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.

A Note on Alternating Minimization Algorithm for the Matrix Completion Problem

DOE PAGES

Gamarnik, David; Misra, Sidhant

2016-06-06

Here, we consider the problem of reconstructing a low-rank matrix from a subset of its entries and analyze two variants of the so-called alternating minimization algorithm, which has been proposed in the past.We establish that when the underlying matrix has rank one, has positive bounded entries, and the graph underlying the revealed entries has diameter which is logarithmic in the size of the matrix, both algorithms succeed in reconstructing the matrix approximately in polynomial time starting from an arbitrary initialization.We further provide simulation results which suggest that the second variant which is based on the message passing type updates performsmore » significantly better.« less

Spectral Retrieval of Latent Heating Profiles from TRMM PR data. Part 3; Moistening Estimates over Tropical Ocean Regions

NASA Technical Reports Server (NTRS)

Shige, S.; Takayabu, Y.; Tao, W.-K.

2007-01-01

The global hydrological cycle is central to the Earth's climate system, with rainfall and the physics of precipitation formation acting as the key links in the cycle. Two-thirds of global rainfall occurs in the tropics with the associated latent heating (LH) accounting for threefourths of the total heat energy available to the Earth's atmosphere. In the last decade, it has been established that standard products of LH from satellite measurements, particularly TRMM measurements, would be a valuable resource for scientific research and applications. Such products would enable new insights and investigations concerning the complexities of convection system life cycles, the diabatic heating controls and feedbacks related to rne-sosynoptic circulations and their forecasting, the relationship of tropical patterns of LH to the global circulation and climate, and strategies for improving cloud parameterizations In environmental prediction models. However, the LH and water vapor profile or budget (called the apparent moisture sink, or Q2) is closely related. This paper presented the development of an algorithm for retrieving Q2 using 'TRMM precipitation radar. Since there is no direct measurement of LH and Q2, the validation of algorithm usually applies a method called consistency check. Consistency checking involving Cloud Resolving Model (CRM)-generated LH and 42 profiles and algorithm-reconstructed is a useful step in evaluating the performance of a given algorithm. In this process, the CRM simulation of a time-dependent precipitation process (multiple-day time series) is used to obtain the required input parameters for a given algorithm. The algorithm is then used to "econsti-LKth"e heating and moisture profiles that the CRM simulation originally produced, and finally both sets of conformal estimates (model and algorithm) are compared each other. The results indicate that discrepancies between the reconstructed and CM-simulated profiles for Q2, especially at low levels, are larger than those for latent heat. Larger discrepancies in Q2 at low levels are due to moistening for non-precipitating region that algorithm cannot reconstruct. Nevertheless, the algorithm-reconstructed total Q2 profiles are in good agreement with the CRM-simulated ones.

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.

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.

Shrink-wrapped isosurface from cross sectional images

PubMed Central

Choi, Y. K.; Hahn, J. K.

2010-01-01

Summary This paper addresses a new surface reconstruction scheme for approximating the isosurface from a set of tomographic cross sectional images. Differently from the novel Marching Cubes (MC) algorithm, our method does not extract the iso-density surface (isosurface) directly from the voxel data but calculates the iso-density point (isopoint) first. After building a coarse initial mesh approximating the ideal isosurface by the cell-boundary representation, it metamorphoses the mesh into the final isosurface by a relaxation scheme, called shrink-wrapping process. Compared with the MC algorithm, our method is robust and does not make any cracks on surface. Furthermore, since it is possible to utilize lots of additional isopoints during the surface reconstruction process by extending the adjacency definition, theoretically the resulting surface can be better in quality than the MC algorithm. According to experiments, it is proved to be very robust and efficient for isosurface reconstruction from cross sectional images. PMID:20703361

Approximate static condensation algorithm for solving multi-material diffusion problems on meshes non-aligned with material interfaces

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

Kikinzon, Evgeny; Kuznetsov, Yuri; Lipnikov, Konstatin

In this study, we describe a new algorithm for solving multi-material diffusion problem when material interfaces are not aligned with the mesh. In this case interface reconstruction methods are used to construct approximate representation of interfaces between materials. They produce so-called multi-material cells, in which materials are represented by material polygons that contain only one material. The reconstructed interface is not continuous between cells. Finally, we suggest the new method for solving multi-material diffusion problems on such meshes and compare its performance with known homogenization methods.

Approximate static condensation algorithm for solving multi-material diffusion problems on meshes non-aligned with material interfaces

DOE PAGES

Kikinzon, Evgeny; Kuznetsov, Yuri; Lipnikov, Konstatin; ...

2017-07-08

In this study, we describe a new algorithm for solving multi-material diffusion problem when material interfaces are not aligned with the mesh. In this case interface reconstruction methods are used to construct approximate representation of interfaces between materials. They produce so-called multi-material cells, in which materials are represented by material polygons that contain only one material. The reconstructed interface is not continuous between cells. Finally, we suggest the new method for solving multi-material diffusion problems on such meshes and compare its performance with known homogenization methods.

Fast parallel MR image reconstruction via B1-based, adaptive restart, iterative soft thresholding algorithms (BARISTA).

PubMed

Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A

2015-02-01

Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms.

Fast Parallel MR Image Reconstruction via B1-based, Adaptive Restart, Iterative Soft Thresholding Algorithms (BARISTA)

PubMed Central

Noll, Douglas C.; Fessler, Jeffrey A.

2014-01-01

Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms. PMID:25330484

3D imaging of nanomaterials by discrete tomography.

PubMed

Batenburg, K J; Bals, S; Sijbers, J; Kübel, C; Midgley, P A; Hernandez, J C; Kaiser, U; Encina, E R; Coronado, E A; Van Tendeloo, G

2009-05-01

The field of discrete tomography focuses on the reconstruction of samples that consist of only a few different materials. Ideally, a three-dimensional (3D) reconstruction of such a sample should contain only one grey level for each of the compositions in the sample. By exploiting this property in the reconstruction algorithm, either the quality of the reconstruction can be improved significantly, or the number of required projection images can be reduced. The discrete reconstruction typically contains fewer artifacts and does not have to be segmented, as it already contains one grey level for each composition. Recently, a new algorithm, called discrete algebraic reconstruction technique (DART), has been proposed that can be used effectively on experimental electron tomography datasets. In this paper, we propose discrete tomography as a general reconstruction method for electron tomography in materials science. We describe the basic principles of DART and show that it can be applied successfully to three different types of samples, consisting of embedded ErSi(2) nanocrystals, a carbon nanotube grown from a catalyst particle and a single gold nanoparticle, respectively.

Investigation of cone-beam CT image quality trade-off for image-guided radiation therapy

NASA Astrophysics Data System (ADS)

Bian, Junguo; Sharp, Gregory C.; Park, Yang-Kyun; Ouyang, Jinsong; Bortfeld, Thomas; El Fakhri, Georges

2016-05-01

It is well-known that projections acquired over an angular range slightly over 180° (so-called short scan) are sufficient for fan-beam reconstruction. However, due to practical imaging conditions (projection data and reconstruction image discretization, physical factors, and data noise), the short-scan reconstructions may have different appearances and properties from the full-scan (scans over 360°) reconstructions. Nevertheless, short-scan configurations have been used in applications such as cone-beam CT (CBCT) for head-neck-cancer image-guided radiation therapy (IGRT) that only requires a small field of view due to the potential reduced imaging time and dose. In this work, we studied the image quality trade-off for full, short, and full/short scan configurations with both conventional filtered-backprojection (FBP) reconstruction and iterative reconstruction algorithms based on total-variation (TV) minimization for head-neck-cancer IGRT. Anthropomorphic and Catphan phantoms were scanned at different exposure levels with a clinical scanner used in IGRT. Both visualization- and numerical-metric-based evaluation studies were performed. The results indicate that the optimal exposure level and number of views are in the middle range for both FBP and TV-based iterative algorithms and the optimization is object-dependent and task-dependent. The optimal view numbers decrease with the total exposure levels for both FBP and TV-based algorithms. The results also indicate there are slight differences between FBP and TV-based iterative algorithms for the image quality trade-off: FBP seems to be more in favor of larger number of views while the TV-based algorithm is more robust to different data conditions (number of views and exposure levels) than the FBP algorithm. The studies can provide a general guideline for image-quality optimization for CBCT used in IGRT and other applications.

Investigation of cone-beam CT image quality trade-off for image-guided radiation therapy.

PubMed

Bian, Junguo; Sharp, Gregory C; Park, Yang-Kyun; Ouyang, Jinsong; Bortfeld, Thomas; El Fakhri, Georges

2016-05-07

It is well-known that projections acquired over an angular range slightly over 180° (so-called short scan) are sufficient for fan-beam reconstruction. However, due to practical imaging conditions (projection data and reconstruction image discretization, physical factors, and data noise), the short-scan reconstructions may have different appearances and properties from the full-scan (scans over 360°) reconstructions. Nevertheless, short-scan configurations have been used in applications such as cone-beam CT (CBCT) for head-neck-cancer image-guided radiation therapy (IGRT) that only requires a small field of view due to the potential reduced imaging time and dose. In this work, we studied the image quality trade-off for full, short, and full/short scan configurations with both conventional filtered-backprojection (FBP) reconstruction and iterative reconstruction algorithms based on total-variation (TV) minimization for head-neck-cancer IGRT. Anthropomorphic and Catphan phantoms were scanned at different exposure levels with a clinical scanner used in IGRT. Both visualization- and numerical-metric-based evaluation studies were performed. The results indicate that the optimal exposure level and number of views are in the middle range for both FBP and TV-based iterative algorithms and the optimization is object-dependent and task-dependent. The optimal view numbers decrease with the total exposure levels for both FBP and TV-based algorithms. The results also indicate there are slight differences between FBP and TV-based iterative algorithms for the image quality trade-off: FBP seems to be more in favor of larger number of views while the TV-based algorithm is more robust to different data conditions (number of views and exposure levels) than the FBP algorithm. The studies can provide a general guideline for image-quality optimization for CBCT used in IGRT and other applications.

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.

ICON: 3D reconstruction with 'missing-information' restoration in biological electron tomography.

PubMed

Deng, Yuchen; Chen, Yu; Zhang, Yan; Wang, Shengliu; Zhang, Fa; Sun, Fei

2016-07-01

Electron tomography (ET) plays an important role in revealing biological structures, ranging from macromolecular to subcellular scale. Due to limited tilt angles, ET reconstruction always suffers from the 'missing wedge' artifacts, thus severely weakens the further biological interpretation. In this work, we developed an algorithm called Iterative Compressed-sensing Optimized Non-uniform fast Fourier transform reconstruction (ICON) based on the theory of compressed-sensing and the assumption of sparsity of biological specimens. ICON can significantly restore the missing information in comparison with other reconstruction algorithms. More importantly, we used the leave-one-out method to verify the validity of restored information for both simulated and experimental data. The significant improvement in sub-tomogram averaging by ICON indicates its great potential in the future application of high-resolution structural determination of macromolecules in situ. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Fast ancestral gene order reconstruction of genomes with unequal gene content.

PubMed

Feijão, Pedro; Araujo, Eloi

2016-11-11

During evolution, genomes are modified by large scale structural events, such as rearrangements, deletions or insertions of large blocks of DNA. Of particular interest, in order to better understand how this type of genomic evolution happens, is the reconstruction of ancestral genomes, given a phylogenetic tree with extant genomes at its leaves. One way of solving this problem is to assume a rearrangement model, such as Double Cut and Join (DCJ), and find a set of ancestral genomes that minimizes the number of events on the input tree. Since this problem is NP-hard for most rearrangement models, exact solutions are practical only for small instances, and heuristics have to be used for larger datasets. This type of approach can be called event-based. Another common approach is based on finding conserved structures between the input genomes, such as adjacencies between genes, possibly also assigning weights that indicate a measure of confidence or probability that this particular structure is present on each ancestral genome, and then finding a set of non conflicting adjacencies that optimize some given function, usually trying to maximize total weight and minimizing character changes in the tree. We call this type of methods homology-based. In previous work, we proposed an ancestral reconstruction method that combines homology- and event-based ideas, using the concept of intermediate genomes, that arise in DCJ rearrangement scenarios. This method showed better rate of correctly reconstructed adjacencies than other methods, while also being faster, since the use of intermediate genomes greatly reduces the search space. Here, we generalize the intermediate genome concept to genomes with unequal gene content, extending our method to account for gene insertions and deletions of any length. In many of the simulated datasets, our proposed method had better results than MLGO and MGRA, two state-of-the-art algorithms for ancestral reconstruction with unequal gene content, while running much faster, making it more scalable to larger datasets. Studing ancestral reconstruction problems under a new light, using the concept of intermediate genomes, allows the design of very fast algorithms by greatly reducing the solution search space, while also giving very good results. The algorithms introduced in this paper were implemented in an open-source software called RINGO (ancestral Reconstruction with INtermediate GenOmes), available at https://github.com/pedrofeijao/RINGO .

An ECG signals compression method and its validation using NNs.

PubMed

Fira, Catalina Monica; Goras, Liviu

2008-04-01

This paper presents a new algorithm for electrocardiogram (ECG) signal compression based on local extreme extraction, adaptive hysteretic filtering and Lempel-Ziv-Welch (LZW) coding. The algorithm has been verified using eight of the most frequent normal and pathological types of cardiac beats and an multi-layer perceptron (MLP) neural network trained with original cardiac patterns and tested with reconstructed ones. Aspects regarding the possibility of using the principal component analysis (PCA) to cardiac pattern classification have been investigated as well. A new compression measure called "quality score," which takes into account both the reconstruction errors and the compression ratio, is proposed.

Reduction of Metal Artifact in Single Photon-Counting Computed Tomography by Spectral-Driven Iterative Reconstruction Technique

PubMed Central

Nasirudin, Radin A.; Mei, Kai; Panchev, Petar; Fehringer, Andreas; Pfeiffer, Franz; Rummeny, Ernst J.; Fiebich, Martin; Noël, Peter B.

2015-01-01

Purpose The exciting prospect of Spectral CT (SCT) using photon-counting detectors (PCD) will lead to new techniques in computed tomography (CT) that take advantage of the additional spectral information provided. We introduce a method to reduce metal artifact in X-ray tomography by incorporating knowledge obtained from SCT into a statistical iterative reconstruction scheme. We call our method Spectral-driven Iterative Reconstruction (SPIR). Method The proposed algorithm consists of two main components: material decomposition and penalized maximum likelihood iterative reconstruction. In this study, the spectral data acquisitions with an energy-resolving PCD were simulated using a Monte-Carlo simulator based on EGSnrc C++ class library. A jaw phantom with a dental implant made of gold was used as an object in this study. A total of three dental implant shapes were simulated separately to test the influence of prior knowledge on the overall performance of the algorithm. The generated projection data was first decomposed into three basis functions: photoelectric absorption, Compton scattering and attenuation of gold. A pseudo-monochromatic sinogram was calculated and used as input in the reconstruction, while the spatial information of the gold implant was used as a prior. The results from the algorithm were assessed and benchmarked with state-of-the-art reconstruction methods. Results Decomposition results illustrate that gold implant of any shape can be distinguished from other components of the phantom. Additionally, the result from the penalized maximum likelihood iterative reconstruction shows that artifacts are significantly reduced in SPIR reconstructed slices in comparison to other known techniques, while at the same time details around the implant are preserved. Quantitatively, the SPIR algorithm best reflects the true attenuation value in comparison to other algorithms. Conclusion It is demonstrated that the combination of the additional information from Spectral CT and statistical reconstruction can significantly improve image quality, especially streaking artifacts caused by the presence of materials with high atomic numbers. PMID:25955019

P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations.

PubMed

Young-Rae Cho; Yanan Xin; Speegle, Greg

2015-01-01

Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.

Image reconstruction of muon tomographic data using a density-based clustering method

NASA Astrophysics Data System (ADS)

Perry, Kimberly B.

Muons are subatomic particles capable of reaching the Earth's surface before decaying. When these particles collide with an object that has a high atomic number (Z), their path of travel changes substantially. Tracking muon movement through shielded containers can indicate what types of materials lie inside. This thesis proposes using a density-based clustering algorithm called OPTICS to perform image reconstructions using muon tomographic data. The results show that this method is capable of detecting high-Z materials quickly, and can also produce detailed reconstructions with large amounts of data.

ODTbrain: a Python library for full-view, dense diffraction tomography.

PubMed

Müller, Paul; Schürmann, Mirjam; Guck, Jochen

2015-11-04

Analyzing the three-dimensional (3D) refractive index distribution of a single cell makes it possible to describe and characterize its inner structure in a marker-free manner. A dense, full-view tomographic data set is a set of images of a cell acquired for multiple rotational positions, densely distributed from 0 to 360 degrees. The reconstruction is commonly realized by projection tomography, which is based on the inversion of the Radon transform. The reconstruction quality of projection tomography is greatly improved when first order scattering, which becomes relevant when the imaging wavelength is comparable to the characteristic object size, is taken into account. This advanced reconstruction technique is called diffraction tomography. While many implementations of projection tomography are available today, there is no publicly available implementation of diffraction tomography so far. We present a Python library that implements the backpropagation algorithm for diffraction tomography in 3D. By establishing benchmarks based on finite-difference time-domain (FDTD) simulations, we showcase the superiority of the backpropagation algorithm over the backprojection algorithm. Furthermore, we discuss how measurment parameters influence the reconstructed refractive index distribution and we also give insights into the applicability of diffraction tomography to biological cells. The present software library contains a robust implementation of the backpropagation algorithm. The algorithm is ideally suited for the application to biological cells. Furthermore, the implementation is a drop-in replacement for the classical backprojection algorithm and is made available to the large user community of the Python programming language.

Task Performance with List-Mode Data

NASA Astrophysics Data System (ADS)

Caucci, Luca

This dissertation investigates the application of list-mode data to detection, estimation, and image reconstruction problems, with an emphasis on emission tomography in medical imaging. We begin by introducing a theoretical framework for list-mode data and we use it to define two observers that operate on list-mode data. These observers are applied to the problem of detecting a signal (known in shape and location) buried in a random lumpy background. We then consider maximum-likelihood methods for the estimation of numerical parameters from list-mode data, and we characterize the performance of these estimators via the so-called Fisher information matrix. Reconstruction from PET list-mode data is then considered. In a process we called "double maximum-likelihood" reconstruction, we consider a simple PET imaging system and we use maximum-likelihood methods to first estimate a parameter vector for each pair of gamma-ray photons that is detected by the hardware. The collection of these parameter vectors forms a list, which is then fed to another maximum-likelihood algorithm for volumetric reconstruction over a grid of voxels. Efficient parallel implementation of the algorithms discussed above is then presented. In this work, we take advantage of two low-cost, mass-produced computing platforms that have recently appeared on the market, and we provide some details on implementing our algorithms on these devices. We conclude this dissertation work by elaborating on a possible application of list-mode data to X-ray digital mammography. We argue that today's CMOS detectors and computing platforms have become fast enough to make X-ray digital mammography list-mode data acquisition and processing feasible.

AutoStitcher: An Automated Program for Efficient and Robust Reconstruction of Digitized Whole Histological Sections from Tissue Fragments

NASA Astrophysics Data System (ADS)

Penzias, Gregory; Janowczyk, Andrew; Singanamalli, Asha; Rusu, Mirabela; Shih, Natalie; Feldman, Michael; Stricker, Phillip D.; Delprado, Warick; Tiwari, Sarita; Böhm, Maret; Haynes, Anne-Maree; Ponsky, Lee; Viswanath, Satish; Madabhushi, Anant

2016-07-01

In applications involving large tissue specimens that have been sectioned into smaller tissue fragments, manual reconstruction of a “pseudo whole-mount” histological section (PWMHS) can facilitate (a) pathological disease annotation, and (b) image registration and correlation with radiological images. We have previously presented a program called HistoStitcher, which allows for more efficient manual reconstruction than general purpose image editing tools (such as Photoshop). However HistoStitcher is still manual and hence can be laborious and subjective, especially when doing large cohort studies. In this work we present AutoStitcher, a novel automated algorithm for reconstructing PWMHSs from digitized tissue fragments. AutoStitcher reconstructs (“stitches”) a PWMHS from a set of 4 fragments by optimizing a novel cost function that is domain-inspired to ensure (i) alignment of similar tissue regions, and (ii) contiguity of the prostate boundary. The algorithm achieves computational efficiency by performing reconstruction in a multi-resolution hierarchy. Automated PWMHS reconstruction results (via AutoStitcher) were quantitatively and qualitatively compared to manual reconstructions obtained via HistoStitcher for 113 prostate pathology sections. Distances between corresponding fiducials placed on each of the automated and manual reconstruction results were between 2.7%-3.2%, reflecting their excellent visual similarity.

Context-specific metabolic networks are consistent with experiments.

PubMed

Becker, Scott A; Palsson, Bernhard O

2008-05-16

Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are "genome-scale" and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.

Off-axis phase-only holograms of 3D objects using accelerated point-based Fresnel diffraction algorithm

NASA Astrophysics Data System (ADS)

Zeng, Zhenxiang; Zheng, Huadong; Yu, Yingjie; Asundi, Anand K.

2017-06-01

A method for calculating off-axis phase-only holograms of three-dimensional (3D) object using accelerated point-based Fresnel diffraction algorithm (PB-FDA) is proposed. The complex amplitude of the object points on the z-axis in hologram plane is calculated using Fresnel diffraction formula, called principal complex amplitudes (PCAs). The complex amplitudes of those off-axis object points of the same depth can be obtained by 2D shifting of PCAs. In order to improve the calculating speed of the PB-FDA, the convolution operation based on fast Fourier transform (FFT) is used to calculate the holograms rather than using the point-by-point spatial 2D shifting of the PCAs. The shortest recording distance of the PB-FDA is analyzed in order to remove the influence of multiple-order images in reconstructed images. The optimal recording distance of the PB-FDA is also analyzed to improve the quality of reconstructed images. Numerical reconstructions and optical reconstructions with a phase-only spatial light modulator (SLM) show that holographic 3D display is feasible with the proposed algorithm. The proposed PB-FDA can also avoid the influence of the zero-order image introduced by SLM in optical reconstructed images.

Fast High Resolution Volume Carving for 3D Plant Shoot Reconstruction

PubMed Central

Scharr, Hanno; Briese, Christoph; Embgenbroich, Patrick; Fischbach, Andreas; Fiorani, Fabio; Müller-Linow, Mark

2017-01-01

Volume carving is a well established method for visual hull reconstruction and has been successfully applied in plant phenotyping, especially for 3d reconstruction of small plants and seeds. When imaging larger plants at still relatively high spatial resolution (≤1 mm), well known implementations become slow or have prohibitively large memory needs. Here we present and evaluate a computationally efficient algorithm for volume carving, allowing e.g., 3D reconstruction of plant shoots. It combines a well-known multi-grid representation called “Octree” with an efficient image region integration scheme called “Integral image.” Speedup with respect to less efficient octree implementations is about 2 orders of magnitude, due to the introduced refinement strategy “Mark and refine.” Speedup is about a factor 1.6 compared to a highly optimized GPU implementation using equidistant voxel grids, even without using any parallelization. We demonstrate the application of this method for trait derivation of banana and maize plants. PMID:29033961

Accelerated gradient based diffuse optical tomographic image reconstruction.

PubMed

Biswas, Samir Kumar; Rajan, K; Vasu, R M

2011-01-01

Fast reconstruction of interior optical parameter distribution using a new approach called Broyden-based model iterative image reconstruction (BMOBIIR) and adjoint Broyden-based MOBIIR (ABMOBIIR) of a tissue and a tissue mimicking phantom from boundary measurement data in diffuse optical tomography (DOT). DOT is a nonlinear and ill-posed inverse problem. Newton-based MOBIIR algorithm, which is generally used, requires repeated evaluation of the Jacobian which consumes bulk of the computation time for reconstruction. In this study, we propose a Broyden approach-based accelerated scheme for Jacobian computation and it is combined with conjugate gradient scheme (CGS) for fast reconstruction. The method makes explicit use of secant and adjoint information that can be obtained from forward solution of the diffusion equation. This approach reduces the computational time many fold by approximating the system Jacobian successively through low-rank updates. Simulation studies have been carried out with single as well as multiple inhomogeneities. Algorithms are validated using an experimental study carried out on a pork tissue with fat acting as an inhomogeneity. The results obtained through the proposed BMOBIIR and ABMOBIIR approaches are compared with those of Newton-based MOBIIR algorithm. The mean squared error and execution time are used as metrics for comparing the results of reconstruction. We have shown through experimental and simulation studies that Broyden-based MOBIIR and adjoint Broyden-based methods are capable of reconstructing single as well as multiple inhomogeneities in tissue and a tissue-mimicking phantom. Broyden MOBIIR and adjoint Broyden MOBIIR methods are computationally simple and they result in much faster implementations because they avoid direct evaluation of Jacobian. The image reconstructions have been carried out with different initial values using Newton, Broyden, and adjoint Broyden approaches. These algorithms work well when the initial guess is close to the true solution. However, when initial guess is far away from true solution, Newton-based MOBIIR gives better reconstructed images. The proposed methods are found to be stable with noisy measurement data.

Bayesian reconstruction of gravitational wave bursts using chirplets

NASA Astrophysics Data System (ADS)

Millhouse, Margaret; Cornish, Neil; Littenberg, Tyson

2017-01-01

The BayesWave algorithm has been shown to accurately reconstruct unmodeled short duration gravitational wave bursts and to distinguish between astrophysical signals and transient noise events. BayesWave does this by using a variable number of sine-Gaussian (Morlet) wavelets to reconstruct data in multiple interferometers. While the Morlet wavelets can be summed together to produce any possible waveform, there could be other wavelet functions that improve the performance. Because we expect most astrophysical gravitational wave signals to evolve in frequency, modified Morlet wavelets with linear frequency evolution - called chirplets - may better reconstruct signals with fewer wavelets. We compare the performance of BayesWave using Morlet wavelets and chirplets on a variety of simulated signals.

Charged-particle emission tomography

PubMed Central

Ding, Yijun; Caucci, Luca; Barrett, Harrison H.

2018-01-01

Purpose Conventional charged-particle imaging techniques —such as autoradiography —provide only two-dimensional (2D) black ex vivo images of thin tissue slices. In order to get volumetric information, images of multiple thin slices are stacked. This process is time consuming and prone to distortions, as registration of 2D images is required. We propose a direct three-dimensional (3D) autoradiography technique, which we call charged-particle emission tomography (CPET). This 3D imaging technique enables imaging of thick tissue sections, thus increasing laboratory throughput and eliminating distortions due to registration. CPET also has the potential to enable in vivo charged-particle imaging with a window chamber or an endoscope. Methods Our approach to charged-particle emission tomography uses particle-processing detectors (PPDs) to estimate attributes of each detected particle. The attributes we estimate include location, direction of propagation, and/or the energy deposited in the detector. Estimated attributes are then fed into a reconstruction algorithm to reconstruct the 3D distribution of charged-particle-emitting radionuclides. Several setups to realize PPDs are designed. Reconstruction algorithms for CPET are developed. Results Reconstruction results from simulated data showed that a PPD enables CPET if the PPD measures more attributes than just the position from each detected particle. Experiments showed that a two-foil charged-particle detector is able to measure the position and direction of incident alpha particles. Conclusions We proposed a new volumetric imaging technique for charged-particle-emitting radionuclides, which we have called charged-particle emission tomography (CPET). We also proposed a new class of charged-particle detectors, which we have called particle-processing detectors (PPDs). When a PPD is used to measure the direction and/or energy attributes along with the position attributes, CPET is feasible. PMID:28370094

Charged-particle emission tomography.

PubMed

Ding, Yijun; Caucci, Luca; Barrett, Harrison H

2017-06-01

Conventional charged-particle imaging techniques - such as autoradiography - provide only two-dimensional (2D) black ex vivo images of thin tissue slices. In order to get volumetric information, images of multiple thin slices are stacked. This process is time consuming and prone to distortions, as registration of 2D images is required. We propose a direct three-dimensional (3D) autoradiography technique, which we call charged-particle emission tomography (CPET). This 3D imaging technique enables imaging of thick tissue sections, thus increasing laboratory throughput and eliminating distortions due to registration. CPET also has the potential to enable in vivo charged-particle imaging with a window chamber or an endoscope. Our approach to charged-particle emission tomography uses particle-processing detectors (PPDs) to estimate attributes of each detected particle. The attributes we estimate include location, direction of propagation, and/or the energy deposited in the detector. Estimated attributes are then fed into a reconstruction algorithm to reconstruct the 3D distribution of charged-particle-emitting radionuclides. Several setups to realize PPDs are designed. Reconstruction algorithms for CPET are developed. Reconstruction results from simulated data showed that a PPD enables CPET if the PPD measures more attributes than just the position from each detected particle. Experiments showed that a two-foil charged-particle detector is able to measure the position and direction of incident alpha particles. We proposed a new volumetric imaging technique for charged-particle-emitting radionuclides, which we have called charged-particle emission tomography (CPET). We also proposed a new class of charged-particle detectors, which we have called particle-processing detectors (PPDs). When a PPD is used to measure the direction and/or energy attributes along with the position attributes, CPET is feasible. © 2017 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

J-substitution algorithm in magnetic resonance electrical impedance tomography (MREIT): phantom experiments for static resistivity images.

PubMed

Khang, Hyun Soo; Lee, Byung Il; Oh, Suk Hoon; Woo, Eung Je; Lee, Soo Yeol; Cho, Min Hyoung; Kwon, Ohin; Yoon, Jeong Rock; Seo, Jin Keun

2002-06-01

Recently, a new static resistivity image reconstruction algorithm is proposed utilizing internal current density data obtained by magnetic resonance current density imaging technique. This new imaging method is called magnetic resonance electrical impedance tomography (MREIT). The derivation and performance of J-substitution algorithm in MREIT have been reported as a new accurate and high-resolution static impedance imaging technique via computer simulation methods. In this paper, we present experimental procedures, denoising techniques, and image reconstructions using a 0.3-tesla (T) experimental MREIT system and saline phantoms. MREIT using J-substitution algorithm effectively utilizes the internal current density information resolving the problem inherent in a conventional EIT, that is, the low sensitivity of boundary measurements to any changes of internal tissue resistivity values. Resistivity images of saline phantoms show an accuracy of 6.8%-47.2% and spatial resolution of 64 x 64. Both of them can be significantly improved by using an MRI system with a better signal-to-noise ratio.

Digital tomosynthesis (DTS) with a Circular X-ray tube: Its image reconstruction based on total-variation minimization and the image characteristics

NASA Astrophysics Data System (ADS)

Park, Y. O.; Hong, D. K.; Cho, H. S.; Je, U. K.; Oh, J. E.; Lee, M. S.; Kim, H. J.; Lee, S. H.; Jang, W. S.; Cho, H. M.; Choi, S. I.; Koo, Y. S.

2013-09-01

In this paper, we introduce an effective imaging system for digital tomosynthesis (DTS) with a circular X-ray tube, the so-called circular-DTS (CDTS) system, and its image reconstruction algorithm based on the total-variation (TV) minimization method for low-dose, high-accuracy X-ray imaging. Here, the X-ray tube is equipped with a series of cathodes distributed around a rotating anode, and the detector remains stationary throughout the image acquisition. We considered a TV-based reconstruction algorithm that exploited the sparsity of the image with substantially high image accuracy. We implemented the algorithm for the CDTS geometry and successfully reconstructed images of high accuracy. The image characteristics were investigated quantitatively by using some figures of merit, including the universal-quality index (UQI) and the depth resolution. For selected tomographic angles of 20, 40, and 60°, the corresponding UQI values in the tomographic view were estimated to be about 0.94, 0.97, and 0.98, and the depth resolutions were about 4.6, 3.1, and 1.2 voxels in full width at half maximum (FWHM), respectively. We expect the proposed method to be applicable to developing a next-generation dental or breast X-ray imaging system.

Reconstruction of Tissue-Specific Metabolic Networks Using CORDA

PubMed Central

Schultz, André; Qutub, Amina A.

2016-01-01

Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation. PMID:26942765

Efficient and robust method for simultaneous reconstruction of the temperature distribution and radiative properties in absorbing, emitting, and scattering media

NASA Astrophysics Data System (ADS)

Niu, Chun-Yang; Qi, Hong; Huang, Xing; Ruan, Li-Ming; Tan, He-Ping

2016-11-01

A rapid computational method called generalized sourced multi-flux method (GSMFM) was developed to simulate outgoing radiative intensities in arbitrary directions at the boundary surfaces of absorbing, emitting, and scattering media which were served as input for the inverse analysis. A hybrid least-square QR decomposition-stochastic particle swarm optimization (LSQR-SPSO) algorithm based on the forward GSMFM solution was developed to simultaneously reconstruct multi-dimensional temperature distribution and absorption and scattering coefficients of the cylindrical participating media. The retrieval results for axisymmetric temperature distribution and non-axisymmetric temperature distribution indicated that the temperature distribution and scattering and absorption coefficients could be retrieved accurately using the LSQR-SPSO algorithm even with noisy data. Moreover, the influences of extinction coefficient and scattering albedo on the accuracy of the estimation were investigated, and the results suggested that the reconstruction accuracy decreased with the increase of extinction coefficient and the scattering albedo. Finally, a non-contact measurement platform of flame temperature field based on the light field imaging was set up to validate the reconstruction model experimentally.

Unveiling the development of intracranial injury using dynamic brain EIT: an evaluation of current reconstruction algorithms.

PubMed

Li, Haoting; Chen, Rongqing; Xu, Canhua; Liu, Benyuan; Tang, Mengxing; Yang, Lin; Dong, Xiuzhen; Fu, Feng

2017-08-21

Dynamic brain electrical impedance tomography (EIT) is a promising technique for continuously monitoring the development of cerebral injury. While there are many reconstruction algorithms available for brain EIT, there is still a lack of study to compare their performance in the context of dynamic brain monitoring. To address this problem, we develop a framework for evaluating different current algorithms with their ability to correctly identify small intracranial conductivity changes. Firstly, a simulation 3D head phantom with realistic layered structure and impedance distribution is developed. Next several reconstructing algorithms, such as back projection (BP), damped least-square (DLS), Bayesian, split Bregman (SB) and GREIT are introduced. We investigate their temporal response, noise performance, location and shape error with respect to different noise levels on the simulation phantom. The results show that the SB algorithm demonstrates superior performance in reducing image error. To further improve the location accuracy, we optimize SB by incorporating the brain structure-based conductivity distribution priors, in which differences of the conductivities between different brain tissues and the inhomogeneous conductivity distribution of the skull are considered. We compare this novel algorithm (called SB-IBCD) with SB and DLS using anatomically correct head shaped phantoms with spatial varying skull conductivity. Main results and Significance: The results showed that SB-IBCD is the most effective in unveiling small intracranial conductivity changes, where it can reduce the image error by an average of 30.0% compared to DLS.

Performance measurement of PSF modeling reconstruction (True X) on Siemens Biograph TruePoint TrueV PET/CT.

PubMed

Lee, Young Sub; Kim, Jin Su; Kim, Kyeong Min; Kang, Joo Hyun; Lim, Sang Moo; Kim, Hee-Joung

2014-05-01

The Siemens Biograph TruePoint TrueV (B-TPTV) positron emission tomography (PET) scanner performs 3D PET reconstruction using a system matrix with point spread function (PSF) modeling (called the True X reconstruction). PET resolution was dramatically improved with the True X method. In this study, we assessed the spatial resolution and image quality on a B-TPTV PET scanner. In addition, we assessed the feasibility of animal imaging with a B-TPTV PET and compared it with a microPET R4 scanner. Spatial resolution was measured at center and at 8 cm offset from the center in transverse plane with warm background activity. True X, ordered subset expectation maximization (OSEM) without PSF modeling, and filtered back-projection (FBP) reconstruction methods were used. Percent contrast (% contrast) and percent background variability (% BV) were assessed according to NEMA NU2-2007. The recovery coefficient (RC), non-uniformity, spill-over ratio (SOR), and PET imaging of the Micro Deluxe Phantom were assessed to compare image quality of B-TPTV PET with that of the microPET R4. When True X reconstruction was used, spatial resolution was <3.65 mm with warm background activity. % contrast and % BV with True X reconstruction were higher than those with the OSEM reconstruction algorithm without PSF modeling. In addition, the RC with True X reconstruction was higher than that with the FBP method and the OSEM without PSF modeling method on the microPET R4. The non-uniformity with True X reconstruction was higher than that with FBP and OSEM without PSF modeling on microPET R4. SOR with True X reconstruction was better than that with FBP or OSEM without PSF modeling on the microPET R4. This study assessed the performance of the True X reconstruction. Spatial resolution with True X reconstruction was improved by 45 % and its % contrast was significantly improved compared to those with the conventional OSEM without PSF modeling reconstruction algorithm. The noise level was higher than that with the other reconstruction algorithm. Therefore, True X reconstruction should be used with caution when quantifying PET data.

A multilevel ant colony optimization algorithm for classical and isothermic DNA sequencing by hybridization with multiplicity information available.

PubMed

Kwarciak, Kamil; Radom, Marcin; Formanowicz, Piotr

2016-04-01

The classical sequencing by hybridization takes into account a binary information about sequence composition. A given element from an oligonucleotide library is or is not a part of the target sequence. However, the DNA chip technology has been developed and it enables to receive a partial information about multiplicity of each oligonucleotide the analyzed sequence consist of. Currently, it is not possible to assess the exact data of such type but even partial information should be very useful. Two realistic multiplicity information models are taken into consideration in this paper. The first one, called "one and many" assumes that it is possible to obtain information if a given oligonucleotide occurs in a reconstructed sequence once or more than once. According to the second model, called "one, two and many", one is able to receive from biochemical experiment information if a given oligonucleotide is present in an analyzed sequence once, twice or at least three times. An ant colony optimization algorithm has been implemented to verify the above models and to compare with existing algorithms for sequencing by hybridization which utilize the additional information. The proposed algorithm solves the problem with any kind of hybridization errors. Computational experiment results confirm that using even the partial information about multiplicity leads to increased quality of reconstructed sequences. Moreover, they also show that the more precise model enables to obtain better solutions and the ant colony optimization algorithm outperforms the existing ones. Test data sets and the proposed ant colony optimization algorithm are available on: http://bioserver.cs.put.poznan.pl/download/ACO4mSBH.zip. Copyright © 2016 Elsevier Ltd. All rights reserved.

Graphical programming interface: A development environment for MRI methods.

PubMed

Zwart, Nicholas R; Pipe, James G

2015-11-01

To introduce a multiplatform, Python language-based, development environment called graphical programming interface for prototyping MRI techniques. The interface allows developers to interact with their scientific algorithm prototypes visually in an event-driven environment making tasks such as parameterization, algorithm testing, data manipulation, and visualization an integrated part of the work-flow. Algorithm developers extend the built-in functionality through simple code interfaces designed to facilitate rapid implementation. This article shows several examples of algorithms developed in graphical programming interface including the non-Cartesian MR reconstruction algorithms for PROPELLER and spiral as well as spin simulation and trajectory visualization of a FLORET example. The graphical programming interface framework is shown to be a versatile prototyping environment for developing numeric algorithms used in the latest MR techniques. © 2014 Wiley Periodicals, Inc.

GPU-accelerated regularized iterative reconstruction for few-view cone beam CT

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

Matenine, Dmitri, E-mail: dmitri.matenine.1@ulaval.ca; Goussard, Yves, E-mail: yves.goussard@polymtl.ca; Després, Philippe, E-mail: philippe.despres@phy.ulaval.ca

2015-04-15

Purpose: The present work proposes an iterative reconstruction technique designed for x-ray transmission computed tomography (CT). The main objective is to provide a model-based solution to the cone-beam CT reconstruction problem, yielding accurate low-dose images via few-views acquisitions in clinically acceptable time frames. Methods: The proposed technique combines a modified ordered subsets convex (OSC) algorithm and the total variation minimization (TV) regularization technique and is called OSC-TV. The number of subsets of each OSC iteration follows a reduction pattern in order to ensure the best performance of the regularization method. Considering the high computational cost of the algorithm, it ismore » implemented on a graphics processing unit, using parallelization to accelerate computations. Results: The reconstructions were performed on computer-simulated as well as human pelvic cone-beam CT projection data and image quality was assessed. In terms of convergence and image quality, OSC-TV performs well in reconstruction of low-dose cone-beam CT data obtained via a few-view acquisition protocol. It compares favorably to the few-view TV-regularized projections onto convex sets (POCS-TV) algorithm. It also appears to be a viable alternative to full-dataset filtered backprojection. Execution times are of 1–2 min and are compatible with the typical clinical workflow for nonreal-time applications. Conclusions: Considering the image quality and execution times, this method may be useful for reconstruction of low-dose clinical acquisitions. It may be of particular benefit to patients who undergo multiple acquisitions by reducing the overall imaging radiation dose and associated risks.« less

Reconstruction-Based Digital Dental Occlusion of the Partially Edentulous Dentition.

PubMed

Zhang, Jian; Xia, James J; Li, Jianfu; Zhou, Xiaobo

2017-01-01

Partially edentulous dentition presents a challenging problem for the surgical planning of digital dental occlusion in the field of craniomaxillofacial surgery because of the incorrect maxillomandibular distance caused by missing teeth. We propose an innovative approach called Dental Reconstruction with Symmetrical Teeth (DRST) to achieve accurate dental occlusion for the partially edentulous cases. In this DRST approach, the rigid transformation between two symmetrical teeth existing on the left and right dental model is estimated through probabilistic point registration by matching the two shapes. With the estimated transformation, the partially edentulous space can be virtually filled with the teeth in its symmetrical position. Dental alignment is performed by digital dental occlusion reestablishment algorithm with the reconstructed complete dental model. Satisfactory reconstruction and occlusion results are demonstrated with the synthetic and real partially edentulous models.

Performance of algorithms that reconstruct missing transverse momentum in $$\\sqrt{s}=8$$ TeV proton–proton collisions in the ATLAS detector

DOE PAGES

Aad, G.; Abbott, B.; Abdallah, J.; ...

2017-04-13

The reconstruction and calibration algorithms used to calculate missing transverse momentum (E miss T) with the ATLAS detector exploit energy deposits in the calorimeter and tracks reconstructed in the inner detector as well as the muon spectrometer. Various strategies are used to suppress effects arising from additional proton–proton interactions, called pileup, concurrent with the hard-scatter processes. Tracking information is used to distinguish contributions from the pileup interactions using their vertex separation along the beam axis. The performance of the E miss T reconstruction algorithms, especially with respect to the amount of pileup, is evaluated using data collected in proton–proton collisionsmore » at a centre-of-mass energy of 8 TeV during 2012, and results are shown for a data sample corresponding to an integrated luminosity of 20.3fb –1. The simulation and modelling of E miss T in events containing a Z boson decaying to two charged leptons (electrons or muons) or a W boson decaying to a charged lepton and a neutrino are compared to data. The acceptance for different event topologies, with and without high transverse momentum neutrinos, is shown for a range of threshold criteria for E miss T, and estimates of the systematic uncertainties in the E miss T measurements are presented.« less

Performance of algorithms that reconstruct missing transverse momentum in [Formula: see text]= 8 TeV proton-proton collisions in the ATLAS detector.

PubMed

Aad, G; Abbott, B; Abdallah, J; Abdinov, O; Abeloos, B; Aben, R; Abolins, M; AbouZeid, O S; Abramowicz, H; Abreu, H; Abreu, R; Abulaiti, Y; Acharya, B S; Adamczyk, L; Adams, D L; Adelman, J; Adomeit, S; Adye, T; Affolder, A A; Agatonovic-Jovin, T; Agricola, J; Aguilar-Saavedra, J A; Ahlen, S P; Ahmadov, F; Aielli, G; Akerstedt, H; Åkesson, T P A; Akimov, A V; Alberghi, G L; Albert, J; Albrand, S; Alconada Verzini, M J; Aleksa, M; Aleksandrov, I N; Alexa, C; Alexander, G; Alexopoulos, T; Alhroob, M; Alimonti, G; Alio, L; Alison, J; Alkire, S P; Allbrooke, B M M; Allen, B W; Allport, P P; Aloisio, A; Alonso, A; Alonso, F; Alpigiani, C; Alvarez Gonzalez, B; Piqueras, D Álvarez; Alviggi, M G; Amadio, B T; Amako, K; Amaral Coutinho, Y; Amelung, C; Amidei, D; Dos Santos, S P Amor; Amorim, A; Amoroso, S; Amram, N; Amundsen, G; Anastopoulos, C; Ancu, L S; Andari, N; Andeen, T; Anders, C F; Anders, G; Anders, J K; Anderson, K J; Andreazza, A; Andrei, V; Angelidakis, S; Angelozzi, I; Anger, P; Angerami, A; Anghinolfi, F; Anisenkov, A V; Anjos, N; Annovi, A; Antonelli, M; Antonov, A; Antos, J; Anulli, F; Aoki, M; Aperio Bella, L; Arabidze, G; Arai, Y; Araque, J P; Arce, A T H; Arduh, F A; Arguin, J-F; Argyropoulos, S; Arik, M; Armbruster, A J; Arnaez, O; Arnold, H; Arratia, M; Arslan, O; Artamonov, A; Artoni, G; Artz, S; Asai, S; Asbah, N; Ashkenazi, A; Åsman, B; Asquith, L; Assamagan, K; Astalos, R; Atkinson, M; Atlay, N B; Augsten, K; Avolio, G; Axen, B; Ayoub, M K; Azuelos, G; Baak, M A; Baas, A E; Baca, M J; Bachacou, H; Bachas, K; Backes, M; Backhaus, M; Bagiacchi, P; Bagnaia, P; Bai, Y; Baines, J T; Baker, O K; Baldin, E M; Balek, P; Balestri, T; Balli, F; Balunas, W K; Banas, E; Banerjee, Sw; Bannoura, A A E; Barak, L; Barberio, E L; Barberis, D; Barbero, M; Barillari, T; Barklow, T; Barlow, N; Barnes, S L; Barnett, B M; Barnett, R M; Barnovska, Z; Baroncelli, A; Barone, G; Barr, A J; Barranco Navarro, L; Barreiro, F; da Costa, J Barreiro Guimarães; Bartoldus, R; Barton, A E; Bartos, P; 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Weidberg, A R; Weinert, B; Weingarten, J; Weiser, C; Weits, H; Wells, P S; Wenaus, T; Wengler, T; Wenig, S; Wermes, N; Werner, M; Werner, P; Wessels, M; Wetter, J; Whalen, K; Wharton, A M; White, A; White, M J; White, R; White, S; Whiteson, D; Wickens, F J; Wiedenmann, W; Wielers, M; Wienemann, P; Wiglesworth, C; Wiik-Fuchs, L A M; Wildauer, A; Wilkens, H G; Williams, H H; Williams, S; Willis, C; Willocq, S; Wilson, J A; Wingerter-Seez, I; Winklmeier, F; Winter, B T; Wittgen, M; Wittkowski, J; Wollstadt, S J; Wolter, M W; Wolters, H; Wosiek, B K; Wotschack, J; Woudstra, M J; Wozniak, K W; Wu, M; Wu, M; Wu, S L; Wu, X; Wu, Y; Wyatt, T R; Wynne, B M; Xella, S; Xu, D; Xu, L; Yabsley, B; Yacoob, S; Yakabe, R; Yamaguchi, D; Yamaguchi, Y; Yamamoto, A; Yamamoto, S; Yamanaka, T; Yamauchi, K; Yamazaki, Y; Yan, Z; Yang, H; Yang, H; Yang, Y; Yang, Z; Yao, W-M; Yap, Y C; Yasu, Y; Yatsenko, E; Wong, K H Yau; Ye, J; Ye, S; Yeletskikh, I; Yen, A L; Yildirim, E; Yorita, K; Yoshida, R; Yoshihara, K; Young, C; Young, C J S; Youssef, S; Yu, D R; Yu, J; Yu, J M; Yu, J; Yuan, L; Yuen, S P Y; Yusuff, I; Zabinski, B; Zaidan, R; Zaitsev, A M; Zakharchuk, N; Zalieckas, J; Zaman, A; Zambito, S; Zanello, L; Zanzi, D; Zeitnitz, C; Zeman, M; Zemla, A; Zeng, J C; Zeng, Q; Zengel, K; Zenin, O; Ženiš, T; Zerwas, D; Zhang, D; Zhang, F; Zhang, G; Zhang, H; Zhang, J; Zhang, L; Zhang, R; Zhang, R; Zhang, X; Zhang, Z; Zhao, X; Zhao, Y; Zhao, Z; Zhemchugov, A; Zhong, J; Zhou, B; Zhou, C; Zhou, L; Zhou, L; Zhou, M; Zhou, N; Zhu, C G; Zhu, H; Zhu, J; Zhu, Y; Zhuang, X; Zhukov, K; Zibell, A; Zieminska, D; Zimine, N I; Zimmermann, C; Zimmermann, S; Zinonos, Z; Zinser, M; Ziolkowski, M; Živković, L; Zobernig, G; Zoccoli, A; Zur Nedden, M; Zurzolo, G; Zwalinski, L

2017-01-01

The reconstruction and calibration algorithms used to calculate missing transverse momentum ([Formula: see text] ) with the ATLAS detector exploit energy deposits in the calorimeter and tracks reconstructed in the inner detector as well as the muon spectrometer. Various strategies are used to suppress effects arising from additional proton-proton interactions, called pileup, concurrent with the hard-scatter processes. Tracking information is used to distinguish contributions from the pileup interactions using their vertex separation along the beam axis. The performance of the [Formula: see text] reconstruction algorithms, especially with respect to the amount of pileup, is evaluated using data collected in proton-proton collisions at a centre-of-mass energy of 8 [Formula: see text] during 2012, and results are shown for a data sample corresponding to an integrated luminosity of [Formula: see text]. The simulation and modelling of [Formula: see text]  in events containing a Z boson decaying to two charged leptons (electrons or muons) or a W boson decaying to a charged lepton and a neutrino are compared to data. The acceptance for different event topologies, with and without high transverse momentum neutrinos, is shown for a range of threshold criteria for [Formula: see text] , and estimates of the systematic uncertainties in the [Formula: see text]  measurements are presented.

Performance Assessment of Different Pulse Reconstruction Algorithms for the ATHENA X-Ray Integral Field Unit

NASA Technical Reports Server (NTRS)

Peille, Phillip; Ceballos, Maria Teresa; Cobo, Beatriz; Wilms, Joern; Bandler, Simon; Smith, Stephen J.; Dauser, Thomas; Brand, Thorsten; Den Haretog, Roland; de Plaa, Jelle;

AIDA: an adaptive image deconvolution algorithm with application to multi-frame and three-dimensional data

PubMed Central

Hom, Erik F. Y.; Marchis, Franck; Lee, Timothy K.; Haase, Sebastian; Agard, David A.; Sedat, John W.

2011-01-01

We describe an adaptive image deconvolution algorithm (AIDA) for myopic deconvolution of multi-frame and three-dimensional data acquired through astronomical and microscopic imaging. AIDA is a reimplementation and extension of the MISTRAL method developed by Mugnier and co-workers and shown to yield object reconstructions with excellent edge preservation and photometric precision [J. Opt. Soc. Am. A 21, 1841 (2004)]. Written in Numerical Python with calls to a robust constrained conjugate gradient method, AIDA has significantly improved run times over the original MISTRAL implementation. Included in AIDA is a scheme to automatically balance maximum-likelihood estimation and object regularization, which significantly decreases the amount of time and effort needed to generate satisfactory reconstructions. We validated AIDA using synthetic data spanning a broad range of signal-to-noise ratios and image types and demonstrated the algorithm to be effective for experimental data from adaptive optics–equipped telescope systems and wide-field microscopy. PMID:17491626

Electrons and photons at High Level Trigger in CMS for Run II

NASA Astrophysics Data System (ADS)

Anuar, Afiq A.

2015-12-01

The CMS experiment has been designed with a 2-level trigger system. The first level is implemented using custom-designed electronics. The second level is the so-called High Level Trigger (HLT), a streamlined version of the CMS offline reconstruction software running on a computer farm. For Run II of the Large Hadron Collider, the increase in center-of-mass energy and luminosity will raise the event rate to a level challenging for the HLT algorithms. New approaches have been studied to keep the HLT output rate manageable while maintaining thresholds low enough to cover physics analyses. The strategy mainly relies on porting online the ingredients that have been successfully applied in the offline reconstruction, thus allowing to move HLT selection closer to offline cuts. Improvements in HLT electron and photon definitions will be presented, focusing in particular on: updated clustering algorithm and the energy calibration procedure, new Particle-Flow-based isolation approach and pileup mitigation techniques, and the electron-dedicated track fitting algorithm based on Gaussian Sum Filter.

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

Experimental investigations on airborne gravimetry based on compressed sensing.

PubMed

Yang, Yapeng; Wu, Meiping; Wang, Jinling; Zhang, Kaidong; Cao, Juliang; Cai, Shaokun

2014-03-18

Gravity surveys are an important research topic in geophysics and geodynamics. This paper investigates a method for high accuracy large scale gravity anomaly data reconstruction. Based on the airborne gravimetry technology, a flight test was carried out in China with the strap-down airborne gravimeter (SGA-WZ) developed by the Laboratory of Inertial Technology of the National University of Defense Technology. Taking into account the sparsity of airborne gravimetry by the discrete Fourier transform (DFT), this paper proposes a method for gravity anomaly data reconstruction using the theory of compressed sensing (CS). The gravity anomaly data reconstruction is an ill-posed inverse problem, which can be transformed into a sparse optimization problem. This paper uses the zero-norm as the objective function and presents a greedy algorithm called Orthogonal Matching Pursuit (OMP) to solve the corresponding minimization problem. The test results have revealed that the compressed sampling rate is approximately 14%, the standard deviation of the reconstruction error by OMP is 0.03 mGal and the signal-to-noise ratio (SNR) is 56.48 dB. In contrast, the standard deviation of the reconstruction error by the existing nearest-interpolation method (NIPM) is 0.15 mGal and the SNR is 42.29 dB. These results have shown that the OMP algorithm can reconstruct the gravity anomaly data with higher accuracy and fewer measurements.

Experimental Investigations on Airborne Gravimetry Based on Compressed Sensing

PubMed Central

Yang, Yapeng; Wu, Meiping; Wang, Jinling; Zhang, Kaidong; Cao, Juliang; Cai, Shaokun

2014-01-01

Gravity surveys are an important research topic in geophysics and geodynamics. This paper investigates a method for high accuracy large scale gravity anomaly data reconstruction. Based on the airborne gravimetry technology, a flight test was carried out in China with the strap-down airborne gravimeter (SGA-WZ) developed by the Laboratory of Inertial Technology of the National University of Defense Technology. Taking into account the sparsity of airborne gravimetry by the discrete Fourier transform (DFT), this paper proposes a method for gravity anomaly data reconstruction using the theory of compressed sensing (CS). The gravity anomaly data reconstruction is an ill-posed inverse problem, which can be transformed into a sparse optimization problem. This paper uses the zero-norm as the objective function and presents a greedy algorithm called Orthogonal Matching Pursuit (OMP) to solve the corresponding minimization problem. The test results have revealed that the compressed sampling rate is approximately 14%, the standard deviation of the reconstruction error by OMP is 0.03 mGal and the signal-to-noise ratio (SNR) is 56.48 dB. In contrast, the standard deviation of the reconstruction error by the existing nearest-interpolation method (NIPM) is 0.15 mGal and the SNR is 42.29 dB. These results have shown that the OMP algorithm can reconstruct the gravity anomaly data with higher accuracy and fewer measurements. PMID:24647125

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.

An Image Processing Algorithm Based On FMAT

NASA Technical Reports Server (NTRS)

Wang, Lui; Pal, Sankar K.

1995-01-01

Information deleted in ways minimizing adverse effects on reconstructed images. New grey-scale generalization of medial axis transformation (MAT), called FMAT (short for Fuzzy MAT) proposed. Formulated by making natural extension to fuzzy-set theory of all definitions and conditions (e.g., characteristic function of disk, subset condition of disk, and redundancy checking) used in defining MAT of crisp set. Does not need image to have any kind of priori segmentation, and allows medial axis (and skeleton) to be fuzzy subset of input image. Resulting FMAT (consisting of maximal fuzzy disks) capable of reconstructing exactly original image.

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.

Minimizing camera-eye optical aberrations during the 3D reconstruction of retinal structures

NASA Astrophysics Data System (ADS)

Aldana-Iuit, Javier; Martinez-Perez, M. Elena; Espinosa-Romero, Arturo; Diaz-Uribe, Rufino

2010-05-01

3D reconstruction of blood vessels is a powerful visualization tool for physicians, since it allows them to refer to qualitative representation of their subject of study. In this paper we propose a 3D reconstruction method of retinal vessels from fundus images. The reconstruction method propose herein uses images of the same retinal structure in epipolar geometry. Images are preprocessed by RISA system for segmenting blood vessels and obtaining feature points for correspondences. The correspondence points process is solved using correlation. The LMedS analysis and Graph Transformation Matching algorithm are used for outliers suppression. Camera projection matrices are computed with the normalized eight point algorithm. Finally, we retrieve 3D position of the retinal tree points by linear triangulation. In order to increase the power of visualization, 3D tree skeletons are represented by surfaces via generalized cylinders whose radius correspond to morphological measurements obtained by RISA. In this paper the complete calibration process including the fundus camera and the optical properties of the eye, the so called camera-eye system is proposed. On one hand, the internal parameters of the fundus camera are obtained by classical algorithms using a reference pattern. On the other hand, we minimize the undesirable efects of the aberrations induced by the eyeball optical system assuming that contact enlarging lens corrects astigmatism, spherical and coma aberrations are reduced changing the aperture size and eye refractive errors are suppressed adjusting camera focus during image acquisition. Evaluation of two self-calibration proposals and results of 3D blood vessel surface reconstruction are presented.

Reconstruction-based Digital Dental Occlusion of the Partially Edentulous Dentition

PubMed Central

Zhang, Jian; Xia, James J.; Li, Jianfu; Zhou, Xiaobo

2016-01-01

Partially edentulous dentition presents a challenging problem for the surgical planning of digital dental occlusion in the field of craniomaxillofacial surgery because of the incorrect maxillomandibular distance caused by missing teeth. We propose an innovative approach called Dental Reconstruction with Symmetrical Teeth (DRST) to achieve accurate dental occlusion for the partially edentulous cases. In this DRST approach, the rigid transformation between two symmetrical teeth existing on the left and right dental model is estimated through probabilistic point registration by matching the two shapes. With the estimated transformation, the partially edentulous space can be virtually filled with the teeth in its symmetrical position. Dental alignment is performed by digital dental occlusion reestablishment algorithm with the reconstructed complete dental model. Satisfactory reconstruction and occlusion results are demonstrated with the synthetic and real partially edentulous models. PMID:26584502

Automatic alignment for three-dimensional tomographic reconstruction

NASA Astrophysics Data System (ADS)

van Leeuwen, Tristan; Maretzke, Simon; Joost Batenburg, K.

2018-02-01

In tomographic reconstruction, the goal is to reconstruct an unknown object from a collection of line integrals. Given a complete sampling of such line integrals for various angles and directions, explicit inverse formulas exist to reconstruct the object. Given noisy and incomplete measurements, the inverse problem is typically solved through a regularized least-squares approach. A challenge for both approaches is that in practice the exact directions and offsets of the x-rays are only known approximately due to, e.g. calibration errors. Such errors lead to artifacts in the reconstructed image. In the case of sufficient sampling and geometrically simple misalignment, the measurements can be corrected by exploiting so-called consistency conditions. In other cases, such conditions may not apply and we have to solve an additional inverse problem to retrieve the angles and shifts. In this paper we propose a general algorithmic framework for retrieving these parameters in conjunction with an algebraic reconstruction technique. The proposed approach is illustrated by numerical examples for both simulated data and an electron tomography dataset.

Adaptive panoramic tomography with a circular rotational movement for the formation of multifocal image layers

NASA Astrophysics Data System (ADS)

Kim, D. S.; Cho, H. S.; Park, Y. O.; Je, U. K.; Hong, D. K.; Choi, S. I.; Koo, Y. S.

2012-02-01

Panoramic radiography with which only structures within a certain image layer are in focus and others out of focus on the panoramic image has become a popular imaging technique especially in dentistry. However, the major drawback to the technique is a mismatch between the structures to be focused and the predefined image layer mainly due to the various shapes and sizes of dental arches and/or to malpositioning of the patient. These result in image quality typically inferior to that obtained using intraoral radiographic techniques. In this paper, to overcome these difficulties, we suggest a new panoramic reconstruction algorithm, the so-called adaptive panoramic tomography ( APT), capable of reconstructing multifocal image layers with no additional exposure. In order to verify the effectiveness of the proposed algorithm, we performed systematic simulation studies with a circular rotational movement and investigated the image performance.

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.

Spectral Prior Image Constrained Compressed Sensing (Spectral PICCS) for Photon-Counting Computed Tomography

PubMed Central

Yu, Zhicong; Leng, Shuai; Li, Zhoubo; McCollough, Cynthia H.

2016-01-01

Photon-counting computed tomography (PCCT) is an emerging imaging technique that enables multi-energy imaging with only a single scan acquisition. To enable multi-energy imaging, the detected photons corresponding to the full x-ray spectrum are divided into several subgroups of bin data that correspond to narrower energy windows. Consequently, noise in each energy bin increases compared to the full-spectrum data. This work proposes an iterative reconstruction algorithm for noise suppression in the narrower energy bins used in PCCT imaging. The algorithm is based on the framework of prior image constrained compressed sensing (PICCS) and is called spectral PICCS; it uses the full-spectrum image reconstructed using conventional filtered back-projection as the prior image. The spectral PICCS algorithm is implemented using a constrained optimization scheme with adaptive iterative step sizes such that only two tuning parameters are required in most cases. The algorithm was first evaluated using computer simulations, and then validated by both physical phantoms and in-vivo swine studies using a research PCCT system. Results from both computer-simulation and experimental studies showed substantial image noise reduction in narrow energy bins (43~73%) without sacrificing CT number accuracy or spatial resolution. PMID:27551878

Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography

NASA Astrophysics Data System (ADS)

Yu, Zhicong; Leng, Shuai; Li, Zhoubo; McCollough, Cynthia H.

2016-09-01

Photon-counting computed tomography (PCCT) is an emerging imaging technique that enables multi-energy imaging with only a single scan acquisition. To enable multi-energy imaging, the detected photons corresponding to the full x-ray spectrum are divided into several subgroups of bin data that correspond to narrower energy windows. Consequently, noise in each energy bin increases compared to the full-spectrum data. This work proposes an iterative reconstruction algorithm for noise suppression in the narrower energy bins used in PCCT imaging. The algorithm is based on the framework of prior image constrained compressed sensing (PICCS) and is called spectral PICCS; it uses the full-spectrum image reconstructed using conventional filtered back-projection as the prior image. The spectral PICCS algorithm is implemented using a constrained optimization scheme with adaptive iterative step sizes such that only two tuning parameters are required in most cases. The algorithm was first evaluated using computer simulations, and then validated by both physical phantoms and in vivo swine studies using a research PCCT system. Results from both computer-simulation and experimental studies showed substantial image noise reduction in narrow energy bins (43-73%) without sacrificing CT number accuracy or spatial resolution.

Chroma intra prediction based on inter-channel correlation for HEVC.

PubMed

Zhang, Xingyu; Gisquet, Christophe; François, Edouard; Zou, Feng; Au, Oscar C

2014-01-01

In this paper, we investigate a new inter-channel coding mode called LM mode proposed for the next generation video coding standard called high efficiency video coding. This mode exploits inter-channel correlation using reconstructed luma to predict chroma linearly with parameters derived from neighboring reconstructed luma and chroma pixels at both encoder and decoder to avoid overhead signaling. In this paper, we analyze the LM mode and prove that the LM parameters for predicting original chroma and reconstructed chroma are statistically the same. We also analyze the error sensitivity of the LM parameters. We identify some LM mode problematic situations and propose three novel LM-like modes called LMA, LML, and LMO to address the situations. To limit the increase in complexity due to the LM-like modes, we propose some fast algorithms with the help of some new cost functions. We further identify some potentially-problematic conditions in the parameter estimation (including regression dilution problem) and introduce a novel model correction technique to detect and correct those conditions. Simulation results suggest that considerable BD-rate reduction can be achieved by the proposed LM-like modes and model correction technique. In addition, the performance gain of the two techniques appears to be essentially additive when combined.

A fast rebinning algorithm for 3D positron emission tomography using John's equation

NASA Astrophysics Data System (ADS)

Defrise, Michel; Liu, Xuan

1999-08-01

Volume imaging in positron emission tomography (PET) requires the inversion of the three-dimensional (3D) x-ray transform. The usual solution to this problem is based on 3D filtered-backprojection (FBP), but is slow. Alternative methods have been proposed which factor the 3D data into independent 2D data sets corresponding to the 2D Radon transforms of a stack of parallel slices. Each slice is then reconstructed using 2D FBP. These so-called rebinning methods are numerically efficient but are approximate. In this paper a new exact rebinning method is derived by exploiting the fact that the 3D x-ray transform of a function is the solution to the second-order partial differential equation first studied by John. The method is proposed for two sampling schemes, one corresponding to a pair of infinite plane detectors and another one corresponding to a cylindrical multi-ring PET scanner. The new FORE-J algorithm has been implemented for this latter geometry and was compared with the approximate Fourier rebinning algorithm FORE and with another exact rebinning algorithm, FOREX. Results with simulated data demonstrate a significant improvement in accuracy compared to FORE, while the reconstruction time is doubled. Compared to FOREX, the FORE-J algorithm is slightly less accurate but more than three times faster.

Extending the FairRoot framework to allow for simulation and reconstruction of free streaming data

NASA Astrophysics Data System (ADS)

Al-Turany, M.; Klein, D.; Manafov, A.; Rybalchenko, A.; Uhlig, F.

2014-06-01

The FairRoot framework is the standard framework for simulation, reconstruction and data analysis for the FAIR experiments. The framework is designed to optimise the accessibility for beginners and developers, to be flexible and to cope with future developments. FairRoot enhances the synergy between the different physics experiments. As a first step toward simulation of free streaming data, the time based simulation was introduced to the framework. The next step is the event source simulation. This is achieved via a client server system. After digitization the so called "samplers" can be started, where sampler can read the data of the corresponding detector from the simulation files and make it available for the reconstruction clients. The system makes it possible to develop and validate the online reconstruction algorithms. In this work, the design and implementation of the new architecture and the communication layer will be described.

Monte Carlo-based Reconstruction in Water Cherenkov Detectors using Chroma

NASA Astrophysics Data System (ADS)

Seibert, Stanley; Latorre, Anthony

2012-03-01

We demonstrate the feasibility of event reconstruction---including position, direction, energy and particle identification---in water Cherenkov detectors with a purely Monte Carlo-based method. Using a fast optical Monte Carlo package we have written, called Chroma, in combination with several variance reduction techniques, we can estimate the value of a likelihood function for an arbitrary event hypothesis. The likelihood can then be maximized over the parameter space of interest using a form of gradient descent designed for stochastic functions. Although slower than more traditional reconstruction algorithms, this completely Monte Carlo-based technique is universal and can be applied to a detector of any size or shape, which is a major advantage during the design phase of an experiment. As a specific example, we focus on reconstruction results from a simulation of the 200 kiloton water Cherenkov far detector option for LBNE.

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

Park, C; Zhang, H; Chen, Y

Purpose: Recently, compressed sensing (CS) based iterative reconstruction (IR) method is receiving attentions to reconstruct high quality cone beam computed tomography (CBCT) images using sparsely sampled or noisy projections. The aim of this study is to develop a novel baseline algorithm called Mask Guided Image Reconstruction (MGIR), which can provide superior image quality for both low-dose 3DCBCT and 4DCBCT under single mathematical framework. Methods: In MGIR, the unknown CBCT volume was mathematically modeled as a combination of two regions where anatomical structures are 1) within the priori-defined mask and 2) outside the mask. Then we update each part of imagesmore » alternatively thorough solving minimization problems based on CS type IR. For low-dose 3DCBCT, the former region is defined as the anatomically complex region where it is focused to preserve edge information while latter region is defined as contrast uniform, and hence aggressively updated to remove noise/artifact. In 4DCBCT, the regions are separated as the common static part and moving part. Then, static volume and moving volumes were updated with global and phase sorted projection respectively, to optimize the image quality of both moving and static part simultaneously. Results: Examination of MGIR algorithm showed that high quality of both low-dose 3DCBCT and 4DCBCT images can be reconstructed without compromising the image resolution and imaging dose or scanning time respectively. For low-dose 3DCBCT, a clinical viable and high resolution head-and-neck image can be obtained while cutting the dose by 83%. In 4DCBCT, excellent quality 4DCBCT images could be reconstructed while requiring no more projection data and imaging dose than a typical clinical 3DCBCT scan. Conclusion: The results shown that the image quality of MGIR was superior compared to other published CS based IR algorithms for both 4DCBCT and low-dose 3DCBCT. This makes our MGIR algorithm potentially useful in various on-line clinical applications. Provisional Patent: UF#15476; WGS Ref. No. U1198.70067US00.« less

SU-D-206-02: Evaluation of Partial Storage of the System Matrix for Cone Beam Computed Tomography Using a GPU Platform

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

Matenine, D; Cote, G; Mascolo-Fortin, J

2016-06-15

Purpose: Iterative reconstruction algorithms in computed tomography (CT) require a fast method for computing the intersections between the photons’ trajectories and the object, also called ray-tracing or system matrix computation. This work evaluates different ways to store the system matrix, aiming to reconstruct dense image grids in reasonable time. Methods: We propose an optimized implementation of the Siddon’s algorithm using graphics processing units (GPUs) with a novel data storage scheme. The algorithm computes a part of the system matrix on demand, typically, for one projection angle. The proposed method was enhanced with accelerating options: storage of larger subsets of themore » system matrix, systematic reuse of data via geometric symmetries, an arithmetic-rich parallel code and code configuration via machine learning. It was tested on geometries mimicking a cone beam CT acquisition of a human head. To realistically assess the execution time, the ray-tracing routines were integrated into a regularized Poisson-based reconstruction algorithm. The proposed scheme was also compared to a different approach, where the system matrix is fully pre-computed and loaded at reconstruction time. Results: Fast ray-tracing of realistic acquisition geometries, which often lack spatial symmetry properties, was enabled via the proposed method. Ray-tracing interleaved with projection and backprojection operations required significant additional time. In most cases, ray-tracing was shown to use about 66 % of the total reconstruction time. In absolute terms, tracing times varied from 3.6 s to 7.5 min, depending on the problem size. The presence of geometrical symmetries allowed for non-negligible ray-tracing and reconstruction time reduction. Arithmetic-rich parallel code and machine learning permitted a modest reconstruction time reduction, in the order of 1 %. Conclusion: Partial system matrix storage permitted the reconstruction of higher 3D image grid sizes and larger projection datasets at the cost of additional time, when compared to the fully pre-computed approach. This work was supported in part by the Fonds de recherche du Quebec - Nature et technologies (FRQ-NT). The authors acknowledge partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council of Canada (Grant No. 432290).« less

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.

Theory and Application of Auger and Photoelectron Diffraction and Holography

NASA Astrophysics Data System (ADS)

Chen, Xiang

This dissertation addresses the theories and applications of three important surface analysis techniques: Auger electron diffraction (AED), x-ray photoelectron diffraction (XPD), and Auger and photoelectron holography. A full multiple-scattering scheme for the calculations of XPD, AED, and Kikuchi electron diffraction pattern from a surface cluster is described. It is used to simulate 64 eV M_{2,3}VV and 913 eV L_3VV AED patterns from Cu(001) surfaces, in order to test assertions in the literature that they are explicable by a classical "blocking" and channeling model. We find that this contention is not valid, and that only a quantum mechanical multiple-scattering calculation is able to simulate these patterns well. The same multiple scattering simulation scheme is also used to investigate the anomalous phenomena of peak shifts off the forward-scattering directions in photo -electron diffraction patterns of Mg KLL (1180 eV) and O 1s (955 eV) from MgO(001) surfaces. These shifts are explained by calculations assuming a short electron mean free path. Similar simulations of XPD from a CoSi_2(111) surface for Co-3p and Si-2p normal emission agree well with experimental diffraction patterns. A filtering process aimed at eliminating the self -interference effect in photoelectron holography is developed. A better reconstructed image from Si-2p XPD from a Si(001) (2 times 1) surface is seen at atomic resolution. A reconstruction algorithm which corrects for the anisotropic emitter waves as well as the anisotropic atomic scattering factors is used for holographic reconstruction from a Co-3p XPD pattern from a CoSi_2 surface. This new algorithm considerably improves the reconstructed image. Finally, a new reconstruction algorithm called "atomic position recovery by iterative optimization of reconstructed intensities" (APRIORI), which takes account of the self-interference terms omitted by the other holographic algorithms, is developed. Tests on a Ni-C-O chain and Si(111)(sqrt{3} times sqrt{3})B surface suggest that this new method may overcome the twin image problem in the traditional holographic methods, reduce the artifacts in real space, and even separately identify the chemical species of the scatterers.

Detection and 3D reconstruction of traffic signs from multiple view color images

NASA Astrophysics Data System (ADS)

Soheilian, Bahman; Paparoditis, Nicolas; Vallet, Bruno

2013-03-01

3D reconstruction of traffic signs is of great interest in many applications such as image-based localization and navigation. In order to reflect the reality, the reconstruction process should meet both accuracy and precision. In order to reach such a valid reconstruction from calibrated multi-view images, accurate and precise extraction of signs in every individual view is a must. This paper presents first an automatic pipeline for identifying and extracting the silhouette of signs in every individual image. Then, a multi-view constrained 3D reconstruction algorithm provides an optimum 3D silhouette for the detected signs. The first step called detection, tackles with a color-based segmentation to generate ROIs (Region of Interests) in image. The shape of every ROI is estimated by fitting an ellipse, a quadrilateral or a triangle to edge points. A ROI is rejected if none of the three shapes can be fitted sufficiently precisely. Thanks to the estimated shape the remained candidates ROIs are rectified to remove the perspective distortion and then matched with a set of reference signs using textural information. Poor matches are rejected and the types of remained ones are identified. The output of the detection algorithm is a set of identified road signs whose silhouette in image plane is represented by and ellipse, a quadrilateral or a triangle. The 3D reconstruction process is based on a hypothesis generation and verification. Hypotheses are generated by a stereo matching approach taking into account epipolar geometry and also the similarity of the categories. The hypotheses that are plausibly correspond to the same 3D road sign are identified and grouped during this process. Finally, all the hypotheses of the same group are merged to generate a unique 3D road sign by a multi-view algorithm integrating a priori knowledges about 3D shape of road signs as constraints. The algorithm is assessed on real and synthetic images and reached and average accuracy of 3.5cm for position and 4.5° for orientation.

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.

Reconstructing the temporal ordering of biological samples using microarray data.

PubMed

Magwene, Paul M; Lizardi, Paul; Kim, Junhyong

2003-05-01

Accurate time series for biological processes are difficult to estimate due to problems of synchronization, temporal sampling and rate heterogeneity. Methods are needed that can utilize multi-dimensional data, such as those resulting from DNA microarray experiments, in order to reconstruct time series from unordered or poorly ordered sets of observations. We present a set of algorithms for estimating temporal orderings from unordered sets of sample elements. The techniques we describe are based on modifications of a minimum-spanning tree calculated from a weighted, undirected graph. We demonstrate the efficacy of our approach by applying these techniques to an artificial data set as well as several gene expression data sets derived from DNA microarray experiments. In addition to estimating orderings, the techniques we describe also provide useful heuristics for assessing relevant properties of sample datasets such as noise and sampling intensity, and we show how a data structure called a PQ-tree can be used to represent uncertainty in a reconstructed ordering. Academic implementations of the ordering algorithms are available as source code (in the programming language Python) on our web site, along with documentation on their use. The artificial 'jelly roll' data set upon which the algorithm was tested is also available from this web site. The publicly available gene expression data may be found at http://genome-www.stanford.edu/cellcycle/ and http://caulobacter.stanford.edu/CellCycle/.

Acoustic Inversion in Optoacoustic Tomography: A Review

PubMed Central

Rosenthal, Amir; Ntziachristos, Vasilis; Razansky, Daniel

2013-01-01

Optoacoustic tomography enables volumetric imaging with optical contrast in biological tissue at depths beyond the optical mean free path by the use of optical excitation and acoustic detection. The hybrid nature of optoacoustic tomography gives rise to two distinct inverse problems: The optical inverse problem, related to the propagation of the excitation light in tissue, and the acoustic inverse problem, which deals with the propagation and detection of the generated acoustic waves. Since the two inverse problems have different physical underpinnings and are governed by different types of equations, they are often treated independently as unrelated problems. From an imaging standpoint, the acoustic inverse problem relates to forming an image from the measured acoustic data, whereas the optical inverse problem relates to quantifying the formed image. This review focuses on the acoustic aspects of optoacoustic tomography, specifically acoustic reconstruction algorithms and imaging-system practicalities. As these two aspects are intimately linked, and no silver bullet exists in the path towards high-performance imaging, we adopt a holistic approach in our review and discuss the many links between the two aspects. Four classes of reconstruction algorithms are reviewed: time-domain (so called back-projection) formulae, frequency-domain formulae, time-reversal algorithms, and model-based algorithms. These algorithms are discussed in the context of the various acoustic detectors and detection surfaces which are commonly used in experimental studies. We further discuss the effects of non-ideal imaging scenarios on the quality of reconstruction and review methods that can mitigate these effects. Namely, we consider the cases of finite detector aperture, limited-view tomography, spatial under-sampling of the acoustic signals, and acoustic heterogeneities and losses. PMID:24772060

Three-dimensional focus of attention for iterative cone-beam micro-CT reconstruction

NASA Astrophysics Data System (ADS)

Benson, T. M.; Gregor, J.

2006-09-01

Three-dimensional iterative reconstruction of high-resolution, circular orbit cone-beam x-ray CT data is often considered impractical due to the demand for vast amounts of computer cycles and associated memory. In this paper, we show that the computational burden can be reduced by limiting the reconstruction to a small, well-defined portion of the image volume. We first discuss using the support region defined by the set of voxels covered by all of the projection views. We then present a data-driven preprocessing technique called focus of attention that heuristically separates both image and projection data into object and background before reconstruction, thereby further reducing the reconstruction region of interest. We present experimental results for both methods based on mouse data and a parallelized implementation of the SIRT algorithm. The computational savings associated with the support region are substantial. However, the results for focus of attention are even more impressive in that only about one quarter of the computer cycles and memory are needed compared with reconstruction of the entire image volume. The image quality is not compromised by either method.

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.

Computational photography with plenoptic camera and light field capture: tutorial.

PubMed

Lam, Edmund Y

2015-11-01

Photography is a cornerstone of imaging. Ever since cameras became consumer products more than a century ago, we have witnessed great technological progress in optics and recording mediums, with digital sensors replacing photographic films in most instances. The latest revolution is computational photography, which seeks to make image reconstruction computation an integral part of the image formation process; in this way, there can be new capabilities or better performance in the overall imaging system. A leading effort in this area is called the plenoptic camera, which aims at capturing the light field of an object; proper reconstruction algorithms can then adjust the focus after the image capture. In this tutorial paper, we first illustrate the concept of plenoptic function and light field from the perspective of geometric optics. This is followed by a discussion on early attempts and recent advances in the construction of the plenoptic camera. We will then describe the imaging model and computational algorithms that can reconstruct images at different focus points, using mathematical tools from ray optics and Fourier optics. Last, but not least, we will consider the trade-off in spatial resolution and highlight some research work to increase the spatial resolution of the resulting images.

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.

Reconstruction of Ancestral Genomes in Presence of Gene Gain and Loss.

PubMed

Avdeyev, Pavel; Jiang, Shuai; Aganezov, Sergey; Hu, Fei; Alekseyev, Max A

2016-03-01

Since most dramatic genomic changes are caused by genome rearrangements as well as gene duplications and gain/loss events, it becomes crucial to understand their mechanisms and reconstruct ancestral genomes of the given genomes. This problem was shown to be NP-complete even in the "simplest" case of three genomes, thus calling for heuristic rather than exact algorithmic solutions. At the same time, a larger number of input genomes may actually simplify the problem in practice as it was earlier illustrated with MGRA, a state-of-the-art software tool for reconstruction of ancestral genomes of multiple genomes. One of the key obstacles for MGRA and other similar tools is presence of breakpoint reuses when the same breakpoint region is broken by several different genome rearrangements in the course of evolution. Furthermore, such tools are often limited to genomes composed of the same genes with each gene present in a single copy in every genome. This limitation makes these tools inapplicable for many biological datasets and degrades the resolution of ancestral reconstructions in diverse datasets. We address these deficiencies by extending the MGRA algorithm to genomes with unequal gene contents. The developed next-generation tool MGRA2 can handle gene gain/loss events and shares the ability of MGRA to reconstruct ancestral genomes uniquely in the case of limited breakpoint reuse. Furthermore, MGRA2 employs a number of novel heuristics to cope with higher breakpoint reuse and process datasets inaccessible for MGRA. In practical experiments, MGRA2 shows superior performance for simulated and real genomes as compared to other ancestral genome reconstruction tools.

A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification.

PubMed

Zhengming Li; Zhihui Lai; Yong Xu; Jian Yang; Zhang, David

2017-02-01

Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.

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.

First Results of an “Artificial Retina” Processor Prototype

DOE PAGES

Cenci, Riccardo; Bedeschi, Franco; Marino, Pietro; ...

2016-11-15

We report on the performance of a specialized processor capable of reconstructing charged particle tracks in a realistic LHC silicon tracker detector, at the same speed of the readout and with sub-microsecond latency. The processor is based on an innovative pattern-recognition algorithm, called “artificial retina algorithm”, inspired from the vision system of mammals. A prototype of the processor has been designed, simulated, and implemented on Tel62 boards equipped with high-bandwidth Altera Stratix III FPGA devices. Also, the prototype is the first step towards a real-time track reconstruction device aimed at processing complex events of high-luminosity LHC experiments at 40 MHzmore » crossing rate.« less

First Results of an “Artificial Retina” Processor Prototype

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

Cenci, Riccardo; Bedeschi, Franco; Marino, Pietro

We report on the performance of a specialized processor capable of reconstructing charged particle tracks in a realistic LHC silicon tracker detector, at the same speed of the readout and with sub-microsecond latency. The processor is based on an innovative pattern-recognition algorithm, called “artificial retina algorithm”, inspired from the vision system of mammals. A prototype of the processor has been designed, simulated, and implemented on Tel62 boards equipped with high-bandwidth Altera Stratix III FPGA devices. Also, the prototype is the first step towards a real-time track reconstruction device aimed at processing complex events of high-luminosity LHC experiments at 40 MHzmore » crossing rate.« less

Structure-adaptive CBCT reconstruction using weighted total variation and Hessian penalties

PubMed Central

Shi, Qi; Sun, Nanbo; Sun, Tao; Wang, Jing; Tan, Shan

2016-01-01

The exposure of normal tissues to high radiation during cone-beam CT (CBCT) imaging increases the risk of cancer and genetic defects. Statistical iterative algorithms with the total variation (TV) penalty have been widely used for low dose CBCT reconstruction, with state-of-the-art performance in suppressing noise and preserving edges. However, TV is a first-order penalty and sometimes leads to the so-called staircase effect, particularly over regions with smooth intensity transition in the reconstruction images. A second-order penalty known as the Hessian penalty was recently used to replace TV to suppress the staircase effect in CBCT reconstruction at the cost of slightly blurring object edges. In this study, we proposed a new penalty, the TV-H, which combines TV and Hessian penalties for CBCT reconstruction in a structure-adaptive way. The TV-H penalty automatically differentiates the edges, gradual transition and uniform local regions within an image using the voxel gradient, and adaptively weights TV and Hessian according to the local image structures in the reconstruction process. Our proposed penalty retains the benefits of TV, including noise suppression and edge preservation. It also maintains the structures in regions with gradual intensity transition more successfully. A majorization-minimization (MM) approach was designed to optimize the objective energy function constructed with the TV-H penalty. The MM approach employed a quadratic upper bound of the original objective function, and the original optimization problem was changed to a series of quadratic optimization problems, which could be efficiently solved using the Gauss-Seidel update strategy. We tested the reconstruction algorithm on two simulated digital phantoms and two physical phantoms. Our experiments indicated that the TV-H penalty visually and quantitatively outperformed both TV and Hessian penalties. PMID:27699100

Structure-adaptive CBCT reconstruction using weighted total variation and Hessian penalties.

PubMed

Shi, Qi; Sun, Nanbo; Sun, Tao; Wang, Jing; Tan, Shan

2016-09-01

The exposure of normal tissues to high radiation during cone-beam CT (CBCT) imaging increases the risk of cancer and genetic defects. Statistical iterative algorithms with the total variation (TV) penalty have been widely used for low dose CBCT reconstruction, with state-of-the-art performance in suppressing noise and preserving edges. However, TV is a first-order penalty and sometimes leads to the so-called staircase effect, particularly over regions with smooth intensity transition in the reconstruction images. A second-order penalty known as the Hessian penalty was recently used to replace TV to suppress the staircase effect in CBCT reconstruction at the cost of slightly blurring object edges. In this study, we proposed a new penalty, the TV-H, which combines TV and Hessian penalties for CBCT reconstruction in a structure-adaptive way. The TV-H penalty automatically differentiates the edges, gradual transition and uniform local regions within an image using the voxel gradient, and adaptively weights TV and Hessian according to the local image structures in the reconstruction process. Our proposed penalty retains the benefits of TV, including noise suppression and edge preservation. It also maintains the structures in regions with gradual intensity transition more successfully. A majorization-minimization (MM) approach was designed to optimize the objective energy function constructed with the TV-H penalty. The MM approach employed a quadratic upper bound of the original objective function, and the original optimization problem was changed to a series of quadratic optimization problems, which could be efficiently solved using the Gauss-Seidel update strategy. We tested the reconstruction algorithm on two simulated digital phantoms and two physical phantoms. Our experiments indicated that the TV-H penalty visually and quantitatively outperformed both TV and Hessian penalties.

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

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.

Time-of-flight PET image reconstruction using origin ensembles.

PubMed

Wülker, Christian; Sitek, Arkadiusz; Prevrhal, Sven

2015-03-07

The origin ensemble (OE) algorithm is a novel statistical method for minimum-mean-square-error (MMSE) reconstruction of emission tomography data. This method allows one to perform reconstruction entirely in the image domain, i.e. without the use of forward and backprojection operations. We have investigated the OE algorithm in the context of list-mode (LM) time-of-flight (TOF) PET reconstruction. In this paper, we provide a general introduction to MMSE reconstruction, and a statistically rigorous derivation of the OE algorithm. We show how to efficiently incorporate TOF information into the reconstruction process, and how to correct for random coincidences and scattered events. To examine the feasibility of LM-TOF MMSE reconstruction with the OE algorithm, we applied MMSE-OE and standard maximum-likelihood expectation-maximization (ML-EM) reconstruction to LM-TOF phantom data with a count number typically registered in clinical PET examinations. We analyzed the convergence behavior of the OE algorithm, and compared reconstruction time and image quality to that of the EM algorithm. In summary, during the reconstruction process, MMSE-OE contrast recovery (CRV) remained approximately the same, while background variability (BV) gradually decreased with an increasing number of OE iterations. The final MMSE-OE images exhibited lower BV and a slightly lower CRV than the corresponding ML-EM images. The reconstruction time of the OE algorithm was approximately 1.3 times longer. At the same time, the OE algorithm can inherently provide a comprehensive statistical characterization of the acquired data. This characterization can be utilized for further data processing, e.g. in kinetic analysis and image registration, making the OE algorithm a promising approach in a variety of applications.

Time-of-flight PET image reconstruction using origin ensembles

NASA Astrophysics Data System (ADS)

Wülker, Christian; Sitek, Arkadiusz; Prevrhal, Sven

2015-03-01

The origin ensemble (OE) algorithm is a novel statistical method for minimum-mean-square-error (MMSE) reconstruction of emission tomography data. This method allows one to perform reconstruction entirely in the image domain, i.e. without the use of forward and backprojection operations. We have investigated the OE algorithm in the context of list-mode (LM) time-of-flight (TOF) PET reconstruction. In this paper, we provide a general introduction to MMSE reconstruction, and a statistically rigorous derivation of the OE algorithm. We show how to efficiently incorporate TOF information into the reconstruction process, and how to correct for random coincidences and scattered events. To examine the feasibility of LM-TOF MMSE reconstruction with the OE algorithm, we applied MMSE-OE and standard maximum-likelihood expectation-maximization (ML-EM) reconstruction to LM-TOF phantom data with a count number typically registered in clinical PET examinations. We analyzed the convergence behavior of the OE algorithm, and compared reconstruction time and image quality to that of the EM algorithm. In summary, during the reconstruction process, MMSE-OE contrast recovery (CRV) remained approximately the same, while background variability (BV) gradually decreased with an increasing number of OE iterations. The final MMSE-OE images exhibited lower BV and a slightly lower CRV than the corresponding ML-EM images. The reconstruction time of the OE algorithm was approximately 1.3 times longer. At the same time, the OE algorithm can inherently provide a comprehensive statistical characterization of the acquired data. This characterization can be utilized for further data processing, e.g. in kinetic analysis and image registration, making the OE algorithm a promising approach in a variety of applications.

Resolving phase ambiguities in the calibration of redundant interferometric arrays: implications for array design

NASA Astrophysics Data System (ADS)

Kurien, Binoy G.; Tarokh, Vahid; Rachlin, Yaron; Shah, Vinay N.; Ashcom, Jonathan B.

2016-10-01

We provide new results enabling robust interferometric image reconstruction in the presence of unknown aperture piston variation via the technique of redundant spacing calibration (RSC). The RSC technique uses redundant measurements of the same interferometric baseline with different pairs of apertures to reveal the piston variation among these pairs. In both optical and radio interferometry, the presence of phase-wrapping ambiguities in the measurements is a fundamental issue that needs to be addressed for reliable image reconstruction. In this paper, we show that these ambiguities affect recently developed RSC phasor-based reconstruction approaches operating on the complex visibilities, as well as traditional phase-based approaches operating on their logarithm. We also derive new sufficient conditions for an interferometric array to be immune to these ambiguities in the sense that their effect can be rendered benign in image reconstruction. This property, which we call wrap-invariance, has implications for the reliability of imaging via classical three-baseline phase closures as well as generalized closures. We show that wrap-invariance is conferred upon arrays whose interferometric graph satisfies a certain cycle-free condition. For cases in which this condition is not satisfied, a simple algorithm is provided for identifying those graph cycles which prevent its satisfaction. We apply this algorithm to diagnose and correct a member of a pattern family popular in the literature.

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.

Compressed sensing and the reconstruction of ultrafast 2D NMR data: Principles and biomolecular applications.

PubMed

Shrot, Yoav; Frydman, Lucio

2011-04-01

A topic of active investigation in 2D NMR relates to the minimum number of scans required for acquiring this kind of spectra, particularly when these are dictated by sampling rather than by sensitivity considerations. Reductions in this minimum number of scans have been achieved by departing from the regular sampling used to monitor the indirect domain, and relying instead on non-uniform sampling and iterative reconstruction algorithms. Alternatively, so-called "ultrafast" methods can compress the minimum number of scans involved in 2D NMR all the way to a minimum number of one, by spatially encoding the indirect domain information and subsequently recovering it via oscillating field gradients. Given ultrafast NMR's simultaneous recording of the indirect- and direct-domain data, this experiment couples the spectral constraints of these orthogonal domains - often calling for the use of strong acquisition gradients and large filter widths to fulfill the desired bandwidth and resolution demands along all spectral dimensions. This study discusses a way to alleviate these demands, and thereby enhance the method's performance and applicability, by combining spatial encoding with iterative reconstruction approaches. Examples of these new principles are given based on the compressed-sensed reconstruction of biomolecular 2D HSQC ultrafast NMR data, an approach that we show enables a decrease of the gradient strengths demanded in this type of experiments by up to 80%. Copyright © 2011 Elsevier Inc. All rights reserved.

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.

The structure of reconstructed chalcopyrite surfaces

NASA Astrophysics Data System (ADS)

Thinius, Sascha; Islam, Mazharul M.; Bredow, Thomas

2018-03-01

Chalcopyrite (CuFeS2) surfaces are of major interest for copper exploitation in aqueous solution, called leaching. Since leaching is a surface process knowledge of the surface structure, bonding pattern and oxidation states is important for improving the efficiency. At present such information is not available from experimental studies. Therefore a detailed computational study of chalcopyrite surfaces is performed. The structures of low-index stoichiometric chalcopyrite surfaces {hkl} h, k, l ∈ {0, 1, 2} have been studied with density functional theory (DFT) and global optimization strategies. We have applied ab initio molecular dynamics (MD) in combination with simulated annealing (SA) in order to explore possible reconstructions via a minima hopping (MH) algorithm. In almost all cases reconstruction involving substantial rearrangement has occurred accompanied by reduction of the surface energy. The analysis of the change in the coordination sphere and migration during reconstruction reveals that S-S dimers are formed on the surface. Further it was observed that metal atoms near the surface move toward the bulk forming metal alloys passivated by sulfur. The obtained surface energies of reconstructed surfaces are in the range of 0.53-0.95 J/m2.

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.

Alpha image reconstruction (AIR): A new iterative CT image reconstruction approach using voxel-wise alpha blending

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

Hofmann, Christian; Sawall, Stefan; Knaup, Michael

2014-06-15

Purpose: Iterative image reconstruction gains more and more interest in clinical routine, as it promises to reduce image noise (and thereby patient dose), to reduce artifacts, or to improve spatial resolution. Among vendors and researchers, however, there is no consensus of how to best achieve these aims. The general approach is to incorporatea priori knowledge into iterative image reconstruction, for example, by adding additional constraints to the cost function, which penalize variations between neighboring voxels. However, this approach to regularization in general poses a resolution noise trade-off because the stronger the regularization, and thus the noise reduction, the stronger themore » loss of spatial resolution and thus loss of anatomical detail. The authors propose a method which tries to improve this trade-off. The proposed reconstruction algorithm is called alpha image reconstruction (AIR). One starts with generating basis images, which emphasize certain desired image properties, like high resolution or low noise. The AIR algorithm reconstructs voxel-specific weighting coefficients that are applied to combine the basis images. By combining the desired properties of each basis image, one can generate an image with lower noise and maintained high contrast resolution thus improving the resolution noise trade-off. Methods: All simulations and reconstructions are performed in native fan-beam geometry. A water phantom with resolution bar patterns and low contrast disks is simulated. A filtered backprojection (FBP) reconstruction with a Ram-Lak kernel is used as a reference reconstruction. The results of AIR are compared against the FBP results and against a penalized weighted least squares reconstruction which uses total variation as regularization. The simulations are based on the geometry of the Siemens Somatom Definition Flash scanner. To quantitatively assess image quality, the authors analyze line profiles through resolution patterns to define a contrast factor for contrast-resolution plots. Furthermore, the authors calculate the contrast-to-noise ratio with the low contrast disks and the authors compare the agreement of the reconstructions with the ground truth by calculating the normalized cross-correlation and the root-mean-square deviation. To evaluate the clinical performance of the proposed method, the authors reconstruct patient data acquired with a Somatom Definition Flash dual source CT scanner (Siemens Healthcare, Forchheim, Germany). Results: The results of the simulation study show that among the compared algorithms AIR achieves the highest resolution and the highest agreement with the ground truth. Compared to the reference FBP reconstruction AIR is able to reduce the relative pixel noise by up to 50% and at the same time achieve a higher resolution by maintaining the edge information from the basis images. These results can be confirmed with the patient data. Conclusions: To evaluate the AIR algorithm simulated and measured patient data of a state-of-the-art clinical CT system were processed. It is shown, that generating CT images through the reconstruction of weighting coefficients has the potential to improve the resolution noise trade-off and thus to improve the dose usage in clinical CT.« less

Tomographic Neutron Imaging using SIRT

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

Gregor, Jens; FINNEY, Charles E A; Toops, Todd J

2013-01-01

Neutron imaging is complementary to x-ray imaging in that materials such as water and plastic are highly attenuating while material such as metal is nearly transparent. We showcase tomographic imaging of a diesel particulate filter. Reconstruction is done using a modified version of SIRT called PSIRT. We expand on previous work and introduce Tikhonov regularization. We show that near-optimal relaxation can still be achieved. The algorithmic ideas apply to cone beam x-ray CT and other inverse problems.

Group foliation of finite difference equations

NASA Astrophysics Data System (ADS)

Thompson, Robert; Valiquette, Francis

2018-06-01

Using the theory of equivariant moving frames, a group foliation method for invariant finite difference equations is developed. This method is analogous to the group foliation of differential equations and uses the symmetry group of the equation to decompose the solution process into two steps, called resolving and reconstruction. Our constructions are performed algorithmically and symbolically by making use of discrete recurrence relations among joint invariants. Applications to invariant finite difference equations that approximate differential equations are given.

Controlled wavelet domain sparsity for x-ray tomography

NASA Astrophysics Data System (ADS)

Purisha, Zenith; Rimpeläinen, Juho; Bubba, Tatiana; Siltanen, Samuli

2018-01-01

Tomographic reconstruction is an ill-posed inverse problem that calls for regularization. One possibility is to require sparsity of the unknown in an orthonormal wavelet basis. This, in turn, can be achieved by variational regularization, where the penalty term is the sum of the absolute values of the wavelet coefficients. The primal-dual fixed point algorithm showed that the minimizer of the variational regularization functional can be computed iteratively using a soft-thresholding operation. Choosing the soft-thresholding parameter \

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.

Fast-SG: an alignment-free algorithm for hybrid assembly.

PubMed

Di Genova, Alex; Ruz, Gonzalo A; Sagot, Marie-France; Maass, Alejandro

2018-05-01

Long-read sequencing technologies are the ultimate solution for genome repeats, allowing near reference-level reconstructions of large genomes. However, long-read de novo assembly pipelines are computationally intense and require a considerable amount of coverage, thereby hindering their broad application to the assembly of large genomes. Alternatively, hybrid assembly methods that combine short- and long-read sequencing technologies can reduce the time and cost required to produce de novo assemblies of large genomes. Here, we propose a new method, called Fast-SG, that uses a new ultrafast alignment-free algorithm specifically designed for constructing a scaffolding graph using light-weight data structures. Fast-SG can construct the graph from either short or long reads. This allows the reuse of efficient algorithms designed for short-read data and permits the definition of novel modular hybrid assembly pipelines. Using comprehensive standard datasets and benchmarks, we show how Fast-SG outperforms the state-of-the-art short-read aligners when building the scaffoldinggraph and can be used to extract linking information from either raw or error-corrected long reads. We also show how a hybrid assembly approach using Fast-SG with shallow long-read coverage (5X) and moderate computational resources can produce long-range and accurate reconstructions of the genomes of Arabidopsis thaliana (Ler-0) and human (NA12878). Fast-SG opens a door to achieve accurate hybrid long-range reconstructions of large genomes with low effort, high portability, and low cost.

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.

Recent Advances in X-ray Cone-beam Computed Laminography.

PubMed

O'Brien, Neil S; Boardman, Richard P; Sinclair, Ian; Blumensath, Thomas

2016-10-06

X-ray computed tomography is an established volume imaging technique used routinely in medical diagnosis, industrial non-destructive testing, and a wide range of scientific fields. Traditionally, computed tomography uses scanning geometries with a single axis of rotation together with reconstruction algorithms specifically designed for this setup. Recently there has however been increasing interest in more complex scanning geometries. These include so called X-ray computed laminography systems capable of imaging specimens with large lateral dimensions or large aspect ratios, neither of which are well suited to conventional CT scanning procedures. Developments throughout this field have thus been rapid, including the introduction of novel system trajectories, the application and refinement of various reconstruction methods, and the use of recently developed computational hardware and software techniques to accelerate reconstruction times. Here we examine the advances made in the last several years and consider their impact on the state of the art.

LCAMP: Location Constrained Approximate Message Passing for Compressed Sensing MRI

PubMed Central

Sung, Kyunghyun; Daniel, Bruce L; Hargreaves, Brian A

2016-01-01

Iterative thresholding methods have been extensively studied as faster alternatives to convex optimization methods for solving large-sized problems in compressed sensing. A novel iterative thresholding method called LCAMP (Location Constrained Approximate Message Passing) is presented for reducing computational complexity and improving reconstruction accuracy when a nonzero location (or sparse support) constraint can be obtained from view shared images. LCAMP modifies the existing approximate message passing algorithm by replacing the thresholding stage with a location constraint, which avoids adjusting regularization parameters or thresholding levels. This work is first compared with other conventional reconstruction methods using random 1D signals and then applied to dynamic contrast-enhanced breast MRI to demonstrate the excellent reconstruction accuracy (less than 2% absolute difference) and low computation time (5 - 10 seconds using Matlab) with highly undersampled 3D data (244 × 128 × 48; overall reduction factor = 10). PMID:23042658

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.

Phase retrieval from intensity-only data by relative entropy minimization.

PubMed

Deming, Ross W

2007-11-01

A recursive algorithm, which appears to be new, is presented for estimating the amplitude and phase of a wave field from intensity-only measurements on two or more scan planes at different axial positions. The problem is framed as a nonlinear optimization, in which the angular spectrum of the complex field model is adjusted in order to minimize the relative entropy, or Kullback-Leibler divergence, between the measured and reconstructed intensities. The most common approach to this so-called phase retrieval problem is a variation of the well-known Gerchberg-Saxton algorithm devised by Misell (J. Phys. D6, L6, 1973), which is efficient and extremely simple to implement. The new algorithm has a computational structure that is very similar to Misell's approach, despite the fundamental difference in the optimization criteria used for each. Based upon results from noisy simulated data, the new algorithm appears to be more robust than Misell's approach and to produce better results from low signal-to-noise ratio data. The convergence of the new algorithm is examined.

Label consistent K-SVD: learning a discriminative dictionary for recognition.

PubMed

Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

2013-11-01

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.

A CLASS OF RECONSTRUCTED DISCONTINUOUS GALERKIN METHODS IN COMPUTATIONAL FLUID DYNAMICS

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

Hong Luo; Yidong Xia; Robert Nourgaliev

2011-05-01

A class of reconstructed discontinuous Galerkin (DG) methods is presented to solve compressible flow problems on arbitrary grids. The idea is to combine the efficiency of the reconstruction methods in finite volume methods and the accuracy of the DG methods to obtain a better numerical algorithm in computational fluid dynamics. The beauty of the resulting reconstructed discontinuous Galerkin (RDG) methods is that they provide a unified formulation for both finite volume and DG methods, and contain both classical finite volume and standard DG methods as two special cases of the RDG methods, and thus allow for a direct efficiency comparison.more » Both Green-Gauss and least-squares reconstruction methods and a least-squares recovery method are presented to obtain a quadratic polynomial representation of the underlying linear discontinuous Galerkin solution on each cell via a so-called in-cell reconstruction process. The devised in-cell reconstruction is aimed to augment the accuracy of the discontinuous Galerkin method by increasing the order of the underlying polynomial solution. These three reconstructed discontinuous Galerkin methods are used to compute a variety of compressible flow problems on arbitrary meshes to assess their accuracy. The numerical experiments demonstrate that all three reconstructed discontinuous Galerkin methods can significantly improve the accuracy of the underlying second-order DG method, although the least-squares reconstructed DG method provides the best performance in terms of both accuracy, efficiency, and robustness.« less

Low-Complexity Lossless and Near-Lossless Data Compression Technique for Multispectral Imagery

NASA Technical Reports Server (NTRS)

Xie, Hua; Klimesh, Matthew A.

2009-01-01

This work extends the lossless data compression technique described in Fast Lossless Compression of Multispectral- Image Data, (NPO-42517) NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26. The original technique was extended to include a near-lossless compression option, allowing substantially smaller compressed file sizes when a small amount of distortion can be tolerated. Near-lossless compression is obtained by including a quantization step prior to encoding of prediction residuals. The original technique uses lossless predictive compression and is designed for use on multispectral imagery. A lossless predictive data compression algorithm compresses a digitized signal one sample at a time as follows: First, a sample value is predicted from previously encoded samples. The difference between the actual sample value and the prediction is called the prediction residual. The prediction residual is encoded into the compressed file. The decompressor can form the same predicted sample and can decode the prediction residual from the compressed file, and so can reconstruct the original sample. A lossless predictive compression algorithm can generally be converted to a near-lossless compression algorithm by quantizing the prediction residuals prior to encoding them. In this case, since the reconstructed sample values will not be identical to the original sample values, the encoder must determine the values that will be reconstructed and use these values for predicting later sample values. The technique described here uses this method, starting with the original technique, to allow near-lossless compression. The extension to allow near-lossless compression adds the ability to achieve much more compression when small amounts of distortion are tolerable, while retaining the low complexity and good overall compression effectiveness of the original algorithm.

Inverse scattering and refraction corrected reflection for breast cancer imaging

NASA Astrophysics Data System (ADS)

Wiskin, J.; Borup, D.; Johnson, S.; Berggren, M.; Robinson, D.; Smith, J.; Chen, J.; Parisky, Y.; Klock, John

2010-03-01

Reflection ultrasound (US) has been utilized as an adjunct imaging modality for over 30 years. TechniScan, Inc. has developed unique, transmission and concomitant reflection algorithms which are used to reconstruct images from data gathered during a tomographic breast scanning process called Warm Bath Ultrasound (WBU™). The transmission algorithm yields high resolution, 3D, attenuation and speed of sound (SOS) images. The reflection algorithm is based on canonical ray tracing utilizing refraction correction via the SOS and attenuation reconstructions. The refraction correction reflection algorithm allows 360 degree compounding resulting in the reflection image. The requisite data are collected when scanning the entire breast in a 33° C water bath, on average in 8 minutes. This presentation explains how the data are collected and processed by the 3D transmission and reflection imaging mode algorithms. The processing is carried out using two NVIDIA® Tesla™ GPU processors, accessing data on a 4-TeraByte RAID. The WBU™ images are displayed in a DICOM viewer that allows registration of all three modalities. Several representative cases are presented to demonstrate potential diagnostic capability including: a cyst, fibroadenoma, and a carcinoma. WBU™ images (SOS, attenuation, and reflection modalities) are shown along with their respective mammograms and standard ultrasound images. In addition, anatomical studies are shown comparing WBU™ images and MRI images of a cadaver breast. This innovative technology is designed to provide additional tools in the armamentarium for diagnosis of breast disease.

Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks.

PubMed

Casero, Ramón; Siedlecka, Urszula; Jones, Elizabeth S; Gruscheski, Lena; Gibb, Matthew; Schneider, Jürgen E; Kohl, Peter; Grau, Vicente

2017-05-01

Traditional histology is the gold standard for tissue studies, but it is intrinsically reliant on two-dimensional (2D) images. Study of volumetric tissue samples such as whole hearts produces a stack of misaligned and distorted 2D images that need to be reconstructed to recover a congruent volume with the original sample's shape. In this paper, we develop a mathematical framework called Transformation Diffusion (TD) for stack alignment refinement as a solution to the heat diffusion equation. This general framework does not require contour segmentation, is independent of the registration method used, and is trivially parallelizable. After the first stack sweep, we also replace registration operations by operations in the space of transformations, several orders of magnitude faster and less memory-consuming. Implementing TD with operations in the space of transformations produces our Transformation Diffusion Reconstruction (TDR) algorithm, applicable to general transformations that are closed under inversion and composition. In particular, we provide formulas for translation and affine transformations. We also propose an Approximated TDR (ATDR) algorithm that extends the same principles to tensor-product B-spline transformations. Using TDR and ATDR, we reconstruct a full mouse heart at pixel size 0.92µm×0.92µm, cut 10µm thick, spaced 20µm (84G). Our algorithms employ only local information from transformations between neighboring slices, but the TD framework allows theoretical analysis of the refinement as applying a global Gaussian low-pass filter to the unknown stack misalignments. We also show that reconstruction without an external reference produces large shape artifacts in a cardiac specimen while still optimizing slice-to-slice alignment. To overcome this problem, we use a pre-cutting blockface imaging process previously developed by our group that takes advantage of Brewster's angle and a polarizer to capture the outline of only the topmost layer of wax in the block containing embedded tissue for histological sectioning. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

SparseBeads data: benchmarking sparsity-regularized computed tomography

NASA Astrophysics Data System (ADS)

Jørgensen, Jakob S.; Coban, Sophia B.; Lionheart, William R. B.; McDonald, Samuel A.; Withers, Philip J.

2017-12-01

Sparsity regularization (SR) such as total variation (TV) minimization allows accurate image reconstruction in x-ray computed tomography (CT) from fewer projections than analytical methods. Exactly how few projections suffice and how this number may depend on the image remain poorly understood. Compressive sensing connects the critical number of projections to the image sparsity, but does not cover CT, however empirical results suggest a similar connection. The present work establishes for real CT data a connection between gradient sparsity and the sufficient number of projections for accurate TV-regularized reconstruction. A collection of 48 x-ray CT datasets called SparseBeads was designed for benchmarking SR reconstruction algorithms. Beadpacks comprising glass beads of five different sizes as well as mixtures were scanned in a micro-CT scanner to provide structured datasets with variable image sparsity levels, number of projections and noise levels to allow the systematic assessment of parameters affecting performance of SR reconstruction algorithms6. Using the SparseBeads data, TV-regularized reconstruction quality was assessed as a function of numbers of projections and gradient sparsity. The critical number of projections for satisfactory TV-regularized reconstruction increased almost linearly with the gradient sparsity. This establishes a quantitative guideline from which one may predict how few projections to acquire based on expected sample sparsity level as an aid in planning of dose- or time-critical experiments. The results are expected to hold for samples of similar characteristics, i.e. consisting of few, distinct phases with relatively simple structure. Such cases are plentiful in porous media, composite materials, foams, as well as non-destructive testing and metrology. For samples of other characteristics the proposed methodology may be used to investigate similar relations.

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.

a Line-Based 3d Roof Model Reconstruction Algorithm: Tin-Merging and Reshaping (tmr)

NASA Astrophysics Data System (ADS)

Rau, J.-Y.

2012-07-01

Three-dimensional building model is one of the major components of a cyber-city and is vital for the realization of 3D GIS applications. In the last decade, the airborne laser scanning (ALS) data is widely used for 3D building model reconstruction and object extraction. Instead, based on 3D roof structural lines, this paper presents a novel algorithm for automatic roof models reconstruction. A line-based roof model reconstruction algorithm, called TIN-Merging and Reshaping (TMR), is proposed. The roof structural line, such as edges, eaves and ridges, can be measured manually from aerial stereo-pair, derived by feature line matching or inferred from ALS data. The originality of the TMR algorithm for 3D roof modelling is to perform geometric analysis and topology reconstruction among those unstructured lines and then reshapes the roof-type using elevation information from the 3D structural lines. For topology reconstruction, a line constrained Delaunay Triangulation algorithm is adopted where the input structural lines act as constraint and their vertex act as input points. Thus, the constructed TINs will not across the structural lines. Later at the stage of Merging, the shared edge between two TINs will be check if the original structural line exists. If not, those two TINs will be merged into a polygon. Iterative checking and merging of any two neighboured TINs/Polygons will result in roof polygons on the horizontal plane. Finally, at the Reshaping stage any two structural lines with fixed height will be used to adjust a planar function for the whole roof polygon. In case ALS data exist, the Reshaping stage can be simplified by adjusting the point cloud within the roof polygon. The proposed scheme reduces the complexity of 3D roof modelling and makes the modelling process easier. Five test datasets provided by ISPRS WG III/4 located at downtown Toronto, Canada and Vaihingen, Germany are used for experiment. The test sites cover high rise buildings and residential area with diverse roof type. For performance evaluation, the adopted roof structural lines are manually measured from the provided stereo-pair. Experimental results indicate a nearly 100% success rate for topology reconstruction was achieved provided that the 3D structural lines can be enclosed as polygons. On the other hand, the success rate at the Reshaping stage is dependent on the complexity of the rooftop structure. Thus, a visual inspection and semi-automatic adjustment of roof-type is suggested and implemented to complete the roof modelling. The results demonstrate that the proposed scheme is robust and reliable with a high degree of completeness, correctness, and quality, even when a group of connected buildings with multiple layers and mixed roof types is processed.

Noniterative MAP reconstruction using sparse matrix representations.

PubMed

Cao, Guangzhi; Bouman, Charles A; Webb, Kevin J

2009-09-01

We present a method for noniterative maximum a posteriori (MAP) tomographic reconstruction which is based on the use of sparse matrix representations. Our approach is to precompute and store the inverse matrix required for MAP reconstruction. This approach has generally not been used in the past because the inverse matrix is typically large and fully populated (i.e., not sparse). In order to overcome this problem, we introduce two new ideas. The first idea is a novel theory for the lossy source coding of matrix transformations which we refer to as matrix source coding. This theory is based on a distortion metric that reflects the distortions produced in the final matrix-vector product, rather than the distortions in the coded matrix itself. The resulting algorithms are shown to require orthonormal transformations of both the measurement data and the matrix rows and columns before quantization and coding. The second idea is a method for efficiently storing and computing the required orthonormal transformations, which we call a sparse-matrix transform (SMT). The SMT is a generalization of the classical FFT in that it uses butterflies to compute an orthonormal transform; but unlike an FFT, the SMT uses the butterflies in an irregular pattern, and is numerically designed to best approximate the desired transforms. We demonstrate the potential of the noniterative MAP reconstruction with examples from optical tomography. The method requires offline computation to encode the inverse transform. However, once these offline computations are completed, the noniterative MAP algorithm is shown to reduce both storage and computation by well over two orders of magnitude, as compared to a linear iterative reconstruction methods.

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.

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.

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.

Super-Resolution Image Reconstruction Applied to Medical Ultrasound

NASA Astrophysics Data System (ADS)

Ellis, Michael

Ultrasound is the preferred imaging modality for many diagnostic applications due to its real-time image reconstruction and low cost. Nonetheless, conventional ultrasound is not used in many applications because of limited spatial resolution and soft tissue contrast. Most commercial ultrasound systems reconstruct images using a simple delay-and-sum architecture on receive, which is fast and robust but does not utilize all information available in the raw data. Recently, more sophisticated image reconstruction methods have been developed that make use of far more information in the raw data to improve resolution and contrast. One such method is the Time-Domain Optimized Near-Field Estimator (TONE), which employs a maximum a priori estimation to solve a highly underdetermined problem, given a well-defined system model. TONE has been shown to significantly improve both the contrast and resolution of ultrasound images when compared to conventional methods. However, TONE's lack of robustness to variations from the system model and extremely high computational cost hinder it from being readily adopted in clinical scanners. This dissertation aims to reduce the impact of TONE's shortcomings, transforming it from an academic construct to a clinically viable image reconstruction algorithm. By altering the system model from a collection of individual hypothetical scatterers to a collection of weighted, diffuse regions, dTONE is able to achieve much greater robustness to modeling errors. A method for efficient parallelization of dTONE is presented that reduces reconstruction time by more than an order of magnitude with little loss in image fidelity. An alternative reconstruction algorithm, called qTONE, is also developed and is able to reduce reconstruction times by another two orders of magnitude while simultaneously improving image contrast. Each of these methods for improving TONE are presented, their limitations are explored, and all are used in concert to reconstruct in vivo images of a human testicle. In all instances, the methods presented here outperform conventional image reconstruction methods by a significant margin. As TONE and its variants are general image reconstruction techniques, the theories and research presented here have the potential to significantly improve not only ultrasound's clinical utility, but that of other imaging modalities as well.

Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering

USGS Publications Warehouse

Sethi, Suresh; Linden, Daniel; Wenburg, John; Lewis, Cara; Lemons, Patrick R.; Fuller, Angela K.; Hare, Matthew P.

2016-01-01

Error-tolerant likelihood-based match calling presents a promising technique to accurately identify recapture events in genetic mark–recapture studies by combining probabilities of latent genotypes and probabilities of observed genotypes, which may contain genotyping errors. Combined with clustering algorithms to group samples into sets of recaptures based upon pairwise match calls, these tools can be used to reconstruct accurate capture histories for mark–recapture modelling. Here, we assess the performance of a recently introduced error-tolerant likelihood-based match-calling model and sample clustering algorithm for genetic mark–recapture studies. We assessed both biallelic (i.e. single nucleotide polymorphisms; SNP) and multiallelic (i.e. microsatellite; MSAT) markers using a combination of simulation analyses and case study data on Pacific walrus (Odobenus rosmarus divergens) and fishers (Pekania pennanti). A novel two-stage clustering approach is demonstrated for genetic mark–recapture applications. First, repeat captures within a sampling occasion are identified. Subsequently, recaptures across sampling occasions are identified. The likelihood-based matching protocol performed well in simulation trials, demonstrating utility for use in a wide range of genetic mark–recapture studies. Moderately sized SNP (64+) and MSAT (10–15) panels produced accurate match calls for recaptures and accurate non-match calls for samples from closely related individuals in the face of low to moderate genotyping error. Furthermore, matching performance remained stable or increased as the number of genetic markers increased, genotyping error notwithstanding.

Determination of calibration parameters of a VRX CT system using an “Amoeba” algorithm

PubMed Central

Jordan, Lawrence M.; DiBianca, Frank A.; Melnyk, Roman; Choudhary, Apoorva; Shukla, Hemant; Laughter, Joseph; Gaber, M. Waleed

2008-01-01

Efforts to improve the spatial resolution of CT scanners have focused mainly on reducing the source and detector element sizes, ignoring losses from the size of the secondary-ionization charge “clouds” created by the detected x-ray photons, i.e., the “physics limit.” This paper focuses on implementing a technique called “projective compression.” which allows further reduction in effective cell size while overcoming the physics limit as well. Projective compression signifies detector geometries in which the apparent cell size is smaller than the physical cell size, allowing large resolution boosts. A realization of this technique has been developed with a dual-arm “variable-resolution x-ray” (VRX) detector. Accurate values of the geometrical parameters are needed to convert VRX outputs to formats ready for optimal image reconstruction by standard CT techniques. The required calibrating data are obtained by scanning a rotating pin and fitting a theoretical parametric curve (using a multi-parameter minimization algorithm) to the resulting pin sinogram. Excellent fits are obtained for both detector-arm sections with an average (maximum) fit deviation of ~0.05 (0.1) detector cell width. Fit convergence and sensitivity to starting conditions are considered. Pre- and post-optimization reconstructions of the alignment pin and a biological subject reconstruction after calibration are shown. PMID:19430581

Determination of calibration parameters of a VRX CT system using an "Amoeba" algorithm.

PubMed

Jordan, Lawrence M; Dibianca, Frank A; Melnyk, Roman; Choudhary, Apoorva; Shukla, Hemant; Laughter, Joseph; Gaber, M Waleed

2004-01-01

Efforts to improve the spatial resolution of CT scanners have focused mainly on reducing the source and detector element sizes, ignoring losses from the size of the secondary-ionization charge "clouds" created by the detected x-ray photons, i.e., the "physics limit." This paper focuses on implementing a technique called "projective compression." which allows further reduction in effective cell size while overcoming the physics limit as well. Projective compression signifies detector geometries in which the apparent cell size is smaller than the physical cell size, allowing large resolution boosts. A realization of this technique has been developed with a dual-arm "variable-resolution x-ray" (VRX) detector. Accurate values of the geometrical parameters are needed to convert VRX outputs to formats ready for optimal image reconstruction by standard CT techniques. The required calibrating data are obtained by scanning a rotating pin and fitting a theoretical parametric curve (using a multi-parameter minimization algorithm) to the resulting pin sinogram. Excellent fits are obtained for both detector-arm sections with an average (maximum) fit deviation of ~0.05 (0.1) detector cell width. Fit convergence and sensitivity to starting conditions are considered. Pre- and post-optimization reconstructions of the alignment pin and a biological subject reconstruction after calibration are shown.

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

Aad, G.; Abbott, B.; Abdallah, J.

The reconstruction and calibration algorithms used to calculate missing transverse momentum (E miss T) with the ATLAS detector exploit energy deposits in the calorimeter and tracks reconstructed in the inner detector as well as the muon spectrometer. Various strategies are used to suppress effects arising from additional proton–proton interactions, called pileup, concurrent with the hard-scatter processes. Tracking information is used to distinguish contributions from the pileup interactions using their vertex separation along the beam axis. The performance of the E miss T reconstruction algorithms, especially with respect to the amount of pileup, is evaluated using data collected in proton–proton collisionsmore » at a centre-of-mass energy of 8 TeV during 2012, and results are shown for a data sample corresponding to an integrated luminosity of 20.3fb –1. The simulation and modelling of E miss T in events containing a Z boson decaying to two charged leptons (electrons or muons) or a W boson decaying to a charged lepton and a neutrino are compared to data. The acceptance for different event topologies, with and without high transverse momentum neutrinos, is shown for a range of threshold criteria for E miss T, and estimates of the systematic uncertainties in the E miss T measurements are presented.« less

Wind speed time series reconstruction using a hybrid neural genetic approach

NASA Astrophysics Data System (ADS)

Rodriguez, H.; Flores, J. J.; Puig, V.; Morales, L.; Guerra, A.; Calderon, F.

2017-11-01

Currently, electric energy is used in practically all modern human activities. Most of the energy produced came from fossil fuels, making irreversible damage to the environment. Lately, there has been an effort by nations to produce energy using clean methods, such as solar and wind energy, among others. Wind energy is one of the cleanest alternatives. However, the wind speed is not constant, making the planning and operation at electric power systems a difficult activity. Knowing in advance the amount of raw material (wind speed) used for energy production allows us to estimate the energy to be generated by the power plant, helping the maintenance planning, the operational management, optimal operational cost. For these reasons, the forecast of wind speed becomes a necessary task. The forecast process involves the use of past observations from the variable to forecast (wind speed). To measure wind speed, weather stations use devices called anemometers, but due to poor maintenance, connection error, or natural wear, they may present false or missing data. In this work, a hybrid methodology is proposed, and it uses a compact genetic algorithm with an artificial neural network to reconstruct wind speed time series. The proposed methodology reconstructs the time series using a ANN defined by a Compact Genetic Algorithm.

Calculating tissue shear modulus and pressure by 2D log-elastographic methods

NASA Astrophysics Data System (ADS)

McLaughlin, Joyce R.; Zhang, Ning; Manduca, Armando

2010-08-01

Shear modulus imaging, often called elastography, enables detection and characterization of tissue abnormalities. In this paper the data are two displacement components obtained from successive MR or ultrasound data sets acquired while the tissue is excited mechanically. A 2D plane strain elastic model is assumed to govern the 2D displacement, u. The shear modulus, μ, is unknown and whether or not the first Lamé parameter, λ, is known the pressure p = λ∇ sdot u which is present in the plane strain model cannot be measured and is unreliably computed from measured data and can be shown to be an order one quantity in the units kPa. So here we present a 2D log-elastographic inverse algorithm that (1) simultaneously reconstructs the shear modulus, μ, and p, which together satisfy a first-order partial differential equation system, with the goal of imaging μ (2) controls potential exponential growth in the numerical error and (3) reliably reconstructs the quantity p in the inverse algorithm as compared to the same quantity computed with a forward algorithm. This work generalizes the log-elastographic algorithm in Lin et al (2009 Inverse Problems 25) which uses one displacement component, is derived assuming that the component satisfies the wave equation and is tested on synthetic data computed with the wave equation model. The 2D log-elastographic algorithm is tested on 2D synthetic data and 2D in vivo data from Mayo Clinic. We also exhibit examples to show that the 2D log-elastographic algorithm improves the quality of the recovered images as compared to the log-elastographic and direct inversion algorithms.

Higher order total variation regularization for EIT reconstruction.

PubMed

Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Zhang, Fan; Mueller-Lisse, Ullrich; Moeller, Knut

2018-01-08

Electrical impedance tomography (EIT) attempts to reveal the conductivity distribution of a domain based on the electrical boundary condition. This is an ill-posed inverse problem; its solution is very unstable. Total variation (TV) regularization is one of the techniques commonly employed to stabilize reconstructions. However, it is well known that TV regularization induces staircase effects, which are not realistic in clinical applications. To reduce such artifacts, modified TV regularization terms considering a higher order differential operator were developed in several previous studies. One of them is called total generalized variation (TGV) regularization. TGV regularization has been successively applied in image processing in a regular grid context. In this study, we adapted TGV regularization to the finite element model (FEM) framework for EIT reconstruction. Reconstructions using simulation and clinical data were performed. First results indicate that, in comparison to TV regularization, TGV regularization promotes more realistic images. Graphical abstract Reconstructed conductivity changes located on selected vertical lines. For each of the reconstructed images as well as the ground truth image, conductivity changes located along the selected left and right vertical lines are plotted. In these plots, the notation GT in the legend stands for ground truth, TV stands for total variation method, and TGV stands for total generalized variation method. Reconstructed conductivity distributions from the GREIT algorithm are also demonstrated.

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.

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.

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.

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.

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

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

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.

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.

Reconstruction of noisy and blurred images using blur kernel

NASA Astrophysics Data System (ADS)

Ellappan, Vijayan; Chopra, Vishal

2017-11-01

Blur is a common in so many digital images. Blur can be caused by motion of the camera and scene object. In this work we proposed a new method for deblurring images. This work uses sparse representation to identify the blur kernel. By analyzing the image coordinates Using coarse and fine, we fetch the kernel based image coordinates and according to that observation we get the motion angle of the shaken or blurred image. Then we calculate the length of the motion kernel using radon transformation and Fourier for the length calculation of the image and we use Lucy Richardson algorithm which is also called NON-Blind(NBID) Algorithm for more clean and less noisy image output. All these operation will be performed in MATLAB IDE.

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.

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.

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.

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.

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.

Metal-induced streak artifact reduction using iterative reconstruction algorithms in x-ray computed tomography image of the dentoalveolar region.

PubMed

Dong, Jian; Hayakawa, Yoshihiko; Kannenberg, Sven; Kober, Cornelia

2013-02-01

The objective of this study was to reduce metal-induced streak artifact on oral and maxillofacial x-ray computed tomography (CT) images by developing the fast statistical image reconstruction system using iterative reconstruction algorithms. Adjacent CT images often depict similar anatomical structures in thin slices. So, first, images were reconstructed using the same projection data of an artifact-free image. Second, images were processed by the successive iterative restoration method where projection data were generated from reconstructed image in sequence. Besides the maximum likelihood-expectation maximization algorithm, the ordered subset-expectation maximization algorithm (OS-EM) was examined. Also, small region of interest (ROI) setting and reverse processing were applied for improving performance. Both algorithms reduced artifacts instead of slightly decreasing gray levels. The OS-EM and small ROI reduced the processing duration without apparent detriments. Sequential and reverse processing did not show apparent effects. Two alternatives in iterative reconstruction methods were effective for artifact reduction. The OS-EM algorithm and small ROI setting improved the performance. Copyright © 2012 Elsevier Inc. All rights reserved.

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.

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.

Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.

PubMed

Guo, Xiaobo; Zhang, Ye; Hu, Wenhao; Tan, Haizhu; Wang, Xueqin

2014-01-01

Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.

Inferring Nonlinear Gene Regulatory Networks from Gene Expression Data Based on Distance Correlation

PubMed Central

Guo, Xiaobo; Zhang, Ye; Hu, Wenhao; Tan, Haizhu; Wang, Xueqin

2014-01-01

Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference. PMID:24551058

Adaptive multimode signal reconstruction from time–frequency representations

PubMed Central

Meignen, Sylvain; Oberlin, Thomas; Depalle, Philippe; Flandrin, Patrick

2016-01-01

This paper discusses methods for the adaptive reconstruction of the modes of multicomponent AM–FM signals by their time–frequency (TF) representation derived from their short-time Fourier transform (STFT). The STFT of an AM–FM component or mode spreads the information relative to that mode in the TF plane around curves commonly called ridges. An alternative view is to consider a mode as a particular TF domain termed a basin of attraction. Here we discuss two new approaches to mode reconstruction. The first determines the ridge associated with a mode by considering the location where the direction of the reassignment vector sharply changes, the technique used to determine the basin of attraction being directly derived from that used for ridge extraction. A second uses the fact that the STFT of a signal is fully characterized by its zeros (and then the particular distribution of these zeros for Gaussian noise) to deduce an algorithm to compute the mode domains. For both techniques, mode reconstruction is then carried out by simply integrating the information inside these basins of attraction or domains. PMID:26953184

Eigenspace-based minimum variance adaptive beamformer combined with delay multiply and sum: experimental study

NASA Astrophysics Data System (ADS)

Mozaffarzadeh, Moein; Mahloojifar, Ali; Nasiriavanaki, Mohammadreza; Orooji, Mahdi

2018-02-01

Delay and sum (DAS) is the most common beamforming algorithm in linear-array photoacoustic imaging (PAI) as a result of its simple implementation. However, it leads to a low resolution and high sidelobes. Delay multiply and sum (DMAS) was used to address the incapabilities of DAS, providing a higher image quality. However, the resolution improvement is not well enough compared to eigenspace-based minimum variance (EIBMV). In this paper, the EIBMV beamformer has been combined with DMAS algebra, called EIBMV-DMAS, using the expansion of DMAS algorithm. The proposed method is used as the reconstruction algorithm in linear-array PAI. EIBMV-DMAS is experimentally evaluated where the quantitative and qualitative results show that it outperforms DAS, DMAS and EIBMV. The proposed method degrades the sidelobes for about 365 %, 221 % and 40 %, compared to DAS, DMAS and EIBMV, respectively. Moreover, EIBMV-DMAS improves the SNR about 158 %, 63 % and 20 %, respectively.

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.

Research on Ship-Radiated Noise Denoising Using Secondary Variational Mode Decomposition and Correlation Coefficient.

PubMed

Li, Yuxing; Li, Yaan; Chen, Xiao; Yu, Jing

2017-12-26

As the sound signal of ships obtained by sensors contains other many significant characteristics of ships and called ship-radiated noise (SN), research into a denoising algorithm and its application has obtained great significance. Using the advantage of variational mode decomposition (VMD) combined with the correlation coefficient for denoising, a hybrid secondary denoising algorithm is proposed using secondary VMD combined with a correlation coefficient (CC). First, different kinds of simulation signals are decomposed into several bandwidth-limited intrinsic mode functions (IMFs) using VMD, where the decomposition number by VMD is equal to the number by empirical mode decomposition (EMD); then, the CCs between the IMFs and the simulation signal are calculated respectively. The noise IMFs are identified by the CC threshold and the rest of the IMFs are reconstructed in order to realize the first denoising process. Finally, secondary denoising of the simulation signal can be accomplished by repeating the above steps of decomposition, screening and reconstruction. The final denoising result is determined according to the CC threshold. The denoising effect is compared under the different signal-to-noise ratio and the time of decomposition by VMD. Experimental results show the validity of the proposed denoising algorithm using secondary VMD (2VMD) combined with CC compared to EMD denoising, ensemble EMD (EEMD) denoising, VMD denoising and cubic VMD (3VMD) denoising, as well as two denoising algorithms presented recently. The proposed denoising algorithm is applied to feature extraction and classification for SN signals, which can effectively improve the recognition rate of different kinds of ships.

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

Automated red blood cells extraction from holographic images using fully convolutional neural networks.

PubMed

Yi, Faliu; Moon, Inkyu; Javidi, Bahram

2017-10-01

In this paper, we present two models for automatically extracting red blood cells (RBCs) from RBCs holographic images based on a deep learning fully convolutional neural network (FCN) algorithm. The first model, called FCN-1, only uses the FCN algorithm to carry out RBCs prediction, whereas the second model, called FCN-2, combines the FCN approach with the marker-controlled watershed transform segmentation scheme to achieve RBCs extraction. Both models achieve good segmentation accuracy. In addition, the second model has much better performance in terms of cell separation than traditional segmentation methods. In the proposed methods, the RBCs phase images are first numerically reconstructed from RBCs holograms recorded with off-axis digital holographic microscopy. Then, some RBCs phase images are manually segmented and used as training data to fine-tune the FCN. Finally, each pixel in new input RBCs phase images is predicted into either foreground or background using the trained FCN models. The RBCs prediction result from the first model is the final segmentation result, whereas the result from the second model is used as the internal markers of the marker-controlled transform algorithm for further segmentation. Experimental results show that the given schemes can automatically extract RBCs from RBCs phase images and much better RBCs separation results are obtained when the FCN technique is combined with the marker-controlled watershed segmentation algorithm.

Automated red blood cells extraction from holographic images using fully convolutional neural networks

PubMed Central

Yi, Faliu; Moon, Inkyu; Javidi, Bahram

2017-01-01

In this paper, we present two models for automatically extracting red blood cells (RBCs) from RBCs holographic images based on a deep learning fully convolutional neural network (FCN) algorithm. The first model, called FCN-1, only uses the FCN algorithm to carry out RBCs prediction, whereas the second model, called FCN-2, combines the FCN approach with the marker-controlled watershed transform segmentation scheme to achieve RBCs extraction. Both models achieve good segmentation accuracy. In addition, the second model has much better performance in terms of cell separation than traditional segmentation methods. In the proposed methods, the RBCs phase images are first numerically reconstructed from RBCs holograms recorded with off-axis digital holographic microscopy. Then, some RBCs phase images are manually segmented and used as training data to fine-tune the FCN. Finally, each pixel in new input RBCs phase images is predicted into either foreground or background using the trained FCN models. The RBCs prediction result from the first model is the final segmentation result, whereas the result from the second model is used as the internal markers of the marker-controlled transform algorithm for further segmentation. Experimental results show that the given schemes can automatically extract RBCs from RBCs phase images and much better RBCs separation results are obtained when the FCN technique is combined with the marker-controlled watershed segmentation algorithm. PMID:29082078

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

DOE PAGES

Bicer, Tekin; Gursoy, Doga; Andrade, Vincent De; ...

2017-01-28

Here, synchrotron light source and detector technologies enable scientists to perform advanced experiments. These scientific instruments and experiments produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used data acquisition technique at light sources is Computed Tomography, which can generate tens of GB/s depending on x-ray range. A large-scale tomographic dataset, such as mouse brain, may require hours of computation time with a medium size workstation. In this paper, we present Trace, a data-intensive computing middleware we developed for implementation and parallelization of iterative tomographic reconstruction algorithms. Tracemore » provides fine-grained reconstruction of tomography datasets using both (thread level) shared memory and (process level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations we have done on the replicated reconstruction objects and evaluate them using a shale and a mouse brain sinogram. Our experimental evaluations show that the applied optimizations and parallelization techniques can provide 158x speedup (using 32 compute nodes) over single core configuration, which decreases the reconstruction time of a sinogram (with 4501 projections and 22400 detector resolution) from 12.5 hours to less than 5 minutes per iteration.« less

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

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

Bicer, Tekin; Gursoy, Doga; Andrade, Vincent De

Here, synchrotron light source and detector technologies enable scientists to perform advanced experiments. These scientific instruments and experiments produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used data acquisition technique at light sources is Computed Tomography, which can generate tens of GB/s depending on x-ray range. A large-scale tomographic dataset, such as mouse brain, may require hours of computation time with a medium size workstation. In this paper, we present Trace, a data-intensive computing middleware we developed for implementation and parallelization of iterative tomographic reconstruction algorithms. Tracemore » provides fine-grained reconstruction of tomography datasets using both (thread level) shared memory and (process level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations we have done on the replicated reconstruction objects and evaluate them using a shale and a mouse brain sinogram. Our experimental evaluations show that the applied optimizations and parallelization techniques can provide 158x speedup (using 32 compute nodes) over single core configuration, which decreases the reconstruction time of a sinogram (with 4501 projections and 22400 detector resolution) from 12.5 hours to less than 5 minutes per iteration.« less

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.

GIGA: a simple, efficient algorithm for gene tree inference in the genomic age

PubMed Central

2010-01-01

Background Phylogenetic relationships between genes are not only of theoretical interest: they enable us to learn about human genes through the experimental work on their relatives in numerous model organisms from bacteria to fruit flies and mice. Yet the most commonly used computational algorithms for reconstructing gene trees can be inaccurate for numerous reasons, both algorithmic and biological. Additional information beyond gene sequence data has been shown to improve the accuracy of reconstructions, though at great computational cost. Results We describe a simple, fast algorithm for inferring gene phylogenies, which makes use of information that was not available prior to the genomic age: namely, a reliable species tree spanning much of the tree of life, and knowledge of the complete complement of genes in a species' genome. The algorithm, called GIGA, constructs trees agglomeratively from a distance matrix representation of sequences, using simple rules to incorporate this genomic age information. GIGA makes use of a novel conceptualization of gene trees as being composed of orthologous subtrees (containing only speciation events), which are joined by other evolutionary events such as gene duplication or horizontal gene transfer. An important innovation in GIGA is that, at every step in the agglomeration process, the tree is interpreted/reinterpreted in terms of the evolutionary events that created it. Remarkably, GIGA performs well even when using a very simple distance metric (pairwise sequence differences) and no distance averaging over clades during the tree construction process. Conclusions GIGA is efficient, allowing phylogenetic reconstruction of very large gene families and determination of orthologs on a large scale. It is exceptionally robust to adding more gene sequences, opening up the possibility of creating stable identifiers for referring to not only extant genes, but also their common ancestors. We compared trees produced by GIGA to those in the TreeFam database, and they were very similar in general, with most differences likely due to poor alignment quality. However, some remaining differences are algorithmic, and can be explained by the fact that GIGA tends to put a larger emphasis on minimizing gene duplication and deletion events. PMID:20534164

GIGA: a simple, efficient algorithm for gene tree inference in the genomic age.

PubMed

Thomas, Paul D

2010-06-09

Phylogenetic relationships between genes are not only of theoretical interest: they enable us to learn about human genes through the experimental work on their relatives in numerous model organisms from bacteria to fruit flies and mice. Yet the most commonly used computational algorithms for reconstructing gene trees can be inaccurate for numerous reasons, both algorithmic and biological. Additional information beyond gene sequence data has been shown to improve the accuracy of reconstructions, though at great computational cost. We describe a simple, fast algorithm for inferring gene phylogenies, which makes use of information that was not available prior to the genomic age: namely, a reliable species tree spanning much of the tree of life, and knowledge of the complete complement of genes in a species' genome. The algorithm, called GIGA, constructs trees agglomeratively from a distance matrix representation of sequences, using simple rules to incorporate this genomic age information. GIGA makes use of a novel conceptualization of gene trees as being composed of orthologous subtrees (containing only speciation events), which are joined by other evolutionary events such as gene duplication or horizontal gene transfer. An important innovation in GIGA is that, at every step in the agglomeration process, the tree is interpreted/reinterpreted in terms of the evolutionary events that created it. Remarkably, GIGA performs well even when using a very simple distance metric (pairwise sequence differences) and no distance averaging over clades during the tree construction process. GIGA is efficient, allowing phylogenetic reconstruction of very large gene families and determination of orthologs on a large scale. It is exceptionally robust to adding more gene sequences, opening up the possibility of creating stable identifiers for referring to not only extant genes, but also their common ancestors. We compared trees produced by GIGA to those in the TreeFam database, and they were very similar in general, with most differences likely due to poor alignment quality. However, some remaining differences are algorithmic, and can be explained by the fact that GIGA tends to put a larger emphasis on minimizing gene duplication and deletion events.

Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environment.

PubMed

Meng, Bowen; Pratx, Guillem; Xing, Lei

2011-12-01

Four-dimensional CT (4DCT) and cone beam CT (CBCT) are widely used in radiation therapy for accurate tumor target definition and localization. However, high-resolution and dynamic image reconstruction is computationally demanding because of the large amount of data processed. Efficient use of these imaging techniques in the clinic requires high-performance computing. The purpose of this work is to develop a novel ultrafast, scalable and reliable image reconstruction technique for 4D CBCT∕CT using a parallel computing framework called MapReduce. We show the utility of MapReduce for solving large-scale medical physics problems in a cloud computing environment. In this work, we accelerated the Feldcamp-Davis-Kress (FDK) algorithm by porting it to Hadoop, an open-source MapReduce implementation. Gated phases from a 4DCT scans were reconstructed independently. Following the MapReduce formalism, Map functions were used to filter and backproject subsets of projections, and Reduce function to aggregate those partial backprojection into the whole volume. MapReduce automatically parallelized the reconstruction process on a large cluster of computer nodes. As a validation, reconstruction of a digital phantom and an acquired CatPhan 600 phantom was performed on a commercial cloud computing environment using the proposed 4D CBCT∕CT reconstruction algorithm. Speedup of reconstruction time is found to be roughly linear with the number of nodes employed. For instance, greater than 10 times speedup was achieved using 200 nodes for all cases, compared to the same code executed on a single machine. Without modifying the code, faster reconstruction is readily achievable by allocating more nodes in the cloud computing environment. Root mean square error between the images obtained using MapReduce and a single-threaded reference implementation was on the order of 10(-7). Our study also proved that cloud computing with MapReduce is fault tolerant: the reconstruction completed successfully with identical results even when half of the nodes were manually terminated in the middle of the process. An ultrafast, reliable and scalable 4D CBCT∕CT reconstruction method was developed using the MapReduce framework. Unlike other parallel computing approaches, the parallelization and speedup required little modification of the original reconstruction code. MapReduce provides an efficient and fault tolerant means of solving large-scale computing problems in a cloud computing environment.

Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environment

PubMed Central

Meng, Bowen; Pratx, Guillem; Xing, Lei

2011-01-01

Purpose: Four-dimensional CT (4DCT) and cone beam CT (CBCT) are widely used in radiation therapy for accurate tumor target definition and localization. However, high-resolution and dynamic image reconstruction is computationally demanding because of the large amount of data processed. Efficient use of these imaging techniques in the clinic requires high-performance computing. The purpose of this work is to develop a novel ultrafast, scalable and reliable image reconstruction technique for 4D CBCT/CT using a parallel computing framework called MapReduce. We show the utility of MapReduce for solving large-scale medical physics problems in a cloud computing environment. Methods: In this work, we accelerated the Feldcamp–Davis–Kress (FDK) algorithm by porting it to Hadoop, an open-source MapReduce implementation. Gated phases from a 4DCT scans were reconstructed independently. Following the MapReduce formalism, Map functions were used to filter and backproject subsets of projections, and Reduce function to aggregate those partial backprojection into the whole volume. MapReduce automatically parallelized the reconstruction process on a large cluster of computer nodes. As a validation, reconstruction of a digital phantom and an acquired CatPhan 600 phantom was performed on a commercial cloud computing environment using the proposed 4D CBCT/CT reconstruction algorithm. Results: Speedup of reconstruction time is found to be roughly linear with the number of nodes employed. For instance, greater than 10 times speedup was achieved using 200 nodes for all cases, compared to the same code executed on a single machine. Without modifying the code, faster reconstruction is readily achievable by allocating more nodes in the cloud computing environment. Root mean square error between the images obtained using MapReduce and a single-threaded reference implementation was on the order of 10−7. Our study also proved that cloud computing with MapReduce is fault tolerant: the reconstruction completed successfully with identical results even when half of the nodes were manually terminated in the middle of the process. Conclusions: An ultrafast, reliable and scalable 4D CBCT/CT reconstruction method was developed using the MapReduce framework. Unlike other parallel computing approaches, the parallelization and speedup required little modification of the original reconstruction code. MapReduce provides an efficient and fault tolerant means of solving large-scale computing problems in a cloud computing environment. PMID:22149842

An improved algorithm of fiber tractography demonstrates postischemic cerebral reorganization

NASA Astrophysics Data System (ADS)

Liu, Xiao-dong; Lu, Jie; Yao, Li; Li, Kun-cheng; Zhao, Xiao-jie

2008-03-01

In vivo white matter tractography by diffusion tensor imaging (DTI) accurately represents the organizational architecture of white matter in the vicinity of brain lesions and especially ischemic brain. In this study, we suggested an improved fiber tracking algorithm based on TEND, called TENDAS, for tensor deflection with adaptive stepping, which had been introduced a stepping framework for interpreting the algorithm behavior as a function of the tensor shape (linear-shaped or not) and tract history. The propagation direction at each step was given by the deflection vector. TENDAS tractography was used to examine a 17-year-old recovery patient with congenital right hemisphere artery stenosis combining with fMRI. Meaningless picture location was used as spatial working memory task in this study. We detected the shifted functional localization to the contralateral homotypic cortex and more prominent and extensive left-sided parietal and medial frontal cortical activations which were used directly as seed mask for tractography for the reconstruction of individual spatial parietal pathways. Comparing with the TEND algorithms, TENDAS shows smoother and less sharp bending characterization of white matter architecture of the parietal cortex. The results of this preliminary study were twofold. First, TENDAS may provide more adaptability and accuracy in reconstructing certain anatomical features, whereas it is very difficult to verify tractography maps of white matter connectivity in the living human brain. Second, our study indicates that combination of TENDAS and fMRI provide a unique image of functional cortical reorganization and structural modifications of postischemic spatial working memory.

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.

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

Path integration guided with a quality map for shape reconstruction in the fringe reflection technique

NASA Astrophysics Data System (ADS)

Jing, Xiaoli; Cheng, Haobo; Wen, Yongfu

2018-04-01

A new local integration algorithm called quality map path integration (QMPI) is reported for shape reconstruction in the fringe reflection technique. A quality map is proposed to evaluate the quality of gradient data locally, and functions as a guideline for the integrated path. The presented method can be employed in wavefront estimation from its slopes over the general shaped surface with slope noise equivalent to that in practical measurements. Moreover, QMPI is much better at handling the slope data with local noise, which may be caused by the irregular shapes of the surface under test. The performance of QMPI is discussed by simulations and experiment. It is shown that QMPI not only improves the accuracy of local integration, but can also be easily implemented with no iteration compared to Southwell zonal reconstruction (SZR). From an engineering point-of-view, the proposed method may also provide an efficient and stable approach for different shapes with high-precise demand.

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.

Arachne—A web-based event viewer for MINERνA

NASA Astrophysics Data System (ADS)

Tagg, N.; Brangham, J.; Chvojka, J.; Clairemont, M.; Day, M.; Eberly, B.; Felix, J.; Fields, L.; Gago, A. M.; Gran, R.; Harris, D. A.; Kordosky, M.; Lee, H.; Maggi, G.; Maher, E.; Mann, W. A.; Marshall, C. M.; McFarland, K. S.; McGowan, A. M.; Mislivec, A.; Mousseau, J.; Osmanov, B.; Osta, J.; Paolone, V.; Perdue, G.; Ransome, R. D.; Ray, H.; Schellman, H.; Schmitz, D. W.; Simon, C.; Solano Salinas, C. J.; Tice, B. G.; Walding, J.; Walton, T.; Wolcott, J.; Zhang, D.; Ziemer, B. P.; MinerνA Collaboration

2012-06-01

Neutrino interaction events in the MINERνA detector are visually represented with a web-based tool called Arachne. Data are retrieved from a central server via AJAX, and client-side JavaScript draws images into the user's browser window using the draft HTML 5 standard. These technologies allow neutrino interactions to be viewed by anyone with a web browser, allowing for easy hand-scanning of particle interactions. Arachne has been used in MINERνA to evaluate neutrino data in a prototype detector, to tune reconstruction algorithms, and for public outreach and education.

Arachne - A web-based event viewer for MINERvA

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

Tagg, N.; /Otterbein Coll.; Brangham, J.

2011-11-01

Neutrino interaction events in the MINERvA detector are visually represented with a web-based tool called Arachne. Data are retrieved from a central server via AJAX, and client-side JavaScript draws images into the user's browser window using the draft HTML 5 standard. These technologies allow neutrino interactions to be viewed by anyone with a web browser, allowing for easy hand-scanning of particle interactions. Arachne has been used in MINERvA to evaluate neutrino data in a prototype detector, to tune reconstruction algorithms, and for public outreach and education.

Signal to Noise Studies on Thermographic Data with Fabricated Defects for Defense Structures

NASA Technical Reports Server (NTRS)

Zalameda, Joseph N.; Rajic, Nik; Genest, Marc

2006-01-01

There is a growing international interest in thermal inspection systems for asset life assessment and management of defense platforms. The efficacy of flash thermography is generally enhanced by applying image processing algorithms to the observations of raw temperature. Improving the defect signal to noise ratio (SNR) is of primary interest to reduce false calls and allow for easier interpretation of a thermal inspection image. Several factors affecting defect SNR were studied such as data compression and reconstruction using principal component analysis and time window processing.

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.

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.

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.

Towards Real-Time Maneuver Detection: Automatic State and Dynamics Estimation with the Adaptive Optimal Control Based Estimator

NASA Astrophysics Data System (ADS)

Lubey, D.; Scheeres, D.

Tracking objects in Earth orbit is fraught with complications. This is due to the large population of orbiting spacecraft and debris that continues to grow, passive (i.e. no direct communication) and data-sparse observations, and the presence of maneuvers and dynamics mismodeling. Accurate orbit determination in this environment requires an algorithm to capture both a system's state and its state dynamics in order to account for mismodelings. Previous studies by the authors yielded an algorithm called the Optimal Control Based Estimator (OCBE) - an algorithm that simultaneously estimates a system's state and optimal control policies that represent dynamic mismodeling in the system for an arbitrary orbit-observer setup. The stochastic properties of these estimated controls are then used to determine the presence of mismodelings (maneuver detection), as well as characterize and reconstruct the mismodelings. The purpose of this paper is to develop the OCBE into an accurate real-time orbit tracking and maneuver detection algorithm by automating the algorithm and removing its linear assumptions. This results in a nonlinear adaptive estimator. In its original form the OCBE had a parameter called the assumed dynamic uncertainty, which is selected by the user with each new measurement to reflect the level of dynamic mismodeling in the system. This human-in-the-loop approach precludes real-time application to orbit tracking problems due to their complexity. This paper focuses on the Adaptive OCBE, a version of the estimator where the assumed dynamic uncertainty is chosen automatically with each new measurement using maneuver detection results to ensure that state uncertainties are properly adjusted to account for all dynamic mismodelings. The paper also focuses on a nonlinear implementation of the estimator. Originally, the OCBE was derived from a nonlinear cost function then linearized about a nominal trajectory, which is assumed to be ballistic (i.e. the nominal optimal control policy is zero for all times). In this paper, we relax this assumption on the nominal trajectory in order to allow for controlled nominal trajectories. This allows the estimator to be iterated to obtain a more accurate nonlinear solution for both the state and control estimates. Beyond these developments to the estimator, this paper also introduces a modified distance metric for maneuver detection. The original metric used in the OCBE only accounted for the estimated control and its uncertainty. This new metric accounts for measurement deviation and a priori state deviations, such that it accounts for all three major forms of uncertainty in orbit determination. This allows the user to understand the contributions of each source of uncertainty toward the total system mismodeling so that the user can properly account for them. Together these developments create an accurate orbit determination algorithm that is automated, robust to mismodeling, and capable of detecting and reconstructing the presence of mismodeling. These qualities make this algorithm a good foundation from which to approach the problem of real-time maneuver detection and reconstruction for Space Situational Awareness applications. This is further strengthened by the algorithm's general formulation that allows it to be applied to problems with an arbitrary target and observer.

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.

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.

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets.

PubMed

Bicer, Tekin; Gürsoy, Doğa; Andrade, Vincent De; Kettimuthu, Rajkumar; Scullin, William; Carlo, Francesco De; Foster, Ian T

2017-01-01

Modern synchrotron light sources and detectors produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used imaging techniques that generates data at tens of gigabytes per second is computed tomography (CT). Although CT experiments result in rapid data generation, the analysis and reconstruction of the collected data may require hours or even days of computation time with a medium-sized workstation, which hinders the scientific progress that relies on the results of analysis. We present Trace, a data-intensive computing engine that we have developed to enable high-performance implementation of iterative tomographic reconstruction algorithms for parallel computers. Trace provides fine-grained reconstruction of tomography datasets using both (thread-level) shared memory and (process-level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations that we apply to the replicated reconstruction objects and evaluate them using tomography datasets collected at the Advanced Photon Source. Our experimental evaluations show that our optimizations and parallelization techniques can provide 158× speedup using 32 compute nodes (384 cores) over a single-core configuration and decrease the end-to-end processing time of a large sinogram (with 4501 × 1 × 22,400 dimensions) from 12.5 h to <5 min per iteration. The proposed tomographic reconstruction engine can efficiently process large-scale tomographic data using many compute nodes and minimize reconstruction times.

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

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.

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.

3D Lunar Terrain Reconstruction from Apollo Images

NASA Technical Reports Server (NTRS)

Broxton, Michael J.; Nefian, Ara V.; Moratto, Zachary; Kim, Taemin; Lundy, Michael; Segal, Alkeksandr V.

2009-01-01

Generating accurate three dimensional planetary models is becoming increasingly important as NASA plans manned missions to return to the Moon in the next decade. This paper describes a 3D surface reconstruction system called the Ames Stereo Pipeline that is designed to produce such models automatically by processing orbital stereo imagery. We discuss two important core aspects of this system: (1) refinement of satellite station positions and pose estimates through least squares bundle adjustment; and (2) a stochastic plane fitting algorithm that generalizes the Lucas-Kanade method for optimal matching between stereo pair images.. These techniques allow us to automatically produce seamless, highly accurate digital elevation models from multiple stereo image pairs while significantly reducing the influence of image noise. Our technique is demonstrated on a set of 71 high resolution scanned images from the Apollo 15 mission

Some Nonlinear Reconstruction Algorithms for Electrical Impedance Tomography

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

Berryman, J G

2001-03-09

An impedance camera [Henderson and Webster, 1978; Dines and Lytle, 1981]--or what is now more commonly called electrical impedance tomography--attempts to image the electrical impedance (or just the conductivity) distribution inside a body using electrical measurements on its boundary. The method has been used successfully in both biomedical [Brown, 1983; Barber and Brown, 1986; J. C. Newell, D. G. Gisser, and D. Isaacson, 1988; Webster, 1990] and geophysical applications [Wexler, Fry, and Neurnan, 1985; Daily, Lin, and Buscheck, 1987], but the analysis of optimal reconstruction algorithms is still progressing [Murai and Kagawa, 1985; Wexler, Fry, and Neurnan, 1985; Kohn andmore » Vogelius, 1987; Yorkey and Webster, 1987; Yorkey, Webster, and Tompkins, 1987; Berryman and Kohn, 1990; Kohn and McKenney, 1990; Santosa and Vogelius, 1990; Yorkey, 1990]. The most common application is monitoring the influx or efflux of a highly conducting fluid (such as brine in a porous rock or blood in the human body) through the volume being imaged. For biomedical applications, this met hod does not have the resolution of radiological methods, but it is comparatively safe and inexpensive and therefore provides a valuable alternative when continuous monitoring of a patient or process is desired. The following discussion is intended first t o summarize the physics of electrical impedance tomography, then to provide a few details of the data analysis and forward modeling requirements, and finally to outline some of the reconstruction algorithms that have proven to be most useful in practice. Pointers to the literature are provided throughout this brief narrative and the reader is encouraged to explore the references for more complete discussions of the various issues raised here.« less

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

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.

SubspaceEM: A Fast Maximum-a-posteriori Algorithm for Cryo-EM Single Particle Reconstruction

PubMed Central

Dvornek, Nicha C.; Sigworth, Fred J.; Tagare, Hemant D.

2015-01-01

Single particle reconstruction methods based on the maximum-likelihood principle and the expectation-maximization (E–M) algorithm are popular because of their ability to produce high resolution structures. However, these algorithms are computationally very expensive, requiring a network of computational servers. To overcome this computational bottleneck, we propose a new mathematical framework for accelerating maximum-likelihood reconstructions. The speedup is by orders of magnitude and the proposed algorithm produces similar quality reconstructions compared to the standard maximum-likelihood formulation. Our approach uses subspace approximations of the cryo-electron microscopy (cryo-EM) data and projection images, greatly reducing the number of image transformations and comparisons that are computed. Experiments using simulated and actual cryo-EM data show that speedup in overall execution time compared to traditional maximum-likelihood reconstruction reaches factors of over 300. PMID:25839831

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

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

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.

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.

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.

Feasibility study for application of the compressed-sensing framework to interior computed tomography (ICT) for low-dose, high-accurate dental x-ray imaging

NASA Astrophysics Data System (ADS)

Je, U. K.; Cho, H. M.; Cho, H. S.; Park, Y. O.; Park, C. K.; Lim, H. W.; Kim, K. S.; Kim, G. A.; Park, S. Y.; Woo, T. H.; Choi, S. I.

2016-02-01

In this paper, we propose a new/next-generation type of CT examinations, the so-called Interior Computed Tomography (ICT), which may presumably lead to dose reduction to the patient outside the target region-of-interest (ROI), in dental x-ray imaging. Here an x-ray beam from each projection position covers only a relatively small ROI containing a target of diagnosis from the examined structure, leading to imaging benefits such as decreasing scatters and system cost as well as reducing imaging dose. We considered the compressed-sensing (CS) framework, rather than common filtered-backprojection (FBP)-based algorithms, for more accurate ICT reconstruction. We implemented a CS-based ICT algorithm and performed a systematic simulation to investigate the imaging characteristics. Simulation conditions of two ROI ratios of 0.28 and 0.14 between the target and the whole phantom sizes and four projection numbers of 360, 180, 90, and 45 were tested. We successfully reconstructed ICT images of substantially high image quality by using the CS framework even with few-view projection data, still preserving sharp edges in the images.

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.

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

optGpSampler: an improved tool for uniformly sampling the solution-space of genome-scale metabolic networks.

PubMed

Megchelenbrink, Wout; Huynen, Martijn; Marchiori, Elena

2014-01-01

Constraint-based models of metabolic networks are typically underdetermined, because they contain more reactions than metabolites. Therefore the solutions to this system do not consist of unique flux rates for each reaction, but rather a space of possible flux rates. By uniformly sampling this space, an estimated probability distribution for each reaction's flux in the network can be obtained. However, sampling a high dimensional network is time-consuming. Furthermore, the constraints imposed on the network give rise to an irregularly shaped solution space. Therefore more tailored, efficient sampling methods are needed. We propose an efficient sampling algorithm (called optGpSampler), which implements the Artificial Centering Hit-and-Run algorithm in a different manner than the sampling algorithm implemented in the COBRA Toolbox for metabolic network analysis, here called gpSampler. Results of extensive experiments on different genome-scale metabolic networks show that optGpSampler is up to 40 times faster than gpSampler. Application of existing convergence diagnostics on small network reconstructions indicate that optGpSampler converges roughly ten times faster than gpSampler towards similar sampling distributions. For networks of higher dimension (i.e. containing more than 500 reactions), we observed significantly better convergence of optGpSampler and a large deviation between the samples generated by the two algorithms. optGpSampler for Matlab and Python is available for non-commercial use at: http://cs.ru.nl/~wmegchel/optGpSampler/.

Regularized reconstruction of absorbing and phase objects from a single in-line hologram, application to fluid mechanics and micro-biology.

PubMed

Jolivet, Frédéric; Momey, Fabien; Denis, Loïc; Méès, Loïc; Faure, Nicolas; Grosjean, Nathalie; Pinston, Frédéric; Marié, Jean-Louis; Fournier, Corinne

2018-04-02

Reconstruction of phase objects is a central problem in digital holography, whose various applications include microscopy, biomedical imaging, and fluid mechanics. Starting from a single in-line hologram, there is no direct way to recover the phase of the diffracted wave in the hologram plane. The reconstruction of absorbing and phase objects therefore requires the inversion of the non-linear hologram formation model. We propose a regularized reconstruction method that includes several physically-grounded constraints such as bounds on transmittance values, maximum/minimum phase, spatial smoothness or the absence of any object in parts of the field of view. To solve the non-convex and non-smooth optimization problem induced by our modeling, a variable splitting strategy is applied and the closed-form solution of the sub-problem (the so-called proximal operator) is derived. The resulting algorithm is efficient and is shown to lead to quantitative phase estimation on reconstructions of accurate simulations of in-line holograms based on the Mie theory. As our approach is adaptable to several in-line digital holography configurations, we present and discuss the promising results of reconstructions from experimental in-line holograms obtained in two different applications: the tracking of an evaporating droplet (size ∼ 100μm) and the microscopic imaging of bacteria (size ∼ 1μm).

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.

Feature detection on 3D images of dental imprints

NASA Astrophysics Data System (ADS)

Mokhtari, Marielle; Laurendeau, Denis

1994-09-01

A computer vision approach for the extraction of feature points on 3D images of dental imprints is presented. The position of feature points are needed for the measurement of a set of parameters for automatic diagnosis of malocclusion problems in orthodontics. The system for the acquisition of the 3D profile of the imprint, the procedure for the detection of the interstices between teeth, and the approach for the identification of the type of tooth are described, as well as the algorithm for the reconstruction of the surface of each type of tooth. A new approach for the detection of feature points, called the watershed algorithm, is described in detail. The algorithm is a two-stage procedure which tracks the position of local minima at four different scales and produces a final map of the position of the minima. Experimental results of the application of the watershed algorithm on actual 3D images of dental imprints are presented for molars, premolars and canines. The segmentation approach for the analysis of the shape of incisors is also described in detail.

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.

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.

Preconditioned Alternating Projection Algorithms for Maximum a Posteriori ECT Reconstruction

PubMed Central

Krol, Andrzej; Li, Si; Shen, Lixin; Xu, Yuesheng

2012-01-01

We propose a preconditioned alternating projection algorithm (PAPA) for solving the maximum a posteriori (MAP) emission computed tomography (ECT) reconstruction problem. Specifically, we formulate the reconstruction problem as a constrained convex optimization problem with the total variation (TV) regularization. We then characterize the solution of the constrained convex optimization problem and show that it satisfies a system of fixed-point equations defined in terms of two proximity operators raised from the convex functions that define the TV-norm and the constrain involved in the problem. The characterization (of the solution) via the proximity operators that define two projection operators naturally leads to an alternating projection algorithm for finding the solution. For efficient numerical computation, we introduce to the alternating projection algorithm a preconditioning matrix (the EM-preconditioner) for the dense system matrix involved in the optimization problem. We prove theoretically convergence of the preconditioned alternating projection algorithm. In numerical experiments, performance of our algorithms, with an appropriately selected preconditioning matrix, is compared with performance of the conventional MAP expectation-maximization (MAP-EM) algorithm with TV regularizer (EM-TV) and that of the recently developed nested EM-TV algorithm for ECT reconstruction. Based on the numerical experiments performed in this work, we observe that the alternating projection algorithm with the EM-preconditioner outperforms significantly the EM-TV in all aspects including the convergence speed, the noise in the reconstructed images and the image quality. It also outperforms the nested EM-TV in the convergence speed while providing comparable image quality. PMID:23271835

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.

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)

The infection algorithm: an artificial epidemic approach for dense stereo correspondence.

PubMed

Olague, Gustavo; Fernández, Francisco; Pérez, Cynthia B; Lutton, Evelyne

2006-01-01

We present a new bio-inspired approach applied to a problem of stereo image matching. This approach is based on an artificial epidemic process, which we call the infection algorithm. The problem at hand is a basic one in computer vision for 3D scene reconstruction. It has many complex aspects and is known as an extremely difficult one. The aim is to match the contents of two images in order to obtain 3D information that allows the generation of simulated projections from a viewpoint that is different from the ones of the initial photographs. This process is known as view synthesis. The algorithm we propose exploits the image contents in order to produce only the necessary 3D depth information, while saving computational time. It is based on a set of distributed rules, which propagate like an artificial epidemic over the images. Experiments on a pair of real images are presented, and realistic reprojected images have been generated.

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

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

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.

Simulation of a fast diffuse optical tomography system based on radiative transfer equation

NASA Astrophysics Data System (ADS)

Motevalli, S. M.; Payani, A.

2016-12-01

Studies show that near-infrared (NIR) light (light with wavelength between 700nm and 1300nm) undergoes two interactions, absorption and scattering, when it penetrates a tissue. Since scattering is the predominant interaction, the calculation of light distribution in the tissue and the image reconstruction of absorption and scattering coefficients are very complicated. Some analytical and numerical methods, such as radiative transport equation and Monte Carlo method, have been used for the simulation of light penetration in tissue. Recently, some investigators in the world have tried to develop a diffuse optical tomography system. In these systems, NIR light penetrates the tissue and passes through the tissue. Then, light exiting the tissue is measured by NIR detectors placed around the tissue. These data are collected from all the detectors and transferred to the computational parts (including hardware and software), which make a cross-sectional image of the tissue after performing some computational processes. In this paper, the results of the simulation of an optical diffuse tomography system are presented. This simulation involves two stages: a) Simulation of the forward problem (or light penetration in the tissue), which is performed by solving the diffusion approximation equation in the stationary state using FEM. b) Simulation of the inverse problem (or image reconstruction), which is performed by the optimization algorithm called Broyden quasi-Newton. This method of image reconstruction is faster compared to the other Newton-based optimization algorithms, such as the Levenberg-Marquardt one.

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.

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.

Energy Reconstruction for Events Detected in TES X-ray Detectors

NASA Astrophysics Data System (ADS)

Ceballos, M. T.; Cardiel, N.; Cobo, B.

2015-09-01

The processing of the X-ray events detected by a TES (Transition Edge Sensor) device (such as the one that will be proposed in the ESA AO call for instruments for the Athena mission (Nandra et al. 2013) as a high spectral resolution instrument, X-IFU (Barret et al. 2013)), is a several step procedure that starts with the detection of the current pulses in a noisy signal and ends up with their energy reconstruction. For this last stage, an energy calibration process is required to convert the pseudo energies measured in the detector to the real energies of the incoming photons, accounting for possible nonlinearity effects in the detector. We present the details of the energy calibration algorithm we implemented as the last part of the Event Processing software that we are developing for the X-IFU instrument, that permits the calculation of the calibration constants in an analytical way.

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

A nudging-based data assimilation method: the Back and Forth Nudging (BFN) algorithm

NASA Astrophysics Data System (ADS)

Auroux, D.; Blum, J.

2008-03-01

This paper deals with a new data assimilation algorithm, called Back and Forth Nudging. The standard nudging technique consists in adding to the equations of the model a relaxation term that is supposed to force the observations to the model. The BFN algorithm consists in repeatedly performing forward and backward integrations of the model with relaxation (or nudging) terms, using opposite signs in the direct and inverse integrations, so as to make the backward evolution numerically stable. This algorithm has first been tested on the standard Lorenz model with discrete observations (perfect or noisy) and compared with the variational assimilation method. The same type of study has then been performed on the viscous Burgers equation, comparing again with the variational method and focusing on the time evolution of the reconstruction error, i.e. the difference between the reference trajectory and the identified one over a time period composed of an assimilation period followed by a prediction period. The possible use of the BFN algorithm as an initialization for the variational method has also been investigated. Finally the algorithm has been tested on a layered quasi-geostrophic model with sea-surface height observations. The behaviours of the two algorithms have been compared in the presence of perfect or noisy observations, and also for imperfect models. This has allowed us to reach a conclusion concerning the relative performances of the two algorithms.

Cardiac-gated parametric images from 82 Rb PET from dynamic frames and direct 4D reconstruction.

PubMed

Germino, Mary; Carson, Richard E

2018-02-01

Cardiac perfusion PET data can be reconstructed as a dynamic sequence and kinetic modeling performed to quantify myocardial blood flow, or reconstructed as static gated images to quantify function. Parametric images from dynamic PET are conventionally not gated, to allow use of all events with lower noise. An alternative method for dynamic PET is to incorporate the kinetic model into the reconstruction algorithm itself, bypassing the generation of a time series of emission images and directly producing parametric images. So-called "direct reconstruction" can produce parametric images with lower noise than the conventional method because the noise distribution is more easily modeled in projection space than in image space. In this work, we develop direct reconstruction of cardiac-gated parametric images for 82 Rb PET with an extension of the Parametric Motion compensation OSEM List mode Algorithm for Resolution-recovery reconstruction for the one tissue model (PMOLAR-1T). PMOLAR-1T was extended to accommodate model terms to account for spillover from the left and right ventricles into the myocardium. The algorithm was evaluated on a 4D simulated 82 Rb dataset, including a perfusion defect, as well as a human 82 Rb list mode acquisition. The simulated list mode was subsampled into replicates, each with counts comparable to one gate of a gated acquisition. Parametric images were produced by the indirect (separate reconstructions and modeling) and direct methods for each of eight low-count and eight normal-count replicates of the simulated data, and each of eight cardiac gates for the human data. For the direct method, two initialization schemes were tested: uniform initialization, and initialization with the filtered iteration 1 result of the indirect method. For the human dataset, event-by-event respiratory motion compensation was included. The indirect and direct methods were compared for the simulated dataset in terms of bias and coefficient of variation as a function of iteration. Convergence of direct reconstruction was slow with uniform initialization; lower bias was achieved in fewer iterations by initializing with the filtered indirect iteration 1 images. For most parameters and regions evaluated, the direct method achieved the same or lower absolute bias at matched iteration as the indirect method, with 23%-65% lower noise. Additionally, the direct method gave better contrast between the perfusion defect and surrounding normal tissue than the indirect method. Gated parametric images from the human dataset had comparable relative performance of indirect and direct, in terms of mean parameter values per iteration. Changes in myocardial wall thickness and blood pool size across gates were readily visible in the gated parametric images, with higher contrast between myocardium and left ventricle blood pool in parametric images than gated SUV images. Direct reconstruction can produce parametric images with less noise than the indirect method, opening the potential utility of gated parametric imaging for perfusion PET. © 2017 American Association of Physicists in Medicine.

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.

Angular dependence of multiangle dynamic light scattering for particle size distribution inversion using a self-adapting regularization algorithm

NASA Astrophysics Data System (ADS)

Li, Lei; Yu, Long; Yang, Kecheng; Li, Wei; Li, Kai; Xia, Min

2018-04-01

The multiangle dynamic light scattering (MDLS) technique can better estimate particle size distributions (PSDs) than single-angle dynamic light scattering. However, determining the inversion range, angular weighting coefficients, and scattering angle combination is difficult but fundamental to the reconstruction for both unimodal and multimodal distributions. In this paper, we propose a self-adapting regularization method called the wavelet iterative recursion nonnegative Tikhonov-Phillips-Twomey (WIRNNT-PT) algorithm. This algorithm combines a wavelet multiscale strategy with an appropriate inversion method and could self-adaptively optimize several noteworthy issues containing the choices of the weighting coefficients, the inversion range and the optimal inversion method from two regularization algorithms for estimating the PSD from MDLS measurements. In addition, the angular dependence of the MDLS for estimating the PSDs of polymeric latexes is thoroughly analyzed. The dependence of the results on the number and range of measurement angles was analyzed in depth to identify the optimal scattering angle combination. Numerical simulations and experimental results for unimodal and multimodal distributions are presented to demonstrate both the validity of the WIRNNT-PT algorithm and the angular dependence of MDLS and show that the proposed algorithm with a six-angle analysis in the 30-130° range can be satisfactorily applied to retrieve PSDs from MDLS measurements.

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.

Preconditioned alternating projection algorithms for maximum a posteriori ECT reconstruction

NASA Astrophysics Data System (ADS)

Krol, Andrzej; Li, Si; Shen, Lixin; Xu, Yuesheng

2012-11-01

We propose a preconditioned alternating projection algorithm (PAPA) for solving the maximum a posteriori (MAP) emission computed tomography (ECT) reconstruction problem. Specifically, we formulate the reconstruction problem as a constrained convex optimization problem with the total variation (TV) regularization. We then characterize the solution of the constrained convex optimization problem and show that it satisfies a system of fixed-point equations defined in terms of two proximity operators raised from the convex functions that define the TV-norm and the constraint involved in the problem. The characterization (of the solution) via the proximity operators that define two projection operators naturally leads to an alternating projection algorithm for finding the solution. For efficient numerical computation, we introduce to the alternating projection algorithm a preconditioning matrix (the EM-preconditioner) for the dense system matrix involved in the optimization problem. We prove theoretically convergence of the PAPA. In numerical experiments, performance of our algorithms, with an appropriately selected preconditioning matrix, is compared with performance of the conventional MAP expectation-maximization (MAP-EM) algorithm with TV regularizer (EM-TV) and that of the recently developed nested EM-TV algorithm for ECT reconstruction. Based on the numerical experiments performed in this work, we observe that the alternating projection algorithm with the EM-preconditioner outperforms significantly the EM-TV in all aspects including the convergence speed, the noise in the reconstructed images and the image quality. It also outperforms the nested EM-TV in the convergence speed while providing comparable image quality.

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.

A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor

PubMed Central

Salehizadeh, Seyed M. A.; Dao, Duy; Bolkhovsky, Jeffrey; Cho, Chae; Mendelson, Yitzhak; Chon, Ki H.

2015-01-01

Accurate estimation of heart rates from photoplethysmogram (PPG) signals during intense physical activity is a very challenging problem. This is because strenuous and high intensity exercise can result in severe motion artifacts in PPG signals, making accurate heart rate (HR) estimation difficult. In this study we investigated a novel technique to accurately reconstruct motion-corrupted PPG signals and HR based on time-varying spectral analysis. The algorithm is called Spectral filter algorithm for Motion Artifacts and heart rate reconstruction (SpaMA). The idea is to calculate the power spectral density of both PPG and accelerometer signals for each time shift of a windowed data segment. By comparing time-varying spectra of PPG and accelerometer data, those frequency peaks resulting from motion artifacts can be distinguished from the PPG spectrum. The SpaMA approach was applied to three different datasets and four types of activities: (1) training datasets from the 2015 IEEE Signal Process. Cup Database recorded from 12 subjects while performing treadmill exercise from 1 km/h to 15 km/h; (2) test datasets from the 2015 IEEE Signal Process. Cup Database recorded from 11 subjects while performing forearm and upper arm exercise. (3) Chon Lab dataset including 10 min recordings from 10 subjects during treadmill exercise. The ECG signals from all three datasets provided the reference HRs which were used to determine the accuracy of our SpaMA algorithm. The performance of the SpaMA approach was calculated by computing the mean absolute error between the estimated HR from the PPG and the reference HR from the ECG. The average estimation errors using our method on the first, second and third datasets are 0.89, 1.93 and 1.38 beats/min respectively, while the overall error on all 33 subjects is 1.86 beats/min and the performance on only treadmill experiment datasets (22 subjects) is 1.11 beats/min. Moreover, it was found that dynamics of heart rate variability can be accurately captured using the algorithm where the mean Pearson’s correlation coefficient between the power spectral densities of the reference and the reconstructed heart rate time series was found to be 0.98. These results show that the SpaMA method has a potential for PPG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities. PMID:26703618

A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor.

PubMed

Salehizadeh, Seyed M A; Dao, Duy; Bolkhovsky, Jeffrey; Cho, Chae; Mendelson, Yitzhak; Chon, Ki H

2015-12-23

Accurate estimation of heart rates from photoplethysmogram (PPG) signals during intense physical activity is a very challenging problem. This is because strenuous and high intensity exercise can result in severe motion artifacts in PPG signals, making accurate heart rate (HR) estimation difficult. In this study we investigated a novel technique to accurately reconstruct motion-corrupted PPG signals and HR based on time-varying spectral analysis. The algorithm is called Spectral filter algorithm for Motion Artifacts and heart rate reconstruction (SpaMA). The idea is to calculate the power spectral density of both PPG and accelerometer signals for each time shift of a windowed data segment. By comparing time-varying spectra of PPG and accelerometer data, those frequency peaks resulting from motion artifacts can be distinguished from the PPG spectrum. The SpaMA approach was applied to three different datasets and four types of activities: (1) training datasets from the 2015 IEEE Signal Process. Cup Database recorded from 12 subjects while performing treadmill exercise from 1 km/h to 15 km/h; (2) test datasets from the 2015 IEEE Signal Process. Cup Database recorded from 11 subjects while performing forearm and upper arm exercise. (3) Chon Lab dataset including 10 min recordings from 10 subjects during treadmill exercise. The ECG signals from all three datasets provided the reference HRs which were used to determine the accuracy of our SpaMA algorithm. The performance of the SpaMA approach was calculated by computing the mean absolute error between the estimated HR from the PPG and the reference HR from the ECG. The average estimation errors using our method on the first, second and third datasets are 0.89, 1.93 and 1.38 beats/min respectively, while the overall error on all 33 subjects is 1.86 beats/min and the performance on only treadmill experiment datasets (22 subjects) is 1.11 beats/min. Moreover, it was found that dynamics of heart rate variability can be accurately captured using the algorithm where the mean Pearson's correlation coefficient between the power spectral densities of the reference and the reconstructed heart rate time series was found to be 0.98. These results show that the SpaMA method has a potential for PPG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities.

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

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.

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.

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.

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.

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.

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.

High resolution human diffusion tensor imaging using 2-D navigated multi-shot SENSE EPI at 7 Tesla

PubMed Central

Jeong, Ha-Kyu; Gore, John C.; Anderson, Adam W.

2012-01-01

The combination of parallel imaging with partial Fourier acquisition has greatly improved the performance of diffusion-weighted single-shot EPI and is the preferred method for acquisitions at low to medium magnetic field strength such as 1.5 or 3 Tesla. Increased off-resonance effects and reduced transverse relaxation times at 7 Tesla, however, generate more significant artifacts than at lower magnetic field strength and limit data acquisition. Additional acceleration of k-space traversal using a multi-shot approach, which acquires a subset of k-space data after each excitation, reduces these artifacts relative to conventional single-shot acquisitions. However, corrections for motion-induced phase errors are not straightforward in accelerated, diffusion-weighted multi-shot EPI because of phase aliasing. In this study, we introduce a simple acquisition and corresponding reconstruction method for diffusion-weighted multi-shot EPI with parallel imaging suitable for use at high field. The reconstruction uses a simple modification of the standard SENSE algorithm to account for shot-to-shot phase errors; the method is called Image Reconstruction using Image-space Sampling functions (IRIS). Using this approach, reconstruction from highly aliased in vivo image data using 2-D navigator phase information is demonstrated for human diffusion-weighted imaging studies at 7 Tesla. The final reconstructed images show submillimeter in-plane resolution with no ghosts and much reduced blurring and off-resonance artifacts. PMID:22592941

Dual-resolution image reconstruction for region-of-interest CT scan

NASA Astrophysics Data System (ADS)

Jin, S. O.; Shin, K. Y.; Yoo, S. K.; Kim, J. G.; Kim, K. H.; Huh, Y.; Lee, S. Y.; Kwon, O.-K.

2014-07-01

In ordinary CT scan, so called full field-of-view (FFOV) scan, in which the x-ray beam span covers the whole section of the body, a large number of projections are necessary to reconstruct high resolution images. However, excessive x-ray dose is a great concern in FFOV scan. Region-of-interest (ROI) scan is a method to visualize the ROI in high resolution while reducing the x-ray dose. But, ROI scan suffers from bright-band artifacts which may hamper CT-number accuracy. In this study, we propose an image reconstruction method to eliminate the band artifacts in the ROI scan. In addition to the ROI scan with high sampling rate in the view direction, we get FFOV projection data with much lower sampling rate. Then, we reconstruct images in the compressed sensing (CS) framework with dual resolutions, that is, high resolution in the ROI and low resolution outside the ROI. For the dual-resolution image reconstruction, we implemented the dual-CS reconstruction algorithm in which data fidelity and total variation (TV) terms were enforced twice in the framework of adaptive steepest descent projection onto convex sets (ASD-POCS). The proposed method has remarkably reduced the bright-band artifacts at around the ROI boundary, and it has also effectively suppressed the streak artifacts over the entire image. We expect the proposed method can be greatly used for dual-resolution imaging with reducing the radiation dose, artifacts and scan time.

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.

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.

How nonlinear optics can merge interferometry for high resolution imaging

NASA Astrophysics Data System (ADS)

Ceus, D.; Reynaud, F.; Tonello, A.; Delage, L.; Grossard, L.

2017-11-01

High resolution stellar interferometers are very powerful efficient instruments to get a better knowledge of our Universe through the spatial coherence analysis of the light. For this purpose, the optical fields collected by each telescope Ti are mixed together. From the interferometric pattern, two expected information called the contrast Cij and the phase information φij are extracted. These information lead to the Vij, called the complex visibility, with Vij=Cijexp(jφij). For each telescope doublet TiTj, it is possible to get a complex visibility Vij. The Zernike Van Cittert theorem gives a relationship between the intensity distribution of the object observed and the complex visibility. The combination of the acquired complex visibilities and a reconstruction algorithm allows imaging reconstruction. To avoid lots of technical difficulties related to infrared optics (components transmission, thermal noises, thermal cooling…), our team proposes to explore the possibility of using nonlinear optical techniques. This is a promising alternative detection technique for detecting infrared optical signals. This way, we experimentally demonstrate that frequency conversion does not result in additional bias on the interferometric data supplied by a stellar interferometer. In this presentation, we report on wavelength conversion of the light collected by each telescope from the infrared domain to the visible. The interferometric pattern is observed in the visible domain with our, so called, upconversion interferometer. Thereby, one can benefit from mature optical components mainly used in optical telecommunications (waveguide, coupler, multiplexer…) and efficient low-noise detection schemes up to the single-photon counting level.

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.

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.

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.

Acceleration of image-based resolution modelling reconstruction using an expectation maximization nested algorithm.

PubMed

Angelis, G I; Reader, A J; Markiewicz, P J; Kotasidis, F A; Lionheart, W R; Matthews, J C

2013-08-07

Recent studies have demonstrated the benefits of a resolution model within iterative reconstruction algorithms in an attempt to account for effects that degrade the spatial resolution of the reconstructed images. However, these algorithms suffer from slower convergence rates, compared to algorithms where no resolution model is used, due to the additional need to solve an image deconvolution problem. In this paper, a recently proposed algorithm, which decouples the tomographic and image deconvolution problems within an image-based expectation maximization (EM) framework, was evaluated. This separation is convenient, because more computational effort can be placed on the image deconvolution problem and therefore accelerate convergence. Since the computational cost of solving the image deconvolution problem is relatively small, multiple image-based EM iterations do not significantly increase the overall reconstruction time. The proposed algorithm was evaluated using 2D simulations, as well as measured 3D data acquired on the high-resolution research tomograph. Results showed that bias reduction can be accelerated by interleaving multiple iterations of the image-based EM algorithm solving the resolution model problem, with a single EM iteration solving the tomographic problem. Significant improvements were observed particularly for voxels that were located on the boundaries between regions of high contrast within the object being imaged and for small regions of interest, where resolution recovery is usually more challenging. Minor differences were observed using the proposed nested algorithm, compared to the single iteration normally performed, when an optimal number of iterations are performed for each algorithm. However, using the proposed nested approach convergence is significantly accelerated enabling reconstruction using far fewer tomographic iterations (up to 70% fewer iterations for small regions). Nevertheless, the optimal number of nested image-based EM iterations is hard to be defined and it should be selected according to the given application.

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.

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.

Testing light-traces-mass in Hubble Frontier Fields Cluster MACS-J0416.1-2403

DOE PAGES

Sebesta, Kevin; Williams, Liliya L. R.; Mohammed, Irshad; ...

2016-06-17

Here, we reconstruct the projected mass distribution of a massive merging Hubble Frontier Fields cluster MACSJ0416 using the genetic algorithm based free-form technique called Grale. The reconstructions are constrained by 149 lensed images identified by Jauzac et al. using HFF data. No information about cluster galaxies or light is used, which makes our reconstruction unique in this regard. Using visual inspection of the maps, as well as galaxy-mass correlation functions we conclude that overall light does follow mass. Furthermore, the fact that brighter galaxies are more strongly clustered with mass is an important confirmation of the standard biasing scenario inmore » galaxy clusters. On the smallest scales, approximately less than a few arcseconds, the resolution afforded by 149 images is still not sufficient to confirm or rule out galaxy-mass offsets of the kind observed in ACO 3827. We also compare the mass maps of MACSJ0416 obtained by three different groups: Grale, and two parametric Lenstool reconstructions from the CATS and Sharon/Johnson teams. Overall, the three agree well; one interesting discrepancy between Grale and Lenstool galaxy-mass correlation functions occurs on scales of tens of kpc and may suggest that cluster galaxies are more biased tracers of mass than parametric methods generally assume.« less

Testing light-traces-mass in Hubble Frontier Fields Cluster MACS-J0416.1-2403

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

Sebesta, Kevin; Williams, Liliya L. R.; Mohammed, Irshad

Here, we reconstruct the projected mass distribution of a massive merging Hubble Frontier Fields cluster MACSJ0416 using the genetic algorithm based free-form technique called Grale. The reconstructions are constrained by 149 lensed images identified by Jauzac et al. using HFF data. No information about cluster galaxies or light is used, which makes our reconstruction unique in this regard. Using visual inspection of the maps, as well as galaxy-mass correlation functions we conclude that overall light does follow mass. Furthermore, the fact that brighter galaxies are more strongly clustered with mass is an important confirmation of the standard biasing scenario inmore » galaxy clusters. On the smallest scales, approximately less than a few arcseconds, the resolution afforded by 149 images is still not sufficient to confirm or rule out galaxy-mass offsets of the kind observed in ACO 3827. We also compare the mass maps of MACSJ0416 obtained by three different groups: Grale, and two parametric Lenstool reconstructions from the CATS and Sharon/Johnson teams. Overall, the three agree well; one interesting discrepancy between Grale and Lenstool galaxy-mass correlation functions occurs on scales of tens of kpc and may suggest that cluster galaxies are more biased tracers of mass than parametric methods generally assume.« less

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.

TeachEnG: a Teaching Engine for Genomics.

PubMed

Kim, Minji; Kim, Yeonsung; Qian, Lei; Song, Jun S

2017-10-15

Bioinformatics is a rapidly growing field that has emerged from the synergy of computer science, statistics and biology. Given the interdisciplinary nature of bioinformatics, many students from diverse fields struggle with grasping bioinformatic concepts only from classroom lectures. Interactive tools for helping students reinforce their learning would be thus desirable. Here, we present an interactive online educational tool called TeachEnG (acronym for Teaching Engine for Genomics) for reinforcing key concepts in sequence alignment and phylogenetic tree reconstruction. Our instructional games allow students to align sequences by hand, fill out the dynamic programming matrix in the Needleman-Wunsch global sequence alignment algorithm, and reconstruct phylogenetic trees via the maximum parsimony, Unweighted Pair Group Method with Arithmetic mean (UPGMA) and Neighbor-Joining algorithms. With an easily accessible interface and instant visual feedback, TeachEnG will help promote active learning in bioinformatics. TeachEnG is freely available at http://teacheng.illinois.edu. The source code is available from https://github.com/KnowEnG/TeachEnG under the Artistic License 2.0. It is written in JavaScript and compatible with Firefox, Safari, Chrome and Microsoft Edge. songj@illinois.edu. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

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.

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.

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

Optimizing 4DCBCT projection allocation to respiratory bins.

PubMed

O'Brien, Ricky T; Kipritidis, John; Shieh, Chun-Chien; Keall, Paul J

2014-10-07

4D cone beam computed tomography (4DCBCT) is an emerging image guidance strategy used in radiotherapy where projections acquired during a scan are sorted into respiratory bins based on the respiratory phase or displacement. 4DCBCT reduces the motion blur caused by respiratory motion but increases streaking artefacts due to projection under-sampling as a result of the irregular nature of patient breathing and the binning algorithms used. For displacement binning the streak artefacