-01-01

Background Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely-used ASD screening and diagnostic tools. Methods The data consisted of Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non-ASD developmental or psychiatric disorders (DD), split at age 10. Algorithms were created via a robust ML classifier, support vector machine (SVM), while targeting best-estimate clinical diagnosis of ASD vs. non-ASD. Parameter settings were tuned in multiple levels of cross-validation. Results The created algorithms were more effective (higher performing) than current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near-peak performance with five or fewer codes). Results from ML-based fusion of ADI-R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes. Conclusions ML is useful for creating robust, customizable instrument algorithms. In a unique dataset comprised of controls with other difficulties, our findings highlight limitations of current caregiver-report instruments and indicate possible avenues for improving ASD screening and diagnostic tools. PMID:27090613

Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion.

PubMed

Bone, Daniel; Bishop, Somer L; Black, Matthew P; Goodwin, Matthew S; Lord, Catherine; Narayanan, Shrikanth S

2016-08-01

Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools. The data consisted of Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non-ASD developmental or psychiatric disorders, split at age 10. Algorithms were created via a robust ML classifier, support vector machine, while targeting best-estimate clinical diagnosis of ASD versus non-ASD. Parameter settings were tuned in multiple levels of cross-validation. The created algorithms were more effective (higher performing) than the current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near-peak performance with five or fewer codes). Results from ML-based fusion of ADI-R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes. ML is useful for creating robust, customizable instrument algorithms. In a unique dataset comprised of controls with other difficulties, our findings highlight the limitations of current caregiver-report instruments and indicate possible avenues for improving ASD screening and diagnostic tools. © 2016 Association for Child and Adolescent Mental Health.

CT brush and CancerZap!: two video games for computed tomography dose minimization.

PubMed

Alvare, Graham; Gordon, Richard

2015-05-12

X-ray dose from computed tomography (CT) scanners has become a significant public health concern. All CT scanners spray x-ray photons across a patient, including those using compressive sensing algorithms. New technologies make it possible to aim x-ray beams where they are most needed to form a diagnostic or screening image. We have designed a computer game, CT Brush, that takes advantage of this new flexibility. It uses a standard MART algorithm (Multiplicative Algebraic Reconstruction Technique), but with a user defined dynamically selected subset of the rays. The image appears as the player moves the CT brush over an initially blank scene, with dose accumulating with every "mouse down" move. The goal is to find the "tumor" with as few moves (least dose) as possible. We have successfully implemented CT Brush in Java and made it available publicly, requesting crowdsourced feedback on improving the open source code. With this experience, we also outline a "shoot 'em up game" CancerZap! for photon limited CT. We anticipate that human computing games like these, analyzed by methods similar to those used to understand eye tracking, will lead to new object dependent CT algorithms that will require significantly less dose than object independent nonlinear and compressive sensing algorithms that depend on sprayed photons. Preliminary results suggest substantial dose reduction is achievable.

Anisotropic conductivity imaging with MREIT using equipotential projection algorithm.

PubMed

Değirmenci, Evren; Eyüboğlu, B Murat

2007-12-21

Magnetic resonance electrical impedance tomography (MREIT) combines magnetic flux or current density measurements obtained by magnetic resonance imaging (MRI) and surface potential measurements to reconstruct images of true conductivity with high spatial resolution. Most of the biological tissues have anisotropic conductivity; therefore, anisotropy should be taken into account in conductivity image reconstruction. Almost all of the MREIT reconstruction algorithms proposed to date assume isotropic conductivity distribution. In this study, a novel MREIT image reconstruction algorithm is proposed to image anisotropic conductivity. Relative anisotropic conductivity values are reconstructed iteratively, using only current density measurements without any potential measurement. In order to obtain true conductivity values, only either one potential or conductivity measurement is sufficient to determine a scaling factor. The proposed technique is evaluated on simulated data for isotropic and anisotropic conductivity distributions, with and without measurement noise. Simulation results show that the images of both anisotropic and isotropic conductivity distributions can be reconstructed successfully.

Comparisons of hybrid radiosity-diffusion model and diffusion equation for bioluminescence tomography in cavity cancer detection

NASA Astrophysics Data System (ADS)

Chen, Xueli; Yang, Defu; Qu, Xiaochao; Hu, Hao; Liang, Jimin; Gao, Xinbo; Tian, Jie

2012-06-01

Bioluminescence tomography (BLT) has been successfully applied to the detection and therapeutic evaluation of solid cancers. However, the existing BLT reconstruction algorithms are not accurate enough for cavity cancer detection because of neglecting the void problem. Motivated by the ability of the hybrid radiosity-diffusion model (HRDM) in describing the light propagation in cavity organs, an HRDM-based BLT reconstruction algorithm was provided for the specific problem of cavity cancer detection. HRDM has been applied to optical tomography but is limited to simple and regular geometries because of the complexity in coupling the boundary between the scattering and void region. In the provided algorithm, HRDM was first applied to three-dimensional complicated and irregular geometries and then employed as the forward light transport model to describe the bioluminescent light propagation in tissues. Combining HRDM with the sparse reconstruction strategy, the cavity cancer cells labeled with bioluminescent probes can be more accurately reconstructed. Compared with the diffusion equation based reconstruction algorithm, the essentiality and superiority of the HRDM-based algorithm were demonstrated with simulation, phantom and animal studies. An in vivo gastric cancer-bearing nude mouse experiment was conducted, whose results revealed the ability and feasibility of the HRDM-based algorithm in the biomedical application of gastric cancer detection.

Comparisons of hybrid radiosity-diffusion model and diffusion equation for bioluminescence tomography in cavity cancer detection.

PubMed

Chen, Xueli; Yang, Defu; Qu, Xiaochao; Hu, Hao; Liang, Jimin; Gao, Xinbo; Tian, Jie

2012-06-01

Bioluminescence tomography (BLT) has been successfully applied to the detection and therapeutic evaluation of solid cancers. However, the existing BLT reconstruction algorithms are not accurate enough for cavity cancer detection because of neglecting the void problem. Motivated by the ability of the hybrid radiosity-diffusion model (HRDM) in describing the light propagation in cavity organs, an HRDM-based BLT reconstruction algorithm was provided for the specific problem of cavity cancer detection. HRDM has been applied to optical tomography but is limited to simple and regular geometries because of the complexity in coupling the boundary between the scattering and void region. In the provided algorithm, HRDM was first applied to three-dimensional complicated and irregular geometries and then employed as the forward light transport model to describe the bioluminescent light propagation in tissues. Combining HRDM with the sparse reconstruction strategy, the cavity cancer cells labeled with bioluminescent probes can be more accurately reconstructed. Compared with the diffusion equation based reconstruction algorithm, the essentiality and superiority of the HRDM-based algorithm were demonstrated with simulation, phantom and animal studies. An in vivo gastric cancer-bearing nude mouse experiment was conducted, whose results revealed the ability and feasibility of the HRDM-based algorithm in the biomedical application of gastric cancer detection.

Three-Dimensional Weighting in Cone Beam FBP Reconstruction and Its Transformation Over Geometries.

PubMed

Tang, Shaojie; Huang, Kuidong; Cheng, Yunyong; Niu, Tianye; Tang, Xiangyang

2018-06-01

With substantially increased number of detector rows in multidetector CT (MDCT), axial scan with projection data acquired along a circular source trajectory has become the method-of-choice in increasing clinical applications. Recognizing the practical relevance of image reconstruction directly from the projection data acquired in the native cone beam (CB) geometry, especially in scenarios wherein the most achievable in-plane resolution is desirable, we present a three-dimensional (3-D) weighted CB-FBP algorithm in such geometry in this paper. We start the algorithm's derivation in the cone-parallel geometry. Via changing of variables, taking the Jacobian into account and making heuristic and empirical assumptions, we arrive at the formulas for 3-D weighted image reconstruction in the native CB geometry. Using the projection data simulated by computer and acquired by an MDCT scanner, we evaluate and verify performance of the proposed algorithm for image reconstruction directly from projection data acquired in the native CB geometry. The preliminary data show that the proposed algorithm performs as well as the 3-D weighted CB-FBP algorithm in the cone-parallel geometry. The proposed algorithm is anticipated to find its utility in extensive clinical and preclinical applications wherein the reconstruction of images in the native CB geometry, i.e., the geometry for data acquisition, is of relevance.

Tomographic reconstruction of tracer gas concentration profiles in a room with the use of a single OP-FTIR and two iterative algorithms: ART and PWLS.

PubMed

Park, D Y; Fessler, J A; Yost, M G; Levine, S P

2000-03-01

Computed tomographic (CT) reconstructions of air contaminant concentration fields were conducted in a room-sized chamber employing a single open-path Fourier transform infrared (OP-FTIR) instrument and a combination of 52 flat mirrors and 4 retroreflectors. A total of 56 beam path data were repeatedly collected for around 1 hr while maintaining a stable concentration gradient. The plane of the room was divided into 195 pixels (13 x 15) for reconstruction. The algebraic reconstruction technique (ART) failed to reconstruct the original concentration gradient patterns for most cases. These poor results were caused by the "highly underdetermined condition" in which the number of unknown values (156 pixels) exceeds that of known data (56 path integral concentrations) in the experimental setting. A new CT algorithm, called the penalized weighted least-squares (PWLS), was applied to remedy this condition. The peak locations were correctly positioned in the PWLS-CT reconstructions. A notable feature of the PWLS-CT reconstructions was a significant reduction of highly irregular noise peaks found in the ART-CT reconstructions. However, the peak heights were slightly reduced in the PWLS-CT reconstructions due to the nature of the PWLS algorithm. PWLS could converge on the original concentration gradient even when a fairly high error was embedded into some experimentally measured path integral concentrations. It was also found in the simulation tests that the PWLS algorithm was very robust with respect to random errors in the path integral concentrations. This beam geometry and the use of a single OP-FTIR scanning system, in combination with the PWLS algorithm, is a system applicable to both environmental and industrial settings.

Tomographic Reconstruction of Tracer Gas Concentration Profiles in a Room with the Use of a Single OP-FTIR and Two Iterative Algorithms: ART and PWLS.

PubMed

Park, Doo Y; Fessier, Jeffrey A; Yost, Michael G; Levine, Steven P

2000-03-01

Computed tomographic (CT) reconstructions of air contaminant concentration fields were conducted in a room-sized chamber employing a single open-path Fourier transform infrared (OP-FTIR) instrument and a combination of 52 flat mirrors and 4 retroreflectors. A total of 56 beam path data were repeatedly collected for around 1 hr while maintaining a stable concentration gradient. The plane of the room was divided into 195 pixels (13 × 15) for reconstruction. The algebraic reconstruction technique (ART) failed to reconstruct the original concentration gradient patterns for most cases. These poor results were caused by the "highly underdetermined condition" in which the number of unknown values (156 pixels) exceeds that of known data (56 path integral concentrations) in the experimental setting. A new CT algorithm, called the penalized weighted least-squares (PWLS), was applied to remedy this condition. The peak locations were correctly positioned in the PWLS-CT reconstructions. A notable feature of the PWLS-CT reconstructions was a significant reduction of highly irregular noise peaks found in the ART-CT reconstructions. However, the peak heights were slightly reduced in the PWLS-CT reconstructions due to the nature of the PWLS algorithm. PWLS could converge on the original concentration gradient even when a fairly high error was embedded into some experimentally measured path integral concentrations. It was also found in the simulation tests that the PWLS algorithm was very robust with respect to random errors in the path integral concentrations. This beam geometry and the use of a single OP-FTIR scanning system, in combination with the PWLS algorithm, is a system applicable to both environmental and industrial settings.

Jini service to reconstruct tomographic data

NASA Astrophysics Data System (ADS)

Knoll, Peter; Mirzaei, S.; Koriska, K.; Koehn, H.

2002-06-01

A number of imaging systems rely on the reconstruction of a 3- dimensional model from its projections through the process of computed tomography (CT). In medical imaging, for example magnetic resonance imaging (MRI), positron emission tomography (PET), and Single Computer Tomography (SPECT) acquire two-dimensional projections of a three dimensional projections of a three dimensional object. In order to calculate the 3-dimensional representation of the object, i.e. its voxel distribution, several reconstruction algorithms have been developed. Currently, mainly two reconstruct use: the filtered back projection(FBP) and iterative methods. Although the quality of iterative reconstructed SPECT slices is better than that of FBP slices, such iterative algorithms are rarely used for clinical routine studies because of their low availability and increased reconstruction time. We used Jini and a self-developed iterative reconstructions algorithm to design and implement a Jini reconstruction service. With this service, the physician selects the patient study from a database and a Jini client automatically discovers the registered Jini reconstruction services in the department's Intranet. After downloading the proxy object the this Jini service, the SPECT acquisition data are reconstructed. The resulting transaxial slices are visualized using a Jini slice viewer, which can be used for various imaging modalities.

Image reconstruction from few-view CT data by gradient-domain dictionary learning.

PubMed

Hu, Zhanli; Liu, Qiegen; Zhang, Na; Zhang, Yunwan; Peng, Xi; Wu, Peter Z; Zheng, Hairong; Liang, Dong

2016-05-21

Decreasing the number of projections is an effective way to reduce the radiation dose exposed to patients in medical computed tomography (CT) imaging. However, incomplete projection data for CT reconstruction will result in artifacts and distortions. In this paper, a novel dictionary learning algorithm operating in the gradient-domain (Grad-DL) is proposed for few-view CT reconstruction. Specifically, the dictionaries are trained from the horizontal and vertical gradient images, respectively and the desired image is reconstructed subsequently from the sparse representations of both gradients by solving the least-square method. Since the gradient images are sparser than the image itself, the proposed approach could lead to sparser representations than conventional DL methods in the image-domain, and thus a better reconstruction quality is achieved. To evaluate the proposed Grad-DL algorithm, both qualitative and quantitative studies were employed through computer simulations as well as real data experiments on fan-beam and cone-beam geometry. The results show that the proposed algorithm can yield better images than the existing algorithms.

Recovery of a spectrum based on a compressive-sensing algorithm with weighted principal component analysis

NASA Astrophysics Data System (ADS)

Dafu, Shen; Leihong, Zhang; Dong, Liang; Bei, Li; Yi, Kang

2017-07-01

The purpose of this study is to improve the reconstruction precision and better copy the color of spectral image surfaces. A new spectral reflectance reconstruction algorithm based on an iterative threshold combined with weighted principal component space is presented in this paper, and the principal component with weighted visual features is the sparse basis. Different numbers of color cards are selected as the training samples, a multispectral image is the testing sample, and the color differences in the reconstructions are compared. The channel response value is obtained by a Mega Vision high-accuracy, multi-channel imaging system. The results show that spectral reconstruction based on weighted principal component space is superior in performance to that based on traditional principal component space. Therefore, the color difference obtained using the compressive-sensing algorithm with weighted principal component analysis is less than that obtained using the algorithm with traditional principal component analysis, and better reconstructed color consistency with human eye vision is achieved.

Simultaneous deblurring and iterative reconstruction of CBCT for image guided brain radiosurgery.

PubMed

Hashemi, SayedMasoud; Song, William Y; Sahgal, Arjun; Lee, Young; Huynh, Christopher; Grouza, Vladimir; Nordström, Håkan; Eriksson, Markus; Dorenlot, Antoine; Régis, Jean Marie; Mainprize, James G; Ruschin, Mark

2017-04-07

One of the limiting factors in cone-beam CT (CBCT) image quality is system blur, caused by detector response, x-ray source focal spot size, azimuthal blurring, and reconstruction algorithm. In this work, we develop a novel iterative reconstruction algorithm that improves spatial resolution by explicitly accounting for image unsharpness caused by different factors in the reconstruction formulation. While the model-based iterative reconstruction techniques use prior information about the detector response and x-ray source, our proposed technique uses a simple measurable blurring model. In our reconstruction algorithm, denoted as simultaneous deblurring and iterative reconstruction (SDIR), the blur kernel can be estimated using the modulation transfer function (MTF) slice of the CatPhan phantom or any other MTF phantom, such as wire phantoms. The proposed image reconstruction formulation includes two regularization terms: (1) total variation (TV) and (2) nonlocal regularization, solved with a split Bregman augmented Lagrangian iterative method. The SDIR formulation preserves edges, eases the parameter adjustments to achieve both high spatial resolution and low noise variances, and reduces the staircase effect caused by regular TV-penalized iterative algorithms. The proposed algorithm is optimized for a point-of-care head CBCT unit for image-guided radiosurgery and is tested with CatPhan phantom, an anthropomorphic head phantom, and 6 clinical brain stereotactic radiosurgery cases. Our experiments indicate that SDIR outperforms the conventional filtered back projection and TV penalized simultaneous algebraic reconstruction technique methods (represented by adaptive steepest-descent POCS algorithm, ASD-POCS) in terms of MTF and line pair resolution, and retains the favorable properties of the standard TV-based iterative reconstruction algorithms in improving the contrast and reducing the reconstruction artifacts. It improves the visibility of the high contrast details in bony areas and the brain soft-tissue. For example, the results show the ventricles and some brain folds become visible in SDIR reconstructed images and the contrast of the visible lesions is effectively improved. The line-pair resolution was improved from 12 line-pair/cm in FBP to 14 line-pair/cm in SDIR. Adjusting the parameters of the ASD-POCS to achieve 14 line-pair/cm caused the noise variance to be higher than the SDIR. Using these parameters for ASD-POCS, the MTF of FBP and ASD-POCS were very close and equal to 0.7 mm -1 which was increased to 1.2 mm -1 by SDIR, at half maximum.

Simultaneous deblurring and iterative reconstruction of CBCT for image guided brain radiosurgery

NASA Astrophysics Data System (ADS)

Hashemi, SayedMasoud; Song, William Y.; Sahgal, Arjun; Lee, Young; Huynh, Christopher; Grouza, Vladimir; Nordström, Håkan; Eriksson, Markus; Dorenlot, Antoine; Régis, Jean Marie; Mainprize, James G.; Ruschin, Mark

2017-04-01

One of the limiting factors in cone-beam CT (CBCT) image quality is system blur, caused by detector response, x-ray source focal spot size, azimuthal blurring, and reconstruction algorithm. In this work, we develop a novel iterative reconstruction algorithm that improves spatial resolution by explicitly accounting for image unsharpness caused by different factors in the reconstruction formulation. While the model-based iterative reconstruction techniques use prior information about the detector response and x-ray source, our proposed technique uses a simple measurable blurring model. In our reconstruction algorithm, denoted as simultaneous deblurring and iterative reconstruction (SDIR), the blur kernel can be estimated using the modulation transfer function (MTF) slice of the CatPhan phantom or any other MTF phantom, such as wire phantoms. The proposed image reconstruction formulation includes two regularization terms: (1) total variation (TV) and (2) nonlocal regularization, solved with a split Bregman augmented Lagrangian iterative method. The SDIR formulation preserves edges, eases the parameter adjustments to achieve both high spatial resolution and low noise variances, and reduces the staircase effect caused by regular TV-penalized iterative algorithms. The proposed algorithm is optimized for a point-of-care head CBCT unit for image-guided radiosurgery and is tested with CatPhan phantom, an anthropomorphic head phantom, and 6 clinical brain stereotactic radiosurgery cases. Our experiments indicate that SDIR outperforms the conventional filtered back projection and TV penalized simultaneous algebraic reconstruction technique methods (represented by adaptive steepest-descent POCS algorithm, ASD-POCS) in terms of MTF and line pair resolution, and retains the favorable properties of the standard TV-based iterative reconstruction algorithms in improving the contrast and reducing the reconstruction artifacts. It improves the visibility of the high contrast details in bony areas and the brain soft-tissue. For example, the results show the ventricles and some brain folds become visible in SDIR reconstructed images and the contrast of the visible lesions is effectively improved. The line-pair resolution was improved from 12 line-pair/cm in FBP to 14 line-pair/cm in SDIR. Adjusting the parameters of the ASD-POCS to achieve 14 line-pair/cm caused the noise variance to be higher than the SDIR. Using these parameters for ASD-POCS, the MTF of FBP and ASD-POCS were very close and equal to 0.7 mm-1 which was increased to 1.2 mm-1 by SDIR, at half maximum.

Priori mask guided image reconstruction (p-MGIR) for ultra-low dose cone-beam computed tomography

NASA Astrophysics Data System (ADS)

Park, Justin C.; Zhang, Hao; Chen, Yunmei; Fan, Qiyong; Kahler, Darren L.; Liu, Chihray; Lu, Bo

2015-11-01

Recently, the compressed sensing (CS) based iterative reconstruction method has received attention because of its ability to reconstruct cone beam computed tomography (CBCT) images with good quality using sparsely sampled or noisy projections, thus enabling dose reduction. However, some challenges remain. In particular, there is always a tradeoff between image resolution and noise/streak artifact reduction based on the amount of regularization weighting that is applied uniformly across the CBCT volume. The purpose of this study is to develop a novel low-dose CBCT reconstruction algorithm framework called priori mask guided image reconstruction (p-MGIR) that allows reconstruction of high-quality low-dose CBCT images while preserving the image resolution. In p-MGIR, the unknown CBCT volume was mathematically modeled as a combination of two regions: (1) where anatomical structures are complex, and (2) where intensities are relatively uniform. The priori mask, which is the key concept of the p-MGIR algorithm, was defined as the matrix that distinguishes between the two separate CBCT regions where the resolution needs to be preserved and where streak or noise needs to be suppressed. We then alternately updated each part of image by solving two sub-minimization problems iteratively, where one minimization was focused on preserving the edge information of the first part while the other concentrated on the removal of noise/artifacts from the latter part. To evaluate the performance of the p-MGIR algorithm, a numerical head-and-neck phantom, a Catphan 600 physical phantom, and a clinical head-and-neck cancer case were used for analysis. The results were compared with the standard Feldkamp-Davis-Kress as well as conventional CS-based algorithms. Examination of the p-MGIR algorithm showed that high-quality low-dose CBCT images can be reconstructed without compromising the image resolution. For both phantom and the patient cases, the p-MGIR is able to achieve a clinically-reasonable image with 60 projections. Therefore, a clinically-viable, high-resolution head-and-neck CBCT image can be obtained while cutting the dose by 83%. Moreover, the image quality obtained using p-MGIR is better than the quality obtained using other algorithms. In this work, we propose a novel low-dose CBCT reconstruction algorithm called p-MGIR. It can be potentially used as a CBCT reconstruction algorithm with low dose scan requests

Motion and positional error correction for cone beam 3D-reconstruction with mobile C-arms.

PubMed

Bodensteiner, C; Darolti, C; Schumacher, H; Matthäus, L; Schweikard, A

2007-01-01

CT-images acquired by mobile C-arm devices can contain artefacts caused by positioning errors. We propose a data driven method based on iterative 3D-reconstruction and 2D/3D-registration to correct projection data inconsistencies. With a 2D/3D-registration algorithm, transformations are computed to align the acquired projection images to a previously reconstructed volume. In an iterative procedure, the reconstruction algorithm uses the results of the registration step. This algorithm also reduces small motion artefacts within 3D-reconstructions. Experiments with simulated projections from real patient data show the feasibility of the proposed method. In addition, experiments with real projection data acquired with an experimental robotised C-arm device have been performed with promising results.

Bottom-up GGM algorithm for constructing multiple layered hierarchical gene regulatory networks

USDA-ARS?s Scientific Manuscript database

Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. A bottom-up graphic Gaus...

A biomechanical modeling guided simultaneous motion estimation and image reconstruction technique (SMEIR-Bio) for 4D-CBCT reconstruction

NASA Astrophysics Data System (ADS)

Huang, Xiaokun; Zhang, You; Wang, Jing

2017-03-01

Four-dimensional (4D) cone-beam computed tomography (CBCT) enables motion tracking of anatomical structures and removes artifacts introduced by motion. However, the imaging time/dose of 4D-CBCT is substantially longer/higher than traditional 3D-CBCT. We previously developed a simultaneous motion estimation and image reconstruction (SMEIR) algorithm, to reconstruct high-quality 4D-CBCT from limited number of projections to reduce the imaging time/dose. However, the accuracy of SMEIR is limited in reconstructing low-contrast regions with fine structure details. In this study, we incorporate biomechanical modeling into the SMEIR algorithm (SMEIR-Bio), to improve the reconstruction accuracy at low-contrast regions with fine details. The efficacy of SMEIR-Bio is evaluated using 11 lung patient cases and compared to that of the original SMEIR algorithm. Qualitative and quantitative comparisons showed that SMEIR-Bio greatly enhances the accuracy of reconstructed 4D-CBCT volume in low-contrast regions, which can potentially benefit multiple clinical applications including the treatment outcome analysis.

A preliminary investigation of ROI-image reconstruction with the rebinned BPF algorithm

NASA Astrophysics Data System (ADS)

Bian, Junguo; Xia, Dan; Yu, Lifeng; Sidky, Emil Y.; Pan, Xiaochuan

2008-03-01

The back-projection filtration (BPF)algorithm is capable of reconstructing ROI images from truncated data acquired with a wide class of general trajectories. However, it has been observed that, similar to other algorithms for convergent beam geometries, the BPF algorithm involves a spatially varying weighting factor in the backprojection step. This weighting factor can not only increase the computation load, but also amplify the noise in reconstructed images The weighting factor can be eliminated by appropriately rebinning the measured cone-beam data into fan-parallel-beam data. Such an appropriate data rebinning not only removes the weighting factor, but also retain other favorable properties of the BPF algorithm. In this work, we conduct a preliminary study of the rebinned BPF algorithm and its noise property. Specifically, we consider an application in which the detector and source can move in several directions for achieving ROI data acquisition. The combined motion of the detector and source generally forms a complex trajectory. We investigate in this work image reconstruction within an ROI from data acquired in this kind of applications.

An EM Algorithm for Maximum Likelihood Estimation of Process Factor Analysis Models

ERIC Educational Resources Information Center

Lee, Taehun

2010-01-01

In this dissertation, an Expectation-Maximization (EM) algorithm is developed and implemented to obtain maximum likelihood estimates of the parameters and the associated standard error estimates characterizing temporal flows for the latent variable time series following stationary vector ARMA processes, as well as the parameters defining the…

Statistical reconstruction for cosmic ray muon tomography.

PubMed

Schultz, Larry J; Blanpied, Gary S; Borozdin, Konstantin N; Fraser, Andrew M; Hengartner, Nicolas W; Klimenko, Alexei V; Morris, Christopher L; Orum, Chris; Sossong, Michael J

2007-08-01

Highly penetrating cosmic ray muons constantly shower the earth at a rate of about 1 muon per cm2 per minute. We have developed a technique which exploits the multiple Coulomb scattering of these particles to perform nondestructive inspection without the use of artificial radiation. In prior work [1]-[3], we have described heuristic methods for processing muon data to create reconstructed images. In this paper, we present a maximum likelihood/expectation maximization tomographic reconstruction algorithm designed for the technique. This algorithm borrows much from techniques used in medical imaging, particularly emission tomography, but the statistics of muon scattering dictates differences. We describe the statistical model for multiple scattering, derive the reconstruction algorithm, and present simulated examples. We also propose methods to improve the robustness of the algorithm to experimental errors and events departing from the statistical model.

Spectral CT metal artifact reduction with an optimization-based reconstruction algorithm

NASA Astrophysics Data System (ADS)

Gilat Schmidt, Taly; Barber, Rina F.; Sidky, Emil Y.

2017-03-01

Metal objects cause artifacts in computed tomography (CT) images. This work investigated the feasibility of a spectral CT method to reduce metal artifacts. Spectral CT acquisition combined with optimization-based reconstruction is proposed to reduce artifacts by modeling the physical effects that cause metal artifacts and by providing the flexibility to selectively remove corrupted spectral measurements in the spectral-sinogram space. The proposed Constrained `One-Step' Spectral CT Image Reconstruction (cOSSCIR) algorithm directly estimates the basis material maps while enforcing convex constraints. The incorporation of constraints on the reconstructed basis material maps is expected to mitigate undersampling effects that occur when corrupted data is excluded from reconstruction. The feasibility of the cOSSCIR algorithm to reduce metal artifacts was investigated through simulations of a pelvis phantom. The cOSSCIR algorithm was investigated with and without the use of a third basis material representing metal. The effects of excluding data corrupted by metal were also investigated. The results demonstrated that the proposed cOSSCIR algorithm reduced metal artifacts and improved CT number accuracy. For example, CT number error in a bright shading artifact region was reduced from 403 HU in the reference filtered backprojection reconstruction to 33 HU using the proposed algorithm in simulation. In the dark shading regions, the error was reduced from 1141 HU to 25 HU. Of the investigated approaches, decomposing the data into three basis material maps and excluding the corrupted data demonstrated the greatest reduction in metal artifacts.

Low dose CT reconstruction via L1 norm dictionary learning using alternating minimization algorithm and balancing principle.

PubMed

Wu, Junfeng; Dai, Fang; Hu, Gang; Mou, Xuanqin

2018-04-18

Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Recently, statistical iterative reconstruction (SIR) with l0-norm dictionary learning regularization has been developed to reconstruct CT images from the low dose and few-view dataset in order to reduce radiation dose. Nonetheless, the sparse regularization term adopted in this approach is l0-norm, which cannot guarantee the global convergence of the proposed algorithm. To address this problem, in this study we introduced the l1-norm dictionary learning penalty into SIR framework for low dose CT image reconstruction, and developed an alternating minimization algorithm to minimize the associated objective function, which transforms CT image reconstruction problem into a sparse coding subproblem and an image updating subproblem. During the image updating process, an efficient model function approach based on balancing principle is applied to choose the regularization parameters. The proposed alternating minimization algorithm was evaluated first using real projection data of a sheep lung CT perfusion and then using numerical simulation based on sheep lung CT image and chest image. Both visual assessment and quantitative comparison using terms of root mean square error (RMSE) and structural similarity (SSIM) index demonstrated that the new image reconstruction algorithm yielded similar performance with l0-norm dictionary learning penalty and outperformed the conventional filtered backprojection (FBP) and total variation (TV) minimization algorithms.

Adaptive optics image restoration algorithm based on wavefront reconstruction and adaptive total variation method

NASA Astrophysics Data System (ADS)

Li, Dongming; Zhang, Lijuan; Wang, Ting; Liu, Huan; Yang, Jinhua; Chen, Guifen

2016-11-01

To improve the adaptive optics (AO) image's quality, we study the AO image restoration algorithm based on wavefront reconstruction technology and adaptive total variation (TV) method in this paper. Firstly, the wavefront reconstruction using Zernike polynomial is used for initial estimated for the point spread function (PSF). Then, we develop our proposed iterative solutions for AO images restoration, addressing the joint deconvolution issue. The image restoration experiments are performed to verify the image restoration effect of our proposed algorithm. The experimental results show that, compared with the RL-IBD algorithm and Wiener-IBD algorithm, we can see that GMG measures (for real AO image) from our algorithm are increased by 36.92%, and 27.44% respectively, and the computation time are decreased by 7.2%, and 3.4% respectively, and its estimation accuracy is significantly improved.

Improved adaptive genetic algorithm with sparsity constraint applied to thermal neutron CT reconstruction of two-phase flow

NASA Astrophysics Data System (ADS)

Yan, Mingfei; Hu, Huasi; Otake, Yoshie; Taketani, Atsushi; Wakabayashi, Yasuo; Yanagimachi, Shinzo; Wang, Sheng; Pan, Ziheng; Hu, Guang

2018-05-01

Thermal neutron computer tomography (CT) is a useful tool for visualizing two-phase flow due to its high imaging contrast and strong penetrability of neutrons for tube walls constructed with metallic material. A novel approach for two-phase flow CT reconstruction based on an improved adaptive genetic algorithm with sparsity constraint (IAGA-SC) is proposed in this paper. In the algorithm, the neighborhood mutation operator is used to ensure the continuity of the reconstructed object. The adaptive crossover probability P c and mutation probability P m are improved to help the adaptive genetic algorithm (AGA) achieve the global optimum. The reconstructed results for projection data, obtained from Monte Carlo simulation, indicate that the comprehensive performance of the IAGA-SC algorithm exceeds the adaptive steepest descent-projection onto convex sets (ASD-POCS) algorithm in restoring typical and complex flow regimes. It especially shows great advantages in restoring the simply connected flow regimes and the shape of object. In addition, the CT experiment for two-phase flow phantoms was conducted on the accelerator-driven neutron source to verify the performance of the developed IAGA-SC algorithm.

Compressed sensing with gradient total variation for low-dose CBCT reconstruction

NASA Astrophysics Data System (ADS)

Seo, Chang-Woo; Cha, Bo Kyung; Jeon, Seongchae; Huh, Young; Park, Justin C.; Lee, Byeonghun; Baek, Junghee; Kim, Eunyoung

2015-06-01

This paper describes the improvement of convergence speed with gradient total variation (GTV) in compressed sensing (CS) for low-dose cone-beam computed tomography (CBCT) reconstruction. We derive a fast algorithm for the constrained total variation (TV)-based a minimum number of noisy projections. To achieve this task we combine the GTV with a TV-norm regularization term to promote an accelerated sparsity in the X-ray attenuation characteristics of the human body. The GTV is derived from a TV and enforces more efficient computationally and faster in convergence until a desired solution is achieved. The numerical algorithm is simple and derives relatively fast convergence. We apply a gradient projection algorithm that seeks a solution iteratively in the direction of the projected gradient while enforcing a non-negatively of the found solution. In comparison with the Feldkamp, Davis, and Kress (FDK) and conventional TV algorithms, the proposed GTV algorithm showed convergence in ≤18 iterations, whereas the original TV algorithm needs at least 34 iterations in reducing 50% of the projections compared with the FDK algorithm in order to reconstruct the chest phantom images. Future investigation includes improving imaging quality, particularly regarding X-ray cone-beam scatter, and motion artifacts of CBCT reconstruction.

Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Qiaofeng; Sawatzky, Alex; Anastasio, Mark A., E-mail: anastasio@wustl.edu

Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that ismore » solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated accelerated FISTAs for use with two nonsmooth penalty functions that will lead to further reductions in image reconstruction times while preserving image quality. Moreover, with the help of a mixed sparsity-regularization, better preservation of soft-tissue structures can be potentially obtained. The algorithms were systematically evaluated by use of computer-simulated and clinical data sets.« less

Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction.

PubMed

Xu, Qiaofeng; Yang, Deshan; Tan, Jun; Sawatzky, Alex; Anastasio, Mark A

2016-04-01

The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated accelerated FISTAs for use with two nonsmooth penalty functions that will lead to further reductions in image reconstruction times while preserving image quality. Moreover, with the help of a mixed sparsity-regularization, better preservation of soft-tissue structures can be potentially obtained. The algorithms were systematically evaluated by use of computer-simulated and clinical data sets.

Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction

PubMed Central

Xu, Qiaofeng; Yang, Deshan; Tan, Jun; Sawatzky, Alex; Anastasio, Mark A.

2016-01-01

Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated accelerated FISTAs for use with two nonsmooth penalty functions that will lead to further reductions in image reconstruction times while preserving image quality. Moreover, with the help of a mixed sparsity-regularization, better preservation of soft-tissue structures can be potentially obtained. The algorithms were systematically evaluated by use of computer-simulated and clinical data sets. PMID:27036582

Comparison and analysis of nonlinear algorithms for compressed sensing in MRI.

PubMed

Yu, Yeyang; Hong, Mingjian; Liu, Feng; Wang, Hua; Crozier, Stuart

2010-01-01

Compressed sensing (CS) theory has been recently applied in Magnetic Resonance Imaging (MRI) to accelerate the overall imaging process. In the CS implementation, various algorithms have been used to solve the nonlinear equation system for better image quality and reconstruction speed. However, there are no explicit criteria for an optimal CS algorithm selection in the practical MRI application. A systematic and comparative study of those commonly used algorithms is therefore essential for the implementation of CS in MRI. In this work, three typical algorithms, namely, the Gradient Projection For Sparse Reconstruction (GPSR) algorithm, Interior-point algorithm (l(1)_ls), and the Stagewise Orthogonal Matching Pursuit (StOMP) algorithm are compared and investigated in three different imaging scenarios, brain, angiogram and phantom imaging. The algorithms' performances are characterized in terms of image quality and reconstruction speed. The theoretical results show that the performance of the CS algorithms is case sensitive; overall, the StOMP algorithm offers the best solution in imaging quality, while the GPSR algorithm is the most efficient one among the three methods. In the next step, the algorithm performances and characteristics will be experimentally explored. It is hoped that this research will further support the applications of CS in MRI.

[Application of 3D visualization technique in breast cancer surgery with immediate breast reconstruction using laparoscopically harvested pedicled latissimus dorsi muscle flap].

PubMed

Zhang, Pu-Sheng; Wang, Li-Kun; Luo, Yun-Feng; Shi, Fu-Jun; He, Lin-Yun; Zeng, Cheng-Bing; Zhang, Yu; Fang, Chi-Hua

2017-08-20

To study the value of 3D visualization technique in breast-preserving surgery for breast cancer with immediate breast reconstruction using laparoscopically harvested pedicled latissimus dorsi muscle flap. From January, 2015 to May, 2016, 30 patients with breast cancer underwent breast-preserving surgery with immediate breast reconstruction using pedicled latissimus dorsi muscle flap. The CT data of the arterial phase and venous phase were collected preoperatively and imported into the self-developed medical image 3D visualization system for image segmentation and 3D reconstruction. The 3D models were imported into the simulation surgery platform for virtual surgery to prepare for subsequent surgeries. The cosmetic outcomes of the patients were evaluated 6 months after the surgery. Another 18 patients with breast cancer who underwent laparoscopic latissimus dorsi muscle breast reconstruction without using 3D visualization technique from January to December, 2014 served as the control group. The data of the operative time, intraoperative blood loss and postoperative appearance of the breasts were analyzed. The reconstructed 3D model clearly displayed the anatomical structures of the breast, armpit, latissimus dorsi muscle and vessels and their anatomical relationship in all the 30 cases. Immediate breast reconstruction was performed successfully in all the cases with median operation time of 226 min (range, 210 to 420 min), a median blood loss of 95 mL (range, 73 to 132 mL). Evaluation of the appearance of the breast showed excellent results in 22 cases, good appearance in 6 cases and acceptable appearance in 2 cases. In the control group, the median operation time was 283 min (range, 256 to 313 min) and the median blood loss was 107 mL (range, 79 to 147 mL) with excellent appearance of the breasts in 10 cases, good appearance in 4 cases and acceptable appearance in 4 cases. 3D reconstruction technique can clearly display the morphology of the latissimus dorsi and the thoracic dorsal artery, allows calculation of the volume of the breast and the latissimus dorsi, and helps in defining the scope of resection of the latissimus dorsi to avoid injuries of the pedicled vessels. This technique also helps to shorten the operation time, reduce intraoperative bleeding, and improve the appearance of the reconstructed breast using pedicled latissimus dorsi muscle flap.

Quantitative Features of Liver Lesions, Lung Nodules, and Renal Stones at Multi-Detector Row CT Examinations: Dependency on Radiation Dose and Reconstruction Algorithm.

PubMed

Solomon, Justin; Mileto, Achille; Nelson, Rendon C; Roy Choudhury, Kingshuk; Samei, Ehsan

2016-04-01

To determine if radiation dose and reconstruction algorithm affect the computer-based extraction and analysis of quantitative imaging features in lung nodules, liver lesions, and renal stones at multi-detector row computed tomography (CT). Retrospective analysis of data from a prospective, multicenter, HIPAA-compliant, institutional review board-approved clinical trial was performed by extracting 23 quantitative imaging features (size, shape, attenuation, edge sharpness, pixel value distribution, and texture) of lesions on multi-detector row CT images of 20 adult patients (14 men, six women; mean age, 63 years; range, 38-72 years) referred for known or suspected focal liver lesions, lung nodules, or kidney stones. Data were acquired between September 2011 and April 2012. All multi-detector row CT scans were performed at two different radiation dose levels; images were reconstructed with filtered back projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction (MBIR) algorithms. A linear mixed-effects model was used to assess the effect of radiation dose and reconstruction algorithm on extracted features. Among the 23 imaging features assessed, radiation dose had a significant effect on five, three, and four of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Adaptive statistical iterative reconstruction had a significant effect on three, one, and one of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). MBIR reconstruction had a significant effect on nine, 11, and 15 of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Of note, the measured size of lung nodules and renal stones with MBIR was significantly different than those for the other two algorithms (P < .002 for all comparisons). Although lesion texture was significantly affected by the reconstruction algorithm used (average of 3.33 features affected by MBIR throughout lesion types; P < .002, for all comparisons), no significant effect of the radiation dose setting was observed for all but one of the texture features (P = .002-.998). Radiation dose settings and reconstruction algorithms affect the extraction and analysis of quantitative imaging features in lesions at multi-detector row CT.

A new Mumford-Shah total variation minimization based model for sparse-view x-ray computed tomography image reconstruction.

PubMed

Chen, Bo; Bian, Zhaoying; Zhou, Xiaohui; Chen, Wensheng; Ma, Jianhua; Liang, Zhengrong

2018-04-12

Total variation (TV) minimization for the sparse-view x-ray computer tomography (CT) reconstruction has been widely explored to reduce radiation dose. However, due to the piecewise constant assumption for the TV model, the reconstructed images often suffer from over-smoothness on the image edges. To mitigate this drawback of TV minimization, we present a Mumford-Shah total variation (MSTV) minimization algorithm in this paper. The presented MSTV model is derived by integrating TV minimization and Mumford-Shah segmentation. Subsequently, a penalized weighted least-squares (PWLS) scheme with MSTV is developed for the sparse-view CT reconstruction. For simplicity, the proposed algorithm is named as 'PWLS-MSTV.' To evaluate the performance of the present PWLS-MSTV algorithm, both qualitative and quantitative studies were conducted by using a digital XCAT phantom and a physical phantom. Experimental results show that the present PWLS-MSTV algorithm has noticeable gains over the existing algorithms in terms of noise reduction, contrast-to-ratio measure and edge-preservation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grzetic, S; Weldon, M; Noa, K

Purpose: This study compares the newly released MaxFOV Revision 1 EFOV reconstruction algorithm for GE RT590 to the older WideView EFOV algorithm. Two radiotherapy overlays from Q-fix and Diacor, are included in our analysis. Hounsfield Units (HU) generated with the WideView algorithm varied in the extended field (beyond 50cm) and the scanned object’s border varied from slice to slice. A validation of HU consistency between the two reconstruction algorithms is performed. Methods: A CatPhan 504 and CIRS062 Electron Density Phantom were scanned on a GE RT590 CT-Simulator. The phantoms were positioned in multiple locations within the scan field of viewmore » so some of the density plugs were outside the 50cm reconstruction circle. Images were reconstructed using both the WideView and MaxFOV algorithms. The HU for each scan were characterized both in average over a volume and in profile. Results: HU values are consistent between the two algorithms. Low-density material will have a slight increase in HU value and high-density material will have a slight decrease in HU value as the distance from the sweet spot increases. Border inconsistencies and shading artifacts are still present with the MaxFOV reconstruction on the Q-fix overlay but not the Diacor overlay (It should be noted that the Q-fix overlay is not currently GE-certified). HU values for water outside the 50cm FOV are within 40HU of reconstructions at the sweet spot of the scanner. CatPhan HU profiles show improvement with the MaxFOV algorithm as it approaches the scanner edge. Conclusion: The new MaxFOV algorithm improves the contour border for objects outside of the standard FOV when using a GE-approved tabletop. Air cavities outside of the standard FOV create inconsistent object borders. HU consistency is within GE specifications and the accuracy of the phantom edge improves. Further adjustments to the algorithm are being investigated by GE.« less

PSF reconstruction for Compton-based prompt gamma imaging

NASA Astrophysics Data System (ADS)

Jan, Meei-Ling; Lee, Ming-Wei; Huang, Hsuan-Ming

2018-02-01

Compton-based prompt gamma (PG) imaging has been proposed for in vivo range verification in proton therapy. However, several factors degrade the image quality of PG images, some of which are due to inherent properties of a Compton camera such as spatial resolution and energy resolution. Moreover, Compton-based PG imaging has a spatially variant resolution loss. In this study, we investigate the performance of the list-mode ordered subset expectation maximization algorithm with a shift-variant point spread function (LM-OSEM-SV-PSF) model. We also evaluate how well the PG images reconstructed using an SV-PSF model reproduce the distal falloff of the proton beam. The SV-PSF parameters were estimated from simulation data of point sources at various positions. Simulated PGs were produced in a water phantom irradiated with a proton beam. Compared to the LM-OSEM algorithm, the LM-OSEM-SV-PSF algorithm improved the quality of the reconstructed PG images and the estimation of PG falloff positions. In addition, the 4.44 and 5.25 MeV PG emissions can be accurately reconstructed using the LM-OSEM-SV-PSF algorithm. However, for the 2.31 and 6.13 MeV PG emissions, the LM-OSEM-SV-PSF reconstruction provides limited improvement. We also found that the LM-OSEM algorithm followed by a shift-variant Richardson-Lucy deconvolution could reconstruct images with quality visually similar to the LM-OSEM-SV-PSF-reconstructed images, while requiring shorter computation time.

Evaluating low pass filters on SPECT reconstructed cardiac orientation estimation

NASA Astrophysics Data System (ADS)

Dwivedi, Shekhar

2009-02-01

Low pass filters can affect the quality of clinical SPECT images by smoothing. Appropriate filter and parameter selection leads to optimum smoothing that leads to a better quantification followed by correct diagnosis and accurate interpretation by the physician. This study aims at evaluating the low pass filters on SPECT reconstruction algorithms. Criteria for evaluating the filters are estimating the SPECT reconstructed cardiac azimuth and elevation angle. Low pass filters studied are butterworth, gaussian, hamming, hanning and parzen. Experiments are conducted using three reconstruction algorithms, FBP (filtered back projection), MLEM (maximum likelihood expectation maximization) and OSEM (ordered subsets expectation maximization), on four gated cardiac patient projections (two patients with stress and rest projections). Each filter is applied with varying cutoff and order for each reconstruction algorithm (only butterworth used for MLEM and OSEM). The azimuth and elevation angles are calculated from the reconstructed volume and the variation observed in the angles with varying filter parameters is reported. Our results demonstrate that behavior of hamming, hanning and parzen filter (used with FBP) with varying cutoff is similar for all the datasets. Butterworth filter (cutoff > 0.4) behaves in a similar fashion for all the datasets using all the algorithms whereas with OSEM for a cutoff < 0.4, it fails to generate cardiac orientation due to oversmoothing, and gives an unstable response with FBP and MLEM. This study on evaluating effect of low pass filter cutoff and order on cardiac orientation using three different reconstruction algorithms provides an interesting insight into optimal selection of filter parameters.

Influence of radiation dose and iterative reconstruction algorithms for measurement accuracy and reproducibility of pulmonary nodule volumetry: A phantom study.

PubMed

Kim, Hyungjin; Park, Chang Min; Song, Yong Sub; Lee, Sang Min; Goo, Jin Mo

2014-05-01

To evaluate the influence of radiation dose settings and reconstruction algorithms on the measurement accuracy and reproducibility of semi-automated pulmonary nodule volumetry. CT scans were performed on a chest phantom containing various nodules (10 and 12mm; +100, -630 and -800HU) at 120kVp with tube current-time settings of 10, 20, 50, and 100mAs. Each CT was reconstructed using filtered back projection (FBP), iDose(4) and iterative model reconstruction (IMR). Semi-automated volumetry was performed by two radiologists using commercial volumetry software for nodules at each CT dataset. Noise, contrast-to-noise ratio and signal-to-noise ratio of CT images were also obtained. The absolute percentage measurement errors and differences were then calculated for volume and mass. The influence of radiation dose and reconstruction algorithm on measurement accuracy, reproducibility and objective image quality metrics was analyzed using generalized estimating equations. Measurement accuracy and reproducibility of nodule volume and mass were not significantly associated with CT radiation dose settings or reconstruction algorithms (p>0.05). Objective image quality metrics of CT images were superior in IMR than in FBP or iDose(4) at all radiation dose settings (p<0.05). Semi-automated nodule volumetry can be applied to low- or ultralow-dose chest CT with usage of a novel iterative reconstruction algorithm without losing measurement accuracy and reproducibility. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Minimal-scan filtered backpropagation algorithms for diffraction tomography.

PubMed

Pan, X; Anastasio, M A

1999-12-01

The filtered backpropagation (FBPP) algorithm, originally developed by Devaney [Ultrason. Imaging 4, 336 (1982)], has been widely used for reconstructing images in diffraction tomography. It is generally known that the FBPP algorithm requires scattered data from a full angular range of 2 pi for exact reconstruction of a generally complex-valued object function. However, we reveal that one needs scattered data only over the angular range 0 < or = phi < or = 3 pi/2 for exact reconstruction of a generally complex-valued object function. Using this insight, we develop and analyze a family of minimal-scan filtered backpropagation (MS-FBPP) algorithms, which, unlike the FBPP algorithm, use scattered data acquired from view angles over the range 0 < or = phi < or = 3 pi/2. We show analytically that these MS-FBPP algorithms are mathematically identical to the FBPP algorithm. We also perform computer simulation studies for validation, demonstration, and comparison of these MS-FBPP algorithms. The numerical results in these simulation studies corroborate our theoretical assertions.

Fast estimation of diffusion tensors under Rician noise by the EM algorithm.

PubMed

Liu, Jia; Gasbarra, Dario; Railavo, Juha

2016-01-15

Diffusion tensor imaging (DTI) is widely used to characterize, in vivo, the white matter of the central nerve system (CNS). This biological tissue contains much anatomic, structural and orientational information of fibers in human brain. Spectral data from the displacement distribution of water molecules located in the brain tissue are collected by a magnetic resonance scanner and acquired in the Fourier domain. After the Fourier inversion, the noise distribution is Gaussian in both real and imaginary parts and, as a consequence, the recorded magnitude data are corrupted by Rician noise. Statistical estimation of diffusion leads a non-linear regression problem. In this paper, we present a fast computational method for maximum likelihood estimation (MLE) of diffusivities under the Rician noise model based on the expectation maximization (EM) algorithm. By using data augmentation, we are able to transform a non-linear regression problem into the generalized linear modeling framework, reducing dramatically the computational cost. The Fisher-scoring method is used for achieving fast convergence of the tensor parameter. The new method is implemented and applied using both synthetic and real data in a wide range of b-amplitudes up to 14,000s/mm(2). Higher accuracy and precision of the Rician estimates are achieved compared with other log-normal based methods. In addition, we extend the maximum likelihood (ML) framework to the maximum a posteriori (MAP) estimation in DTI under the aforementioned scheme by specifying the priors. We will describe how close numerically are the estimators of model parameters obtained through MLE and MAP estimation. Copyright © 2015 Elsevier B.V. All rights reserved.

Importance of mitral valve repair associated with left ventricular reconstruction for patients with ischemic cardiomyopathy: a real-time three-dimensional echocardiographic study

NASA Technical Reports Server (NTRS)

Qin, Jian Xin; Shiota, Takahiro; McCarthy, Patrick M.; Asher, Craig R.; Hail, Melanie; Agler, Deborah A.; Popovic, Zoran B.; Greenberg, Neil L.; Smedira, Nicholas G.; Starling, Randall C.;

CUDA-based high-performance computing of the S-BPF algorithm with no-waiting pipelining

NASA Astrophysics Data System (ADS)

Deng, Lin; Yan, Bin; Chang, Qingmei; Han, Yu; Zhang, Xiang; Xi, Xiaoqi; Li, Lei

2015-10-01

The backprojection-filtration (BPF) algorithm has become a good solution for local reconstruction in cone-beam computed tomography (CBCT). However, the reconstruction speed of BPF is a severe limitation for clinical applications. The selective-backprojection filtration (S-BPF) algorithm is developed to improve the parallel performance of BPF by selective backprojection. Furthermore, the general-purpose graphics processing unit (GP-GPU) is a popular tool for accelerating the reconstruction. Much work has been performed aiming for the optimization of the cone-beam back-projection. As the cone-beam back-projection process becomes faster, the data transportation holds a much bigger time proportion in the reconstruction than before. This paper focuses on minimizing the total time in the reconstruction with the S-BPF algorithm by hiding the data transportation among hard disk, CPU and GPU. And based on the analysis of the S-BPF algorithm, some strategies are implemented: (1) the asynchronous calls are used to overlap the implemention of CPU and GPU, (2) an innovative strategy is applied to obtain the DBP image to hide the transport time effectively, (3) two streams for data transportation and calculation are synchronized by the cudaEvent in the inverse of finite Hilbert transform on GPU. Our main contribution is a smart reconstruction of the S-BPF algorithm with GPU's continuous calculation and no data transportation time cost. a 5123 volume is reconstructed in less than 0.7 second on a single Tesla-based K20 GPU from 182 views projection with 5122 pixel per projection. The time cost of our implementation is about a half of that without the overlap behavior.

Efficient L1 regularization-based reconstruction for fluorescent molecular tomography using restarted nonlinear conjugate gradient.

PubMed

Shi, Junwei; Zhang, Bin; Liu, Fei; Luo, Jianwen; Bai, Jing

2013-09-15

For the ill-posed fluorescent molecular tomography (FMT) inverse problem, the L1 regularization can protect the high-frequency information like edges while effectively reduce the image noise. However, the state-of-the-art L1 regularization-based algorithms for FMT reconstruction are expensive in memory, especially for large-scale problems. An efficient L1 regularization-based reconstruction algorithm based on nonlinear conjugate gradient with restarted strategy is proposed to increase the computational speed with low memory consumption. The reconstruction results from phantom experiments demonstrate that the proposed algorithm can obtain high spatial resolution and high signal-to-noise ratio, as well as high localization accuracy for fluorescence targets.

The performance of diphoton primary vertex reconstruction methods in H → γγ+Met channel of ATLAS experiment

NASA Astrophysics Data System (ADS)

Tomiwa, K. G.

2017-09-01

The search for new physics in the H → γγ+met relies on how well the missing transverse energy is reconstructed. The Met algorithm used by the ATLAS experiment in turns uses input variables like photon and jets which depend on the reconstruction of the primary vertex. This document presents the performance of di-photon vertex reconstruction algorithms (hardest vertex method and Neural Network method). Comparing the performance of these algorithms for the nominal Standard Model sample and the Beyond Standard Model sample, we see the overall performance of the Neural Network method of primary vertex selection performed better than the Hardest vertex method.

Iterative reconstruction methods in atmospheric tomography: FEWHA, Kaczmarz and Gradient-based algorithm

NASA Astrophysics Data System (ADS)

Ramlau, R.; Saxenhuber, D.; Yudytskiy, M.

2014-07-01

The problem of atmospheric tomography arises in ground-based telescope imaging with adaptive optics (AO), where one aims to compensate in real-time for the rapidly changing optical distortions in the atmosphere. Many of these systems depend on a sufficient reconstruction of the turbulence profiles in order to obtain a good correction. Due to steadily growing telescope sizes, there is a strong increase in the computational load for atmospheric reconstruction with current methods, first and foremost the MVM. In this paper we present and compare three novel iterative reconstruction methods. The first iterative approach is the Finite Element- Wavelet Hybrid Algorithm (FEWHA), which combines wavelet-based techniques and conjugate gradient schemes to efficiently and accurately tackle the problem of atmospheric reconstruction. The method is extremely fast, highly flexible and yields superior quality. Another novel iterative reconstruction algorithm is the three step approach which decouples the problem in the reconstruction of the incoming wavefronts, the reconstruction of the turbulent layers (atmospheric tomography) and the computation of the best mirror correction (fitting step). For the atmospheric tomography problem within the three step approach, the Kaczmarz algorithm and the Gradient-based method have been developed. We present a detailed comparison of our reconstructors both in terms of quality and speed performance in the context of a Multi-Object Adaptive Optics (MOAO) system for the E-ELT setting on OCTOPUS, the ESO end-to-end simulation tool.

The properties of SIRT, TVM, and DART for 3D imaging of tubular domains in nanocomposite thin-films and sections.

PubMed

Chen, Delei; Goris, Bart; Bleichrodt, Folkert; Mezerji, Hamed Heidari; Bals, Sara; Batenburg, Kees Joost; de With, Gijsbertus; Friedrich, Heiner

2014-12-01

In electron tomography, the fidelity of the 3D reconstruction strongly depends on the employed reconstruction algorithm. In this paper, the properties of SIRT, TVM and DART reconstructions are studied with respect to having only a limited number of electrons available for imaging and applying different angular sampling schemes. A well-defined realistic model is generated, which consists of tubular domains within a matrix having slab-geometry. Subsequently, the electron tomography workflow is simulated from calculated tilt-series over experimental effects to reconstruction. In comparison with the model, the fidelity of each reconstruction method is evaluated qualitatively and quantitatively based on global and local edge profiles and resolvable distance between particles. Results show that the performance of all reconstruction methods declines with the total electron dose. Overall, SIRT algorithm is the most stable method and insensitive to changes in angular sampling. TVM algorithm yields significantly sharper edges in the reconstruction, but the edge positions are strongly influenced by the tilt scheme and the tubular objects become thinned. The DART algorithm markedly suppresses the elongation artifacts along the beam direction and moreover segments the reconstruction which can be considered a significant advantage for quantification. Finally, no advantage of TVM and DART to deal better with fewer projections was observed. Copyright © 2014 Elsevier B.V. All rights reserved.

The impact of noisy and misaligned attenuation maps on human-observer performance at lesion detection in SPECT

NASA Astrophysics Data System (ADS)

Wells, R. G.; Gifford, H. C.; Pretorius, P. H.; Famcombe, T. H.; Narayanan, M. V.; King, M. A.

2002-06-01

We have demonstrated an improvement due to attenuation correction (AC) at the task of lesion detection in thoracic SPECT images. However, increased noise in the transmission data due to aging sources or very large patients, and misregistration of the emission and transmission maps, can reduce the accuracy of the AC and may result in a loss of lesion detectability. We investigated the impact of noise in and misregistration of transmission data, on the detection of simulated Ga-67 thoracic lesions. Human-observer localization-receiver-operating-characteristic (LROC) methodology was used to assess performance. Both emission and transmission data were simulated using the MCAT computer phantom. Emission data were reconstructed using OSEM incorporating AC and detector resolution compensation. Clinical noise levels were used in the emission data. The transmission-data noise levels ranged from zero (noise-free) to 32 times the measured clinical levels. Transaxial misregistrations of 0.32, 0.63, and 1.27 cm between emission and transmission data were also examined. Three different algorithms were considered for creating the attenuation maps: filtered backprojection (FBP), unbounded maximum-likelihood (ML), and block-iterative transmission AB (BITAB). Results indicate that a 16-fold increase in the noise was required to eliminate the benefit afforded by AC, when using FBP or ML to reconstruct the attenuation maps. When using BITAB, no significant loss in performance was observed for a 32-fold increase in noise. Misregistration errors are also a concern as even small errors here reduce the performance gains of AC.

Effects of light refraction on the accuracy of camera calibration and reconstruction in underwater motion analysis.

PubMed

Kwon, Young-Hoo; Casebolt, Jeffrey B

2006-01-01

One of the most serious obstacles to accurate quantification of the underwater motion of a swimmer's body is image deformation caused by refraction. Refraction occurs at the water-air interface plane (glass) owing to the density difference. Camera calibration-reconstruction algorithms commonly used in aquatic research do not have the capability to correct this refraction-induced nonlinear image deformation and produce large reconstruction errors. The aim of this paper is to provide a through review of: the nature of the refraction-induced image deformation and its behaviour in underwater object-space plane reconstruction; the intrinsic shortcomings of the Direct Linear Transformation (DLT) method in underwater motion analysis; experimental conditions that interact with refraction; and alternative algorithms and strategies that can be used to improve the calibration-reconstruction accuracy. Although it is impossible to remove the refraction error completely in conventional camera calibration-reconstruction methods, it is possible to improve the accuracy to some extent by manipulating experimental conditions or calibration frame characteristics. Alternative algorithms, such as the localized DLT and the double-plane method are also available for error reduction. The ultimate solution for the refraction problem is to develop underwater camera calibration and reconstruction algorithms that have the capability to correct refraction.

Effects of light refraction on the accuracy of camera calibration and reconstruction in underwater motion analysis.

PubMed

Kwon, Young-Hoo; Casebolt, Jeffrey B

2006-07-01

One of the most serious obstacles to accurate quantification of the underwater motion of a swimmer's body is image deformation caused by refraction. Refraction occurs at the water-air interface plane (glass) owing to the density difference. Camera calibration-reconstruction algorithms commonly used in aquatic research do not have the capability to correct this refraction-induced nonlinear image deformation and produce large reconstruction errors. The aim of this paper is to provide a thorough review of: the nature of the refraction-induced image deformation and its behaviour in underwater object-space plane reconstruction; the intrinsic shortcomings of the Direct Linear Transformation (DLT) method in underwater motion analysis; experimental conditions that interact with refraction; and alternative algorithms and strategies that can be used to improve the calibration-reconstruction accuracy. Although it is impossible to remove the refraction error completely in conventional camera calibration-reconstruction methods, it is possible to improve the accuracy to some extent by manipulating experimental conditions or calibration frame characteristics. Alternative algorithms, such as the localized DLT and the double-plane method are also available for error reduction. The ultimate solution for the refraction problem is to develop underwater camera calibration and reconstruction algorithms that have the capability to correct refraction.

Common-mask guided image reconstruction (c-MGIR) for enhanced 4D cone-beam computed tomography.

PubMed

Park, Justin C; Zhang, Hao; Chen, Yunmei; Fan, Qiyong; Li, Jonathan G; Liu, Chihray; Lu, Bo

2015-12-07

Compared to 3D cone beam computed tomography (3D CBCT), the image quality of commercially available four-dimensional (4D) CBCT is severely impaired due to the insufficient amount of projection data available for each phase. Since the traditional Feldkamp-Davis-Kress (FDK)-based algorithm is infeasible for reconstructing high quality 4D CBCT images with limited projections, investigators had developed several compress-sensing (CS) based algorithms to improve image quality. The aim of this study is to develop a novel algorithm which can provide better image quality than the FDK and other CS based algorithms with limited projections. We named this algorithm 'the common mask guided image reconstruction' (c-MGIR).In c-MGIR, the unknown CBCT volume is mathematically modeled as a combination of phase-specific motion vectors and phase-independent static vectors. The common-mask matrix, which is the key concept behind the c-MGIR algorithm, separates the common static part across all phase images from the possible moving part in each phase image. The moving part and the static part of the volumes were then alternatively updated by solving two sub-minimization problems iteratively. As the novel mathematical transformation allows the static volume and moving volumes to be updated (during each iteration) with global projections and 'well' solved static volume respectively, the algorithm was able to reduce the noise and under-sampling artifact (an issue faced by other algorithms) to the maximum extent. To evaluate the performance of our proposed c-MGIR, we utilized imaging data from both numerical phantoms and a lung cancer patient. The qualities of the images reconstructed with c-MGIR were compared with (1) standard FDK algorithm, (2) conventional total variation (CTV) based algorithm, (3) prior image constrained compressed sensing (PICCS) algorithm, and (4) motion-map constrained image reconstruction (MCIR) algorithm, respectively. To improve the efficiency of the algorithm, the code was implemented with a graphic processing unit for parallel processing purposes.Root mean square error (RMSE) between the ground truth and reconstructed volumes of the numerical phantom were in the descending order of FDK, CTV, PICCS, MCIR, and c-MGIR for all phases. Specifically, the means and the standard deviations of the RMSE of FDK, CTV, PICCS, MCIR and c-MGIR for all phases were 42.64  ±  6.5%, 3.63  ±  0.83%, 1.31%  ±  0.09%, 0.86%  ±  0.11% and 0.52 %  ±  0.02%, respectively. The image quality of the patient case also indicated the superiority of c-MGIR compared to other algorithms.The results indicated that clinically viable 4D CBCT images can be reconstructed while requiring no more projection data than a typical clinical 3D CBCT scan. This makes c-MGIR a potential online reconstruction algorithm for 4D CBCT, which can provide much better image quality than other available algorithms, while requiring less dose and potentially less scanning time.

Characterizing isolated attosecond pulses with angular streaking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Siqi; Guo, Zhaoheng; Coffee, Ryan N.

Here, we present a reconstruction algorithm for isolated attosecond pulses, which exploits the phase dependent energy modulation of a photoelectron ionized in the presence of a strong laser field. The energy modulation due to a circularly polarized laser field is manifest strongly in the angle-resolved photoelectron momentum distribution, allowing for complete reconstruction of the temporal and spectral profile of an attosecond burst. We show that this type of reconstruction algorithm is robust against counting noise and suitable for single-shot experiments. This algorithm holds potential for a variety of applications for attosecond pulse sources.

Characterizing isolated attosecond pulses with angular streaking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Sigi; Guo, Zhaoheng; Coffee, Ryan N.

We present a reconstruction algorithm for isolated attosecond pulses, which exploits the phase dependent energy modulation of a photoelectron ionized in the presence of a strong laser field. The energy modulation due to a circularly polarized laser field is manifest strongly in the angle-resolved photoelectron momentum distribution, allowing for complete reconstruction of the temporal and spectral profile of an attosecond burst. We show that this type of reconstruction algorithm is robust against counting noise and suitable for single-shot experiments. This algorithm holds potential for a variety of applications for attosecond pulse sources.

Characterizing isolated attosecond pulses with angular streaking

DOE PAGES

Li, Siqi; Guo, Zhaoheng; Coffee, Ryan N.; ...

2018-02-12

Here, we present a reconstruction algorithm for isolated attosecond pulses, which exploits the phase dependent energy modulation of a photoelectron ionized in the presence of a strong laser field. The energy modulation due to a circularly polarized laser field is manifest strongly in the angle-resolved photoelectron momentum distribution, allowing for complete reconstruction of the temporal and spectral profile of an attosecond burst. We show that this type of reconstruction algorithm is robust against counting noise and suitable for single-shot experiments. This algorithm holds potential for a variety of applications for attosecond pulse sources.

Characterizing isolated attosecond pulses with angular streaking

DOE PAGES

Li, Sigi; Guo, Zhaoheng; Coffee, Ryan N.; ...

2018-02-13

We present a reconstruction algorithm for isolated attosecond pulses, which exploits the phase dependent energy modulation of a photoelectron ionized in the presence of a strong laser field. The energy modulation due to a circularly polarized laser field is manifest strongly in the angle-resolved photoelectron momentum distribution, allowing for complete reconstruction of the temporal and spectral profile of an attosecond burst. We show that this type of reconstruction algorithm is robust against counting noise and suitable for single-shot experiments. This algorithm holds potential for a variety of applications for attosecond pulse sources.

Image-based 3D reconstruction and virtual environmental walk-through

NASA Astrophysics Data System (ADS)

Sun, Jifeng; Fang, Lixiong; Luo, Ying

2001-09-01

We present a 3D reconstruction method, which combines geometry-based modeling, image-based modeling and rendering techniques. The first component is an interactive geometry modeling method which recovery of the basic geometry of the photographed scene. The second component is model-based stereo algorithm. We discus the image processing problems and algorithms of walking through in virtual space, then designs and implement a high performance multi-thread wandering algorithm. The applications range from architectural planning and archaeological reconstruction to virtual environments and cinematic special effects.

Nonlinear Semi-Supervised Metric Learning Via Multiple Kernels and Local Topology.

PubMed

Li, Xin; Bai, Yanqin; Peng, Yaxin; Du, Shaoyi; Ying, Shihui

2018-03-01

Changing the metric on the data may change the data distribution, hence a good distance metric can promote the performance of learning algorithm. In this paper, we address the semi-supervised distance metric learning (ML) problem to obtain the best nonlinear metric for the data. First, we describe the nonlinear metric by the multiple kernel representation. By this approach, we project the data into a high dimensional space, where the data can be well represented by linear ML. Then, we reformulate the linear ML by a minimization problem on the positive definite matrix group. Finally, we develop a two-step algorithm for solving this model and design an intrinsic steepest descent algorithm to learn the positive definite metric matrix. Experimental results validate that our proposed method is effective and outperforms several state-of-the-art ML methods.

Quantum machine learning: a classical perspective

NASA Astrophysics Data System (ADS)

Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Rocchetto, Andrea; Severini, Simone; Wossnig, Leonard

2018-01-01

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

Quantum machine learning: a classical perspective

PubMed Central

Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Severini, Simone; Wossnig, Leonard

2018-01-01

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed. PMID:29434508

Quantum machine learning: a classical perspective.

PubMed

Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Rocchetto, Andrea; Severini, Simone; Wossnig, Leonard

2018-01-01

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

Accelerating Advanced MRI Reconstructions on GPUs

PubMed Central

Stone, S.S.; Haldar, J.P.; Tsao, S.C.; Hwu, W.-m.W.; Sutton, B.P.; Liang, Z.-P.

2008-01-01

Computational acceleration on graphics processing units (GPUs) can make advanced magnetic resonance imaging (MRI) reconstruction algorithms attractive in clinical settings, thereby improving the quality of MR images across a broad spectrum of applications. This paper describes the acceleration of such an algorithm on NVIDIA’s Quadro FX 5600. The reconstruction of a 3D image with 1283 voxels achieves up to 180 GFLOPS and requires just over one minute on the Quadro, while reconstruction on a quad-core CPU is twenty-one times slower. Furthermore, relative to the true image, the error exhibited by the advanced reconstruction is only 12%, while conventional reconstruction techniques incur error of 42%. PMID:21796230

Accelerating Advanced MRI Reconstructions on GPUs.

PubMed

Stone, S S; Haldar, J P; Tsao, S C; Hwu, W-M W; Sutton, B P; Liang, Z-P

2008-10-01

Computational acceleration on graphics processing units (GPUs) can make advanced magnetic resonance imaging (MRI) reconstruction algorithms attractive in clinical settings, thereby improving the quality of MR images across a broad spectrum of applications. This paper describes the acceleration of such an algorithm on NVIDIA's Quadro FX 5600. The reconstruction of a 3D image with 128(3) voxels achieves up to 180 GFLOPS and requires just over one minute on the Quadro, while reconstruction on a quad-core CPU is twenty-one times slower. Furthermore, relative to the true image, the error exhibited by the advanced reconstruction is only 12%, while conventional reconstruction techniques incur error of 42%.

Iterative image reconstruction for PROPELLER-MRI using the nonuniform fast fourier transform.

PubMed

Tamhane, Ashish A; Anastasio, Mark A; Gui, Minzhi; Arfanakis, Konstantinos

2010-07-01

To investigate an iterative image reconstruction algorithm using the nonuniform fast Fourier transform (NUFFT) for PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction) MRI. Numerical simulations, as well as experiments on a phantom and a healthy human subject were used to evaluate the performance of the iterative image reconstruction algorithm for PROPELLER, and compare it with that of conventional gridding. The trade-off between spatial resolution, signal to noise ratio, and image artifacts, was investigated for different values of the regularization parameter. The performance of the iterative image reconstruction algorithm in the presence of motion was also evaluated. It was demonstrated that, for a certain range of values of the regularization parameter, iterative reconstruction produced images with significantly increased signal to noise ratio, reduced artifacts, for similar spatial resolution, compared with gridding. Furthermore, the ability to reduce the effects of motion in PROPELLER-MRI was maintained when using the iterative reconstruction approach. An iterative image reconstruction technique based on the NUFFT was investigated for PROPELLER MRI. For a certain range of values of the regularization parameter, the new reconstruction technique may provide PROPELLER images with improved image quality compared with conventional gridding. (c) 2010 Wiley-Liss, Inc.

Iterative Image Reconstruction for PROPELLER-MRI using the NonUniform Fast Fourier Transform

PubMed Central

Tamhane, Ashish A.; Anastasio, Mark A.; Gui, Minzhi; Arfanakis, Konstantinos

2013-01-01

Purpose To investigate an iterative image reconstruction algorithm using the non-uniform fast Fourier transform (NUFFT) for PROPELLER (Periodically Rotated Overlapping parallEL Lines with Enhanced Reconstruction) MRI. Materials and Methods Numerical simulations, as well as experiments on a phantom and a healthy human subject were used to evaluate the performance of the iterative image reconstruction algorithm for PROPELLER, and compare it to that of conventional gridding. The trade-off between spatial resolution, signal to noise ratio, and image artifacts, was investigated for different values of the regularization parameter. The performance of the iterative image reconstruction algorithm in the presence of motion was also evaluated. Results It was demonstrated that, for a certain range of values of the regularization parameter, iterative reconstruction produced images with significantly increased SNR, reduced artifacts, for similar spatial resolution, compared to gridding. Furthermore, the ability to reduce the effects of motion in PROPELLER-MRI was maintained when using the iterative reconstruction approach. Conclusion An iterative image reconstruction technique based on the NUFFT was investigated for PROPELLER MRI. For a certain range of values of the regularization parameter the new reconstruction technique may provide PROPELLER images with improved image quality compared to conventional gridding. PMID:20578028

Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy

NASA Astrophysics Data System (ADS)

Tang, Jing; Rahmim, Arman

2015-01-01

A promising approach in PET image reconstruction is to incorporate high resolution anatomical information (measured from MR or CT) taking the anato-functional similarity measures such as mutual information or joint entropy (JE) as the prior. These similarity measures only classify voxels based on intensity values, while neglecting structural spatial information. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction algorithm wherein the JE measure is supplied by spatial information generated using wavelet multi-resolution analysis. The proposed wavelet-based JE (WJE) MAP algorithm involves calculation of derivatives of the subband JE measures with respect to individual PET image voxel intensities, which we have shown can be computed very similarly to how the inverse wavelet transform is implemented. We performed a simulation study with the BrainWeb phantom creating PET data corresponding to different noise levels. Realistically simulated T1-weighted MR images provided by BrainWeb modeling were applied in the anatomy-assisted reconstruction with the WJE-MAP algorithm and the intensity-only JE-MAP algorithm. Quantitative analysis showed that the WJE-MAP algorithm performed similarly to the JE-MAP algorithm at low noise level in the gray matter (GM) and white matter (WM) regions in terms of noise versus bias tradeoff. When noise increased to medium level in the simulated data, the WJE-MAP algorithm started to surpass the JE-MAP algorithm in the GM region, which is less uniform with smaller isolated structures compared to the WM region. In the high noise level simulation, the WJE-MAP algorithm presented clear improvement over the JE-MAP algorithm in both the GM and WM regions. In addition to the simulation study, we applied the reconstruction algorithms to real patient studies involving DPA-173 PET data and Florbetapir PET data with corresponding T1-MPRAGE MRI images. Compared to the intensity-only JE-MAP algorithm, the WJE-MAP algorithm resulted in comparable regional mean values to those from the maximum likelihood algorithm while reducing noise. Achieving robust performance in various noise-level simulation and patient studies, the WJE-MAP algorithm demonstrates its potential in clinical quantitative PET imaging.

The reconstruction algorithm used for [68Ga]PSMA-HBED-CC PET/CT reconstruction significantly influences the number of detected lymph node metastases and coeliac ganglia.

PubMed

Krohn, Thomas; Birmes, Anita; Winz, Oliver H; Drude, Natascha I; Mottaghy, Felix M; Behrendt, Florian F; Verburg, Frederik A

2017-04-01

To investigate whether the numbers of lymph node metastases and coeliac ganglia delineated on [ 68 Ga]PSMA-HBED-CC PET/CT scans differ among datasets generated using different reconstruction algorithms. Data were constructed using the BLOB-OS-TF, BLOB-OS and 3D-RAMLA algorithms. All reconstructions were assessed by two nuclear medicine physicians for the number of pelvic/paraaortal lymph node metastases as well the number of coeliac ganglia. Standardized uptake values (SUV) were also calculated in different regions. At least one [ 68 Ga]PSMA-HBED-CC PET/CT-positive pelvic or paraaortal lymph node metastasis was found in 49 and 35 patients using the BLOB-OS-TF algorithm, in 42 and 33 patients using the BLOB-OS algorithm, and in 41 and 31 patients using the 3D-RAMLA algorithm, respectively, and a positive ganglion was found in 92, 59 and 24 of 100 patients using the three algorithms, respectively. Quantitatively, the SUVmean and SUVmax were significantly higher with the BLOB-OS algorithm than with either the BLOB-OS-TF or the 3D-RAMLA algorithm in all measured regions (p < 0.001 for all comparisons). The differences between the SUVs with the BLOB-OS-TF- and 3D-RAMLA algorithms were not significant in the aorta (SUVmean, p = 0.93; SUVmax, p = 0.97) but were significant in all other regions (p < 0.001 in all cases). The SUVmean ganglion/gluteus ratio was significantly higher with the BLOB-OS-TF algorithm than with either the BLOB-OS or the 3D-RAMLA algorithm and was significantly higher with the BLOB-OS than with the 3D-RAMLA algorithm (p < 0.001 in all cases). The results of [ 68 Ga]PSMA-HBED-CC PET/CT are affected by the reconstruction algorithm used. The highest number of lesions and physiological structures will be visualized using a modern algorithm employing time-of-flight information.

Extending Three-Dimensional Weighted Cone Beam Filtered Backprojection (CB-FBP) Algorithm for Image Reconstruction in Volumetric CT at Low Helical Pitches

PubMed Central

Hsieh, Jiang; Nilsen, Roy A.; McOlash, Scott M.

2006-01-01

A three-dimensional (3D) weighted helical cone beam filtered backprojection (CB-FBP) algorithm (namely, original 3D weighted helical CB-FBP algorithm) has already been proposed to reconstruct images from the projection data acquired along a helical trajectory in angular ranges up to [0, 2 π]. However, an overscan is usually employed in the clinic to reconstruct tomographic images with superior noise characteristics at the most challenging anatomic structures, such as head and spine, extremity imaging, and CT angiography as well. To obtain the most achievable noise characteristics or dose efficiency in a helical overscan, we extended the 3D weighted helical CB-FBP algorithm to handle helical pitches that are smaller than 1: 1 (namely extended 3D weighted helical CB-FBP algorithm). By decomposing a helical over scan with an angular range of [0, 2π + Δβ] into a union of full scans corresponding to an angular range of [0, 2π], the extended 3D weighted function is a summation of all 3D weighting functions corresponding to each full scan. An experimental evaluation shows that the extended 3D weighted helical CB-FBP algorithm can improve noise characteristics or dose efficiency of the 3D weighted helical CB-FBP algorithm at a helical pitch smaller than 1: 1, while its reconstruction accuracy and computational efficiency are maintained. It is believed that, such an efficient CB reconstruction algorithm that can provide superior noise characteristics or dose efficiency at low helical pitches may find its extensive applications in CT medical imaging. PMID:23165031

Comparison of Reconstruction and Control algorithms on the ESO end-to-end simulator OCTOPUS

NASA Astrophysics Data System (ADS)

Montilla, I.; Béchet, C.; Lelouarn, M.; Correia, C.; Tallon, M.; Reyes, M.; Thiébaut, É.

Extremely Large Telescopes are very challenging concerning their Adaptive Optics requirements. Their diameters, the specifications demanded by the science for which they are being designed for, and the planned use of Extreme Adaptive Optics systems, imply a huge increment in the number of degrees of freedom in the deformable mirrors. It is necessary to study new reconstruction algorithms to implement the real time control in Adaptive Optics at the required speed. We have studied the performance, applied to the case of the European ELT, of three different algorithms: the matrix-vector multiplication (MVM) algorithm, considered as a reference; the Fractal Iterative Method (FrIM); and the Fourier Transform Reconstructor (FTR). The algorithms have been tested on ESO's OCTOPUS software, which simulates the atmosphere, the deformable mirror, the sensor and the closed-loop control. The MVM is the default reconstruction and control method implemented in OCTOPUS, but it scales in O(N2) operations per loop so it is not considered as a fast algorithm for wave-front reconstruction and control on an Extremely Large Telescope. The two other methods are the fast algorithms studied in the E-ELT Design Study. The performance, as well as their response in the presence of noise and with various atmospheric conditions, has been compared using a Single Conjugate Adaptive Optics configuration for a 42 m diameter ELT, with a total amount of 5402 actuators. Those comparisons made on a common simulator allow to enhance the pros and cons of the various methods, and give us a better understanding of the type of reconstruction algorithm that an ELT demands.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Naseri, M; Rajabi, H; Wang, J

Purpose: Respiration causes lesion smearing, image blurring and quality degradation, affecting lesion contrast and the ability to define correct lesion size. The spatial resolution of current multi pinhole SPECT (MPHS) scanners is sub-millimeter. Therefore, the effect of motion is more noticeable in comparison to conventional SPECT scanner. Gated imaging aims to reduce motion artifacts. A major issue in gating is the lack of statistics and individual reconstructed frames are noisy. The increased noise in each frame, deteriorates the quantitative accuracy of the MPHS Images. The objective of this work, is to enhance the image quality in 4D-MPHS imaging, by 4Dmore » image reconstruction. Methods: The new algorithm requires deformation vector fields (DVFs) that are calculated by non-rigid Demons registration. The algorithm is based on the motion-incorporated version of ordered subset expectation maximization (OSEM) algorithm. This iterative algorithm is capable to make full use of all projections to reconstruct each individual frame. To evaluate the performance of the proposed algorithm a simulation study was conducted. A fast ray tracing method was used to generate MPHS projections of a 4D digital mouse phantom with a small tumor in liver in eight different respiratory phases. To evaluate the 4D-OSEM algorithm potential, tumor to liver activity ratio was compared with other image reconstruction methods including 3D-MPHS and post reconstruction registered with Demons-derived DVFs. Results: Image quality of 4D-MPHS is greatly improved by the 4D-OSEM algorithm. When all projections are used to reconstruct a 3D-MPHS, motion blurring artifacts are present, leading to overestimation of the tumor size and 24% tumor contrast underestimation. This error reduced to 16% and 10% for post reconstruction registration methods and 4D-OSEM respectively. Conclusion: 4D-OSEM method can be used for motion correction in 4D-MPHS. The statistics and quantification are improved since all projection data are combined together to update the image.« less

SU-F-P-56: On a New Approach to Reconstruct the Patient Dose From Phantom Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bangtsson, E; Vries, W de

Purpose: The development of complex radiation treatment schemes emphasizes the need for advanced QA analysis methods to ensure patient safety. One such tool is the Delta4 DVH Anatomy software, where the patient dose is reconstructed from phantom measurements. Deviations in the measured dose are transferred to the patient anatomy and their clinical impact is evaluated in situ. Results from the original algorithm revealed weaknesses that may introduce artefacts in the reconstructed dose. These can lead to false negatives or obscure the effects of minor dose deviations from delivery failures. Here, we will present results from a new patient dose reconstructionmore » algorithm. Methods: The main steps of the new algorithm are: (1) the dose delivered to a phantom is measured in a number of detector positions. (2) The measured dose is compared to an internally calculated dose distribution evaluated in said positions. The so-obtained dose difference is (3) used to calculate an energy fluence difference. This entity is (4) used as input to a patient dose correction calculation routine. Finally, the patient dose is reconstructed by adding said patient dose correction to the planned patient dose. The internal dose calculation in step (2) and (4) is based on the Pencil Beam algorithm. Results: The new patient dose reconstruction algorithm have been tested on a number of patients and the standard metrics dose deviation (DDev), distance-to-agreement (DTA) and Gamma index are improved when compared to the original algorithm. In a certain case the Gamma index (3%/3mm) increases from 72.9% to 96.6%. Conclusion: The patient dose reconstruction algorithm is improved. This leads to a reduction in non-physical artefacts in the reconstructed patient dose. As a consequence, the possibility to detect deviations in the dose that is delivered to the patient is improved. An increase in Gamma index for the PTV can be seen. The corresponding author is an employee of ScandiDos.« less

A smoothed two- and three-dimensional interface reconstruction method

DOE PAGES

Mosso, Stewart; Garasi, Christopher; Drake, Richard

2008-04-22

The Patterned Interface Reconstruction algorithm reduces the discontinuity between material interfaces in neighboring computational elements. This smoothing improves the accuracy of the reconstruction for smooth bodies. The method can be used in two- and three-dimensional Cartesian and unstructured meshes. Planar interfaces will be returned for planar volume fraction distributions. Finally, the algorithm is second-order accurate for smooth volume fraction distributions.

BPF-type region-of-interest reconstruction for parallel translational computed tomography.

PubMed

Wu, Weiwen; Yu, Hengyong; Wang, Shaoyu; Liu, Fenglin

2017-01-01

The objective of this study is to present and test a new ultra-low-cost linear scan based tomography architecture. Similar to linear tomosynthesis, the source and detector are translated in opposite directions and the data acquisition system targets on a region-of-interest (ROI) to acquire data for image reconstruction. This kind of tomographic architecture was named parallel translational computed tomography (PTCT). In previous studies, filtered backprojection (FBP)-type algorithms were developed to reconstruct images from PTCT. However, the reconstructed ROI images from truncated projections have severe truncation artefact. In order to overcome this limitation, we in this study proposed two backprojection filtering (BPF)-type algorithms named MP-BPF and MZ-BPF to reconstruct ROI images from truncated PTCT data. A weight function is constructed to deal with data redundancy for multi-linear translations modes. Extensive numerical simulations are performed to evaluate the proposed MP-BPF and MZ-BPF algorithms for PTCT in fan-beam geometry. Qualitative and quantitative results demonstrate that the proposed BPF-type algorithms cannot only more accurately reconstruct ROI images from truncated projections but also generate high-quality images for the entire image support in some circumstances.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andreyev, A.

Purpose: Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR. Methods: To validate the proposed algorithm we used Monte Carlomore » simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background. Results: Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2–3 orders of magnitude per iteration. Conclusions: The results of our tests demonstrate the improvement of image resolution provided by the OE reconstructions with resolution recovery. The quality of images and their contrast are similar to those obtained from the OE reconstructions from scans simulated with perfect energy and spatial resolutions.« less

Measuring the performance of super-resolution reconstruction algorithms

NASA Astrophysics Data System (ADS)

Dijk, Judith; Schutte, Klamer; van Eekeren, Adam W. M.; Bijl, Piet

2012-06-01

For many military operations situational awareness is of great importance. This situational awareness and related tasks such as Target Acquisition can be acquired using cameras, of which the resolution is an important characteristic. Super resolution reconstruction algorithms can be used to improve the effective sensor resolution. In order to judge these algorithms and the conditions under which they operate best, performance evaluation methods are necessary. This evaluation, however, is not straightforward for several reasons. First of all, frequency-based evaluation techniques alone will not provide a correct answer, due to the fact that they are unable to discriminate between structure-related and noise-related effects. Secondly, most super-resolution packages perform additional image enhancement techniques such as noise reduction and edge enhancement. As these algorithms improve the results they cannot be evaluated separately. Thirdly, a single high-resolution ground truth is rarely available. Therefore, evaluation of the differences in high resolution between the estimated high resolution image and its ground truth is not that straightforward. Fourth, different artifacts can occur due to super-resolution reconstruction, which are not known on forehand and hence are difficult to evaluate. In this paper we present a set of new evaluation techniques to assess super-resolution reconstruction algorithms. Some of these evaluation techniques are derived from processing on dedicated (synthetic) imagery. Other evaluation techniques can be evaluated on both synthetic and natural images (real camera data). The result is a balanced set of evaluation algorithms that can be used to assess the performance of super-resolution reconstruction algorithms.

Angiographic CT: in vitro comparison of different carotid artery stents-does stent orientation matter?

PubMed

Lettau, Michael; Bendszus, Martin; Hähnel, Stefan

2013-06-01

Our aim was to evaluate the in vitro visualization of different carotid artery stents on angiographic CT (ACT). Of particular interest was the influence of stent orientation to the angiography system by measurement of artificial lumen narrowing (ALN) caused by the stent material within the stented vessel segment to determine whether ACT can be used to detect restenosis within the stent. ACT appearances of 17 carotid artery stents of different designs and sizes (4.0 to 11.0 mm) were investigated in vitro. Stents were placed in different orientations to the angiography system. Standard algorithm image reconstruction and stent-optimized algorithm image reconstruction was performed. For each stent, ALN was calculated. With standard algorithm image reconstruction, ALN ranged from 19.0 to 43.6 %. With stent-optimized algorithm image reconstruction, ALN was significantly lower and ranged from 8.2 to 18.7 %. Stent struts could be visualized in all stents. Differences in ALN between the different stent orientations to the angiography system were not significant. ACT evaluation of vessel patency after stent placement is possible but is impaired by ALN. Stent orientation of the stents to the angiography system did not significantly influence ALN. Stent-optimized algorithm image reconstruction decreases ALN but further research is required to define the visibility of in-stent stenosis depending on image reconstruction.

Imaging metallic samples using electrical capacitance tomography: forward modelling and reconstruction algorithms

NASA Astrophysics Data System (ADS)

Hosani, E. Al; Zhang, M.; Abascal, J. F. P. J.; Soleimani, M.

2016-11-01

Electrical capacitance tomography (ECT) is an imaging technology used to reconstruct the permittivity distribution within the sensing region. So far, ECT has been primarily used to image non-conductive media only, since if the conductivity of the imaged object is high, the capacitance measuring circuit will be almost shortened by the conductivity path and a clear image cannot be produced using the standard image reconstruction approaches. This paper tackles the problem of imaging metallic samples using conventional ECT systems by investigating the two main aspects of image reconstruction algorithms, namely the forward problem and the inverse problem. For the forward problem, two different methods to model the region of high conductivity in ECT is presented. On the other hand, for the inverse problem, three different algorithms to reconstruct the high contrast images are examined. The first two methods are the linear single step Tikhonov method and the iterative total variation regularization method, and use two sets of ECT data to reconstruct the image in time difference mode. The third method, namely the level set method, uses absolute ECT measurements and was developed using a metallic forward model. The results indicate that the applications of conventional ECT systems can be extended to metal samples using the suggested algorithms and forward model, especially using a level set algorithm to find the boundary of the metal.

A new approach for beam hardening correction based on the local spectrum distributions

NASA Astrophysics Data System (ADS)

Rasoulpour, Naser; Kamali-Asl, Alireza; Hemmati, Hamidreza

2015-09-01

Energy dependence of material absorption and polychromatic nature of x-ray beams in the Computed Tomography (CT) causes a phenomenon which called "beam hardening". The purpose of this study is to provide a novel approach for Beam Hardening (BH) correction. This approach is based on the linear attenuation coefficients of Local Spectrum Distributions (LSDs) in the various depths of a phantom. The proposed method includes two steps. Firstly, the hardened spectra in various depths of the phantom (or LSDs) are estimated based on the Expectation Maximization (EM) algorithm for arbitrary thickness interval of known materials in the phantom. The performance of LSD estimation technique is evaluated by applying random Gaussian noise to transmission data. Then, the linear attenuation coefficients with regarding to the mean energy of LSDs are obtained. Secondly, a correction function based on the calculated attenuation coefficients is derived in order to correct polychromatic raw data. Since a correction function has been used for the conversion of the polychromatic data to the monochromatic data, the effect of BH in proposed reconstruction must be reduced in comparison with polychromatic reconstruction. The proposed approach has been assessed in the phantoms which involve less than two materials, but the correction function has been extended for using in the constructed phantoms with more than two materials. The relative mean energy difference in the LSDs estimations based on the noise-free transmission data was less than 1.5%. Also, it shows an acceptable value when a random Gaussian noise is applied to the transmission data. The amount of cupping artifact in the proposed reconstruction method has been effectively reduced and proposed reconstruction profile is uniform more than polychromatic reconstruction profile.

Spectral reconstruction of signals from periodic nonuniform subsampling based on a Nyquist folding scheme

NASA Astrophysics Data System (ADS)

Jiang, Kaili; Zhu, Jun; Tang, Bin

2017-12-01

Periodic nonuniform sampling occurs in many applications, and the Nyquist folding receiver (NYFR) is an efficient, low complexity, and broadband spectrum sensing architecture. In this paper, we first derive that the radio frequency (RF) sample clock function of NYFR is periodic nonuniform. Then, the classical results of periodic nonuniform sampling are applied to NYFR. We extend the spectral reconstruction algorithm of time series decomposed model to the subsampling case by using the spectrum characteristics of NYFR. The subsampling case is common for broadband spectrum surveillance. Finally, we take example for a LFM signal under large bandwidth to verify the proposed algorithm and compare the spectral reconstruction algorithm with orthogonal matching pursuit (OMP) algorithm.

Blur kernel estimation with algebraic tomography technique and intensity profiles of object boundaries

NASA Astrophysics Data System (ADS)

Ingacheva, Anastasia; Chukalina, Marina; Khanipov, Timur; Nikolaev, Dmitry

2018-04-01

Motion blur caused by camera vibration is a common source of degradation in photographs. In this paper we study the problem of finding the point spread function (PSF) of a blurred image using the tomography technique. The PSF reconstruction result strongly depends on the particular tomography technique used. We present a tomography algorithm with regularization adapted specifically for this task. We use the algebraic reconstruction technique (ART algorithm) as the starting algorithm and introduce regularization. We use the conjugate gradient method for numerical implementation of the proposed approach. The algorithm is tested using a dataset which contains 9 kernels extracted from real photographs by the Adobe corporation where the point spread function is known. We also investigate influence of noise on the quality of image reconstruction and investigate how the number of projections influence the magnitude change of the reconstruction error.

Improving Cancer Detection and Dose Efficiency in Dedicated Breast Cancer CT

DTIC Science & Technology

2010-02-01

source trajectory and data truncation, which can however be solved with the back-projection filtration ( BPF ) algorithm [6,7]. I have used the BPF ...high to low radiation dose levels. I have investigated noise properties in images reconstructed by use of FDK and BPF algorithms at different noise...analytic algorithms such as the FDK and BPF algorithms are applied to sparse-view data, the reconstruction images will contain artifacts such as streak

Comparing performance of many-core CPUs and GPUs for static and motion compensated reconstruction of C-arm CT data.

PubMed

Hofmann, Hannes G; Keck, Benjamin; Rohkohl, Christopher; Hornegger, Joachim

2011-01-01

Interventional reconstruction of 3-D volumetric data from C-arm CT projections is a computationally demanding task. Hardware optimization is not an option but mandatory for interventional image processing and, in particular, for image reconstruction due to the high demands on performance. Several groups have published fast analytical 3-D reconstruction on highly parallel hardware such as GPUs to mitigate this issue. The authors show that the performance of modern CPU-based systems is in the same order as current GPUs for static 3-D reconstruction and outperforms them for a recent motion compensated (3-D+time) image reconstruction algorithm. This work investigates two algorithms: Static 3-D reconstruction as well as a recent motion compensated algorithm. The evaluation was performed using a standardized reconstruction benchmark, RABBITCT, to get comparable results and two additional clinical data sets. The authors demonstrate for a parametric B-spline motion estimation scheme that the derivative computation, which requires many write operations to memory, performs poorly on the GPU and can highly benefit from modern CPU architectures with large caches. Moreover, on a 32-core Intel Xeon server system, the authors achieve linear scaling with the number of cores used and reconstruction times almost in the same range as current GPUs. Algorithmic innovations in the field of motion compensated image reconstruction may lead to a shift back to CPUs in the future. For analytical 3-D reconstruction, the authors show that the gap between GPUs and CPUs became smaller. It can be performed in less than 20 s (on-the-fly) using a 32-core server.

3-Dimensional stereo implementation of photoacoustic imaging based on a new image reconstruction algorithm without using discrete Fourier transform

NASA Astrophysics Data System (ADS)

Ham, Woonchul; Song, Chulgyu

2017-05-01

In this paper, we propose a new three-dimensional stereo image reconstruction algorithm for a photoacoustic medical imaging system. We also introduce and discuss a new theoretical algorithm by using the physical concept of Radon transform. The main key concept of proposed theoretical algorithm is to evaluate the existence possibility of the acoustic source within a searching region by using the geometric distance between each sensor element of acoustic detector and the corresponding searching region denoted by grid. We derive the mathematical equation for the magnitude of the existence possibility which can be used for implementing a new proposed algorithm. We handle and derive mathematical equations of proposed algorithm for the one-dimensional sensing array case as well as two dimensional sensing array case too. A mathematical k-wave simulation data are used for comparing the image quality of the proposed algorithm with that of general conventional algorithm in which the FFT should be necessarily used. From the k-wave Matlab simulation results, we can prove the effectiveness of the proposed reconstruction algorithm.

Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh

Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. Tomore » alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.« less

Cone-beam reconstruction for the two-circles-plus-one-line trajectory

NASA Astrophysics Data System (ADS)

Lu, Yanbin; Yang, Jiansheng; Emerson, John W.; Mao, Heng; Zhou, Tie; Si, Yuanzheng; Jiang, Ming

2012-05-01

The Kodak Image Station In-Vivo FX has an x-ray module with cone-beam configuration for radiographic imaging but lacks the functionality of tomography. To introduce x-ray tomography into the system, we choose the two-circles-plus-one-line trajectory by mounting one translation motor and one rotation motor. We establish a reconstruction algorithm by applying the M-line reconstruction method. Numerical studies and preliminary physical phantom experiment demonstrate the feasibility of the proposed design and reconstruction algorithm.

Measurement and Structural Model Class Separation in Mixture CFA: ML/EM versus MCMC

ERIC Educational Resources Information Center

Depaoli, Sarah

2012-01-01

Parameter recovery was assessed within mixture confirmatory factor analysis across multiple estimator conditions under different simulated levels of mixture class separation. Mixture class separation was defined in the measurement model (through factor loadings) and the structural model (through factor variances). Maximum likelihood (ML) via the…

Fully 3D refraction correction dosimetry system.

PubMed

Manjappa, Rakesh; Makki, S Sharath; Kumar, Rajesh; Vasu, Ram Mohan; Kanhirodan, Rajan

2016-02-21

The irradiation of selective regions in a polymer gel dosimeter results in an increase in optical density and refractive index (RI) at those regions. An optical tomography-based dosimeter depends on rayline path through the dosimeter to estimate and reconstruct the dose distribution. The refraction of light passing through a dose region results in artefacts in the reconstructed images. These refraction errors are dependant on the scanning geometry and collection optics. We developed a fully 3D image reconstruction algorithm, algebraic reconstruction technique-refraction correction (ART-rc) that corrects for the refractive index mismatches present in a gel dosimeter scanner not only at the boundary, but also for any rayline refraction due to multiple dose regions inside the dosimeter. In this study, simulation and experimental studies have been carried out to reconstruct a 3D dose volume using 2D CCD measurements taken for various views. The study also focuses on the effectiveness of using different refractive-index matching media surrounding the gel dosimeter. Since the optical density is assumed to be low for a dosimeter, the filtered backprojection is routinely used for reconstruction. We carry out the reconstructions using conventional algebraic reconstruction (ART) and refractive index corrected ART (ART-rc) algorithms. The reconstructions based on FDK algorithm for cone-beam tomography has also been carried out for comparison. Line scanners and point detectors, are used to obtain reconstructions plane by plane. The rays passing through dose region with a RI mismatch does not reach the detector in the same plane depending on the angle of incidence and RI. In the fully 3D scanning setup using 2D array detectors, light rays that undergo refraction are still collected and hence can still be accounted for in the reconstruction algorithm. It is found that, for the central region of the dosimeter, the usable radius using ART-rc algorithm with water as RI matched medium is 71.8%, an increase of 6.4% compared to that achieved using conventional ART algorithm. Smaller diameter dosimeters are scanned with dry air scanning by using a wide-angle lens that collects refracted light. The images reconstructed using cone beam geometry is seen to deteriorate in some planes as those regions are not scanned. Refraction correction is important and needs to be taken in to consideration to achieve quantitatively accurate dose reconstructions. Refraction modeling is crucial in array based scanners as it is not possible to identify refracted rays in the sinogram space.

Parallel heterogeneous architectures for efficient OMP compressive sensing reconstruction

NASA Astrophysics Data System (ADS)

Kulkarni, Amey; Stanislaus, Jerome L.; Mohsenin, Tinoosh

2014-05-01

Compressive Sensing (CS) is a novel scheme, in which a signal that is sparse in a known transform domain can be reconstructed using fewer samples. The signal reconstruction techniques are computationally intensive and have sluggish performance, which make them impractical for real-time processing applications . The paper presents novel architectures for Orthogonal Matching Pursuit algorithm, one of the popular CS reconstruction algorithms. We show the implementation results of proposed architectures on FPGA, ASIC and on a custom many-core platform. For FPGA and ASIC implementation, a novel thresholding method is used to reduce the processing time for the optimization problem by at least 25%. Whereas, for the custom many-core platform, efficient parallelization techniques are applied, to reconstruct signals with variant signal lengths of N and sparsity of m. The algorithm is divided into three kernels. Each kernel is parallelized to reduce execution time, whereas efficient reuse of the matrix operators allows us to reduce area. Matrix operations are efficiently paralellized by taking advantage of blocked algorithms. For demonstration purpose, all architectures reconstruct a 256-length signal with maximum sparsity of 8 using 64 measurements. Implementation on Xilinx Virtex-5 FPGA, requires 27.14 μs to reconstruct the signal using basic OMP. Whereas, with thresholding method it requires 18 μs. ASIC implementation reconstructs the signal in 13 μs. However, our custom many-core, operating at 1.18 GHz, takes 18.28 μs to complete. Our results show that compared to the previous published work of the same algorithm and matrix size, proposed architectures for FPGA and ASIC implementations perform 1.3x and 1.8x respectively faster. Also, the proposed many-core implementation performs 3000x faster than the CPU and 2000x faster than the GPU.

Application of composite dictionary multi-atom matching in gear fault diagnosis.

PubMed

Cui, Lingli; Kang, Chenhui; Wang, Huaqing; Chen, Peng

2011-01-01

The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary combining the impulse time-frequency dictionary and the Fourier dictionary was constituted, and a genetic algorithm was applied to search for the best matching atom. The analysis results of gear fault simulation signals indicated the effectiveness of the hard threshold, and the impulse or harmonic characteristic components could be separately extracted. Meanwhile, the robustness of the composite dictionary multi-atom matching algorithm at different noise levels was investigated. Aiming at the effects of data lengths on the calculation efficiency of the algorithm, an improved segmented decomposition and reconstruction algorithm was proposed, and the calculation efficiency of the decomposition algorithm was significantly enhanced. In addition it is shown that the multi-atom matching algorithm was superior to the single-atom matching algorithm in both calculation efficiency and algorithm robustness. Finally, the above algorithm was applied to gear fault engineering signals, and achieved good results.

An object-oriented simulator for 3D digital breast tomosynthesis imaging system.

PubMed

Seyyedi, Saeed; Cengiz, Kubra; Kamasak, Mustafa; Yildirim, Isa

2013-01-01

Digital breast tomosynthesis (DBT) is an innovative imaging modality that provides 3D reconstructed images of breast to detect the breast cancer. Projections obtained with an X-ray source moving in a limited angle interval are used to reconstruct 3D image of breast. Several reconstruction algorithms are available for DBT imaging. Filtered back projection algorithm has traditionally been used to reconstruct images from projections. Iterative reconstruction algorithms such as algebraic reconstruction technique (ART) were later developed. Recently, compressed sensing based methods have been proposed in tomosynthesis imaging problem. We have developed an object-oriented simulator for 3D digital breast tomosynthesis (DBT) imaging system using C++ programming language. The simulator is capable of implementing different iterative and compressed sensing based reconstruction methods on 3D digital tomosynthesis data sets and phantom models. A user friendly graphical user interface (GUI) helps users to select and run the desired methods on the designed phantom models or real data sets. The simulator has been tested on a phantom study that simulates breast tomosynthesis imaging problem. Results obtained with various methods including algebraic reconstruction technique (ART) and total variation regularized reconstruction techniques (ART+TV) are presented. Reconstruction results of the methods are compared both visually and quantitatively by evaluating performances of the methods using mean structural similarity (MSSIM) values.

An Object-Oriented Simulator for 3D Digital Breast Tomosynthesis Imaging System

PubMed Central

Cengiz, Kubra

2013-01-01

Digital breast tomosynthesis (DBT) is an innovative imaging modality that provides 3D reconstructed images of breast to detect the breast cancer. Projections obtained with an X-ray source moving in a limited angle interval are used to reconstruct 3D image of breast. Several reconstruction algorithms are available for DBT imaging. Filtered back projection algorithm has traditionally been used to reconstruct images from projections. Iterative reconstruction algorithms such as algebraic reconstruction technique (ART) were later developed. Recently, compressed sensing based methods have been proposed in tomosynthesis imaging problem. We have developed an object-oriented simulator for 3D digital breast tomosynthesis (DBT) imaging system using C++ programming language. The simulator is capable of implementing different iterative and compressed sensing based reconstruction methods on 3D digital tomosynthesis data sets and phantom models. A user friendly graphical user interface (GUI) helps users to select and run the desired methods on the designed phantom models or real data sets. The simulator has been tested on a phantom study that simulates breast tomosynthesis imaging problem. Results obtained with various methods including algebraic reconstruction technique (ART) and total variation regularized reconstruction techniques (ART+TV) are presented. Reconstruction results of the methods are compared both visually and quantitatively by evaluating performances of the methods using mean structural similarity (MSSIM) values. PMID:24371468

A study of reconstruction accuracy for a cardiac SPECT system with multi-segmental collimation

NASA Astrophysics Data System (ADS)

Yu, D.-C.; Chang, W.; Pan, T.-S.

1997-06-01

To improve the geometric efficiency of cardiac SPECT imaging, the authors previously proposed to use a multi-segmental collimation with a cylindrical geometry. The proposed collimator consists of multiple parallel-hole collimators with most of the segments directed toward a small central region, where the patient's heart should be positioned. This technique provides a significantly increased detection efficiency for the central region, but at the expense of reduced efficiency for the surrounding region. The authors have used computer simulations to evaluate the implication of this technique on the accuracy of the reconstructed cardiac images. Two imaging situations were simulated: 1) the heart well placed inside the central region, and 2) the heart shifted and partially outside the central region. A neighboring high-uptake liver was simulated for both imaging situations. The images were reconstructed and corrected for attenuation with ML-EM and OS-FM methods using a complete attenuation map. The results indicate that errors caused by projection truncation are not significant and are not strongly dependent on the activity of the liver when the heart is well positioned within the central region. When the heart is partially outside the central region, hybrid emission data (a combination of high-count projections from the central region and low-count projections from the background region) can be used to restore the activity of the truncated section of the myocardium. However, the variance of the image in the section of the myocardium outside the central region is increased by 2-3 times when 10% of the collimator segments are used to image the background region.

Challenges of microtome‐based serial block‐face scanning electron microscopy in neuroscience

PubMed Central

WANNER, A. A.; KIRSCHMANN, M. A.

2015-01-01

Summary Serial block‐face scanning electron microscopy (SBEM) is becoming increasingly popular for a wide range of applications in many disciplines from biology to material sciences. This review focuses on applications for circuit reconstruction in neuroscience, which is one of the major driving forces advancing SBEM. Neuronal circuit reconstruction poses exceptional challenges to volume EM in terms of resolution, field of view, acquisition time and sample preparation. Mapping the connections between neurons in the brain is crucial for understanding information flow and information processing in the brain. However, information on the connectivity between hundreds or even thousands of neurons densely packed in neuronal microcircuits is still largely missing. Volume EM techniques such as serial section TEM, automated tape‐collecting ultramicrotome, focused ion‐beam scanning electron microscopy and SBEM (microtome serial block‐face scanning electron microscopy) are the techniques that provide sufficient resolution to resolve ultrastructural details such as synapses and provides sufficient field of view for dense reconstruction of neuronal circuits. While volume EM techniques are advancing, they are generating large data sets on the terabyte scale that require new image processing workflows and analysis tools. In this review, we present the recent advances in SBEM for circuit reconstruction in neuroscience and an overview of existing image processing and analysis pipelines. PMID:25907464

Image reconstruction algorithms for electrical capacitance tomography based on ROF model using new numerical techniques

NASA Astrophysics Data System (ADS)

Chen, Jiaoxuan; Zhang, Maomao; Liu, Yinyan; Chen, Jiaoliao; Li, Yi

2017-03-01

Electrical capacitance tomography (ECT) is a promising technique applied in many fields. However, the solutions for ECT are not unique and highly sensitive to the measurement noise. To remain a good shape of reconstructed object and endure a noisy data, a Rudin-Osher-Fatemi (ROF) model with total variation regularization is applied to image reconstruction in ECT. Two numerical methods, which are simplified augmented Lagrangian (SAL) and accelerated alternating direction method of multipliers (AADMM), are innovatively introduced to try to solve the above mentioned problems in ECT. The effect of the parameters and the number of iterations for different algorithms, and the noise level in capacitance data are discussed. Both simulation and experimental tests were carried out to validate the feasibility of the proposed algorithms, compared to the Landweber iteration (LI) algorithm. The results show that the SAL and AADMM algorithms can handle a high level of noise and the AADMM algorithm outperforms other algorithms in identifying the object from its background.

Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration

PubMed Central

Yoon, Young-Gyu; Dai, Peilun; Wohlwend, Jeremy; Chang, Jae-Byum; Marblestone, Adam H.; Boyden, Edward S.

2017-01-01

We here introduce and study the properties, via computer simulation, of a candidate automated approach to algorithmic reconstruction of dense neural morphology, based on simulated data of the kind that would be obtained via two emerging molecular technologies—expansion microscopy (ExM) and in-situ molecular barcoding. We utilize a convolutional neural network to detect neuronal boundaries from protein-tagged plasma membrane images obtained via ExM, as well as a subsequent supervoxel-merging pipeline guided by optical readout of information-rich, cell-specific nucleic acid barcodes. We attempt to use conservative imaging and labeling parameters, with the goal of establishing a baseline case that points to the potential feasibility of optical circuit reconstruction, leaving open the possibility of higher-performance labeling technologies and algorithms. We find that, even with these conservative assumptions, an all-optical approach to dense neural morphology reconstruction may be possible via the proposed algorithmic framework. Future work should explore both the design-space of chemical labels and barcodes, as well as algorithms, to ultimately enable routine, high-performance optical circuit reconstruction. PMID:29114215

Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration.

PubMed

Yoon, Young-Gyu; Dai, Peilun; Wohlwend, Jeremy; Chang, Jae-Byum; Marblestone, Adam H; Boyden, Edward S

2017-01-01

We here introduce and study the properties, via computer simulation, of a candidate automated approach to algorithmic reconstruction of dense neural morphology, based on simulated data of the kind that would be obtained via two emerging molecular technologies-expansion microscopy (ExM) and in-situ molecular barcoding. We utilize a convolutional neural network to detect neuronal boundaries from protein-tagged plasma membrane images obtained via ExM, as well as a subsequent supervoxel-merging pipeline guided by optical readout of information-rich, cell-specific nucleic acid barcodes. We attempt to use conservative imaging and labeling parameters, with the goal of establishing a baseline case that points to the potential feasibility of optical circuit reconstruction, leaving open the possibility of higher-performance labeling technologies and algorithms. We find that, even with these conservative assumptions, an all-optical approach to dense neural morphology reconstruction may be possible via the proposed algorithmic framework. Future work should explore both the design-space of chemical labels and barcodes, as well as algorithms, to ultimately enable routine, high-performance optical circuit reconstruction.

Pan-sharpening via compressed superresolution reconstruction and multidictionary learning

NASA Astrophysics Data System (ADS)

Shi, Cheng; Liu, Fang; Li, Lingling; Jiao, Licheng; Hao, Hongxia; Shang, Ronghua; Li, Yangyang

2018-01-01

In recent compressed sensing (CS)-based pan-sharpening algorithms, pan-sharpening performance is affected by two key problems. One is that there are always errors between the high-resolution panchromatic (HRP) image and the linear weighted high-resolution multispectral (HRM) image, resulting in spatial and spectral information lost. The other is that the dictionary construction process depends on the nontruth training samples. These problems have limited applications to CS-based pan-sharpening algorithm. To solve these two problems, we propose a pan-sharpening algorithm via compressed superresolution reconstruction and multidictionary learning. Through a two-stage implementation, compressed superresolution reconstruction model reduces the error effectively between the HRP and the linear weighted HRM images. Meanwhile, the multidictionary with ridgelet and curvelet is learned for both the two stages in the superresolution reconstruction process. Since ridgelet and curvelet can better capture the structure and directional characteristics, a better reconstruction result can be obtained. Experiments are done on the QuickBird and IKONOS satellites images. The results indicate that the proposed algorithm is competitive compared with the recent CS-based pan-sharpening methods and other well-known methods.

Study of Electromagnetic Interactions with the MicroBooNE Detector

NASA Astrophysics Data System (ADS)

Caratelli, David; MicroBooNE Collaboration

2017-01-01

MicroBooNE is an experiment which employs the Liquid Argon Time Projection Chamber (LArTPC) detector technology to study neutrinos produced with the Fermilab Booster Neutrino Beam. As for any accelerator-based detector interested in studying neutrino oscillations, it is essential to be able to identify and reconstruct the kinematic properties of electrons and photons produced in μν and νe interactions. We report current progress in reconstructing electron and photon electromagnetic (EM) showers using data from the MicroBooNE LArTPC. These studies cover EM showers in the tens to hundreds of MeV energy range; they lay the foundation for MicroBooNE's investigation of the excess of low-energy EM events reported by MiniBooNE, and are of interest to the wider LArTPC neutrino community.

MR image reconstruction via guided filter.

PubMed

Huang, Heyan; Yang, Hang; Wang, Kang

2018-04-01

Magnetic resonance imaging (MRI) reconstruction from the smallest possible set of Fourier samples has been a difficult problem in medical imaging field. In our paper, we present a new approach based on a guided filter for efficient MRI recovery algorithm. The guided filter is an edge-preserving smoothing operator and has better behaviors near edges than the bilateral filter. Our reconstruction method is consist of two steps. First, we propose two cost functions which could be computed efficiently and thus obtain two different images. Second, the guided filter is used with these two obtained images for efficient edge-preserving filtering, and one image is used as the guidance image, the other one is used as a filtered image in the guided filter. In our reconstruction algorithm, we can obtain more details by introducing guided filter. We compare our reconstruction algorithm with some competitive MRI reconstruction techniques in terms of PSNR and visual quality. Simulation results are given to show the performance of our new method.

EM in high-dimensional spaces.

PubMed

Draper, Bruce A; Elliott, Daniel L; Hayes, Jeremy; Baek, Kyungim

2005-06-01

This paper considers fitting a mixture of Gaussians model to high-dimensional data in scenarios where there are fewer data samples than feature dimensions. Issues that arise when using principal component analysis (PCA) to represent Gaussian distributions inside Expectation-Maximization (EM) are addressed, and a practical algorithm results. Unlike other algorithms that have been proposed, this algorithm does not try to compress the data to fit low-dimensional models. Instead, it models Gaussian distributions in the (N - 1)-dimensional space spanned by the N data samples. We are able to show that this algorithm converges on data sets where low-dimensional techniques do not.

Incoherent-scatter computed tomography with monochromatic synchrotron x ray: feasibility of multi-CT imaging system for simultaneous measurement-of fluorescent and incoherent scatter x rays

NASA Astrophysics Data System (ADS)

Yuasa, T.; Akiba, M.; Takeda, T.; Kazama, M.; Hoshino, A.; Watanabe, Y.; Hyodo, K.; Dilmanian, F. A.; Akatsuka, T.; Itai, Y.

1997-10-01

We describe a new system of incoherent scatter computed tomography (ISCT) using monochromatic synchrotron X rays, and we discuss its potential to be used in in vivo imaging for medical use. The system operates on the basis of computed tomography (CT) of the first generation. The reconstruction method for ISCT uses the least squares method with singular value decomposition. The research was carried out at the BLNE-5A bending magnet beam line of the Tristan Accumulation Ring in KEK, Japan. An acrylic cylindrical phantom of 20-mm diameter containing a cross-shaped channel was imaged. The channel was filled with a diluted iodine solution with a concentration of 200 /spl mu/gI/ml. Spectra obtained with the system's high purity germanium (HPGe) detector separated the incoherent X-ray line from the other notable peaks, i.e., the iK/sub /spl alpha// and K/sub /spl beta/1/ X-ray fluorescent lines and the coherent scattering peak. CT images were reconstructed from projections generated by integrating the counts In the energy window centering around the incoherent scattering peak and whose width was approximately 2 keV. The reconstruction routine employed an X-ray attenuation correction algorithm. The resulting image showed more homogeneity than one without the attenuation correction.

The role of advanced reconstruction algorithms in cardiac CT

PubMed Central

Halliburton, Sandra S.; Tanabe, Yuki; Partovi, Sasan

2017-01-01

Non-linear iterative reconstruction (IR) algorithms have been increasingly incorporated into clinical cardiac CT protocols at institutions around the world. Multiple IR algorithms are available commercially from various vendors. IR algorithms decrease image noise and are primarily used to enable lower radiation dose protocols. IR can also be used to improve image quality for imaging of obese patients, coronary atherosclerotic plaques, coronary stents, and myocardial perfusion. In this article, we will review the various applications of IR algorithms in cardiac imaging and evaluate how they have changed practice. PMID:29255694

Efficient iterative image reconstruction algorithm for dedicated breast CT

NASA Astrophysics Data System (ADS)

Antropova, Natalia; Sanchez, Adrian; Reiser, Ingrid S.; Sidky, Emil Y.; Boone, John; Pan, Xiaochuan

2016-03-01

Dedicated breast computed tomography (bCT) is currently being studied as a potential screening method for breast cancer. The X-ray exposure is set low to achieve an average glandular dose comparable to that of mammography, yielding projection data that contains high levels of noise. Iterative image reconstruction (IIR) algorithms may be well-suited for the system since they potentially reduce the effects of noise in the reconstructed images. However, IIR outcomes can be difficult to control since the algorithm parameters do not directly correspond to the image properties. Also, IIR algorithms are computationally demanding and have optimal parameter settings that depend on the size and shape of the breast and positioning of the patient. In this work, we design an efficient IIR algorithm with meaningful parameter specifications and that can be used on a large, diverse sample of bCT cases. The flexibility and efficiency of this method comes from having the final image produced by a linear combination of two separately reconstructed images - one containing gray level information and the other with enhanced high frequency components. Both of the images result from few iterations of separate IIR algorithms. The proposed algorithm depends on two parameters both of which have a well-defined impact on image quality. The algorithm is applied to numerous bCT cases from a dedicated bCT prototype system developed at University of California, Davis.

Monte Carlo simulation of PET and SPECT imaging of {sup 90}Y

DOE Office of Scientific and Technical Information (OSTI.GOV)

Takahashi, Akihiko, E-mail: takahsr@hs.med.kyushu-u.ac.jp; Sasaki, Masayuki; Himuro, Kazuhiko

2015-04-15

Purpose: Yittrium-90 ({sup 90}Y) is traditionally thought of as a pure beta emitter, and is used in targeted radionuclide therapy, with imaging performed using bremsstrahlung single-photon emission computed tomography (SPECT). However, because {sup 90}Y also emits positrons through internal pair production with a very small branching ratio, positron emission tomography (PET) imaging is also available. Because of the insufficient image quality of {sup 90}Y bremsstrahlung SPECT, PET imaging has been suggested as an alternative. In this paper, the authors present the Monte Carlo-based simulation–reconstruction framework for {sup 90}Y to comprehensively analyze the PET and SPECT imaging techniques and to quantitativelymore » consider the disadvantages associated with them. Methods: Our PET and SPECT simulation modules were developed using Monte Carlo simulation of Electrons and Photons (MCEP), developed by Dr. S. Uehara. PET code (MCEP-PET) generates a sinogram, and reconstructs the tomography image using a time-of-flight ordered subset expectation maximization (TOF-OSEM) algorithm with attenuation compensation. To evaluate MCEP-PET, simulated results of {sup 18}F PET imaging were compared with the experimental results. The results confirmed that MCEP-PET can simulate the experimental results very well. The SPECT code (MCEP-SPECT) models the collimator and NaI detector system, and generates the projection images and projection data. To save the computational time, the authors adopt the prerecorded {sup 90}Y bremsstrahlung photon data calculated by MCEP. The projection data are also reconstructed using the OSEM algorithm. The authors simulated PET and SPECT images of a water phantom containing six hot spheres filled with different concentrations of {sup 90}Y without background activity. The amount of activity was 163 MBq, with an acquisition time of 40 min. Results: The simulated {sup 90}Y-PET image accurately simulated the experimental results. PET image is visually superior to SPECT image because of the low background noise. The simulation reveals that the detected photon number in SPECT is comparable to that of PET, but the large fraction (approximately 75%) of scattered and penetration photons contaminates SPECT image. The lower limit of {sup 90}Y detection in SPECT image was approximately 200 kBq/ml, while that in PET image was approximately 100 kBq/ml. Conclusions: By comparing the background noise level and the image concentration profile of both the techniques, PET image quality was determined to be superior to that of bremsstrahlung SPECT. The developed simulation codes will be very useful in the future investigations of PET and bremsstrahlung SPECT imaging of {sup 90}Y.« less

Three-dimensional photoacoustic tomography based on graphics-processing-unit-accelerated finite element method.

PubMed

Peng, Kuan; He, Ling; Zhu, Ziqiang; Tang, Jingtian; Xiao, Jiaying

2013-12-01

Compared with commonly used analytical reconstruction methods, the frequency-domain finite element method (FEM) based approach has proven to be an accurate and flexible algorithm for photoacoustic tomography. However, the FEM-based algorithm is computationally demanding, especially for three-dimensional cases. To enhance the algorithm's efficiency, in this work a parallel computational strategy is implemented in the framework of the FEM-based reconstruction algorithm using a graphic-processing-unit parallel frame named the "compute unified device architecture." A series of simulation experiments is carried out to test the accuracy and accelerating effect of the improved method. The results obtained indicate that the parallel calculation does not change the accuracy of the reconstruction algorithm, while its computational cost is significantly reduced by a factor of 38.9 with a GTX 580 graphics card using the improved method.

Missing texture reconstruction method based on error reduction algorithm using Fourier transform magnitude estimation scheme.

PubMed

Ogawa, Takahiro; Haseyama, Miki

2013-03-01

A missing texture reconstruction method based on an error reduction (ER) algorithm, including a novel estimation scheme of Fourier transform magnitudes is presented in this brief. In our method, Fourier transform magnitude is estimated for a target patch including missing areas, and the missing intensities are estimated by retrieving its phase based on the ER algorithm. Specifically, by monitoring errors converged in the ER algorithm, known patches whose Fourier transform magnitudes are similar to that of the target patch are selected from the target image. In the second approach, the Fourier transform magnitude of the target patch is estimated from those of the selected known patches and their corresponding errors. Consequently, by using the ER algorithm, we can estimate both the Fourier transform magnitudes and phases to reconstruct the missing areas.

The Evolution of Complex Microsurgical Midface Reconstruction: A Classification Scheme and Reconstructive Algorithm.

PubMed

Alam, Daniel; Ali, Yaseen; Klem, Christopher; Coventry, Daniel

2016-11-01

Orbito-malar reconstruction after oncological resection represents one of the most challenging facial reconstructive procedures. Until the last few decades, rehabilitation was typically prosthesis based with a limited role for surgery. The advent of microsurgical techniques allowed large-volume tissue reconstitution from a distant donor site, revolutionizing the potential approaches to these defects. The authors report a novel surgery-based algorithm and a classification scheme for complete midface reconstruction with a foundation in the Gillies principles of like-to-like reconstruction and with a significant role of computer-aided virtual planning. With this approach, the authors have been able to achieve significantly better patient outcomes. Copyright © 2016 Elsevier Inc. All rights reserved.

Two-dimensional wavefront reconstruction based on double-shearing and least squares fitting

NASA Astrophysics Data System (ADS)

Liang, Peiying; Ding, Jianping; Zhu, Yangqing; Dong, Qian; Huang, Yuhua; Zhu, Zhen

2017-06-01

The two-dimensional wavefront reconstruction method based on double-shearing and least squares fitting is proposed in this paper. Four one-dimensional phase estimates of the measured wavefront, which correspond to the two shears and the two orthogonal directions, could be calculated from the differential phase, which solves the problem of the missing spectrum, and then by using the least squares method the two-dimensional wavefront reconstruction could be done. The numerical simulations of the proposed algorithm are carried out to verify the feasibility of this method. The influence of noise generated from different shear amount and different intensity on the accuracy of the reconstruction is studied and compared with the results from the algorithm based on single-shearing and least squares fitting. Finally, a two-grating lateral shearing interference experiment is carried out to verify the wavefront reconstruction algorithm based on doubleshearing and least squares fitting.

A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning.

PubMed

Zhang, Shang; Dong, Yuhan; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

2018-02-22

The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer.

A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning

PubMed Central

Zhang, Shang; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

2018-01-01

The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer. PMID:29470406

The E-Step of the MGROUP EM Algorithm. Program Statistics Research Technical Report No. 93-37.

ERIC Educational Resources Information Center

Thomas, Neal

Mislevy (1984, 1985) introduced an EM algorithm for estimating the parameters of a latent distribution model that is used extensively by the National Assessment of Educational Progress. Second order asymptotic corrections are derived and applied along with more common first order asymptotic corrections to approximate the expectations required by…

Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM

NASA Astrophysics Data System (ADS)

Jiji, G. Wiselin; Dehmeshki, Jamshid

2014-04-01

Tissue classification in computed tomography (CT) brain images is an important issue in the analysis of several brain dementias. A combination of different approaches for the segmentation of brain images is presented in this paper. A multi resolution algorithm is proposed along with scaled versions using Gaussian filter and wavelet analysis that extends expectation maximization (EM) algorithm. It is found that it is less sensitive to noise and got more accurate image segmentation than traditional EM. Moreover the algorithm has been applied on 20 sets of CT of the human brain and compared with other works. The segmentation results show the advantages of the proposed work have achieved more promising results and the results have been tested with Doctors.

Superiorization-based multi-energy CT image reconstruction

PubMed Central

Yang, Q; Cong, W; Wang, G

2017-01-01

The recently-developed superiorization approach is efficient and robust for solving various constrained optimization problems. This methodology can be applied to multi-energy CT image reconstruction with the regularization in terms of the prior rank, intensity and sparsity model (PRISM). In this paper, we propose a superiorized version of the simultaneous algebraic reconstruction technique (SART) based on the PRISM model. Then, we compare the proposed superiorized algorithm with the Split-Bregman algorithm in numerical experiments. The results show that both the Superiorized-SART and the Split-Bregman algorithms generate good results with weak noise and reduced artefacts. PMID:28983142

MR fingerprinting reconstruction with Kalman filter.

PubMed

Zhang, Xiaodi; Zhou, Zechen; Chen, Shiyang; Chen, Shuo; Li, Rui; Hu, Xiaoping

2017-09-01

Magnetic resonance fingerprinting (MR fingerprinting or MRF) is a newly introduced quantitative magnetic resonance imaging technique, which enables simultaneous multi-parameter mapping in a single acquisition with improved time efficiency. The current MRF reconstruction method is based on dictionary matching, which may be limited by the discrete and finite nature of the dictionary and the computational cost associated with dictionary construction, storage and matching. In this paper, we describe a reconstruction method based on Kalman filter for MRF, which avoids the use of dictionary to obtain continuous MR parameter measurements. With this Kalman filter framework, the Bloch equation of inversion-recovery balanced steady state free-precession (IR-bSSFP) MRF sequence was derived to predict signal evolution, and acquired signal was entered to update the prediction. The algorithm can gradually estimate the accurate MR parameters during the recursive calculation. Single pixel and numeric brain phantom simulation were implemented with Kalman filter and the results were compared with those from dictionary matching reconstruction algorithm to demonstrate the feasibility and assess the performance of Kalman filter algorithm. The results demonstrated that Kalman filter algorithm is applicable for MRF reconstruction, eliminating the need for a pre-define dictionary and obtaining continuous MR parameter in contrast to the dictionary matching algorithm. Copyright © 2017 Elsevier Inc. All rights reserved.

On the assessment of spatial resolution of PET systems with iterative image reconstruction

NASA Astrophysics Data System (ADS)

Gong, Kuang; Cherry, Simon R.; Qi, Jinyi

2016-03-01

Spatial resolution is an important metric for performance characterization in PET systems. Measuring spatial resolution is straightforward with a linear reconstruction algorithm, such as filtered backprojection, and can be performed by reconstructing a point source scan and calculating the full-width-at-half-maximum (FWHM) along the principal directions. With the widespread adoption of iterative reconstruction methods, it is desirable to quantify the spatial resolution using an iterative reconstruction algorithm. However, the task can be difficult because the reconstruction algorithms are nonlinear and the non-negativity constraint can artificially enhance the apparent spatial resolution if a point source image is reconstructed without any background. Thus, it was recommended that a background should be added to the point source data before reconstruction for resolution measurement. However, there has been no detailed study on the effect of the point source contrast on the measured spatial resolution. Here we use point source scans from a preclinical PET scanner to investigate the relationship between measured spatial resolution and the point source contrast. We also evaluate whether the reconstruction of an isolated point source is predictive of the ability of the system to resolve two adjacent point sources. Our results indicate that when the point source contrast is below a certain threshold, the measured FWHM remains stable. Once the contrast is above the threshold, the measured FWHM monotonically decreases with increasing point source contrast. In addition, the measured FWHM also monotonically decreases with iteration number for maximum likelihood estimate. Therefore, when measuring system resolution with an iterative reconstruction algorithm, we recommend using a low-contrast point source and a fixed number of iterations.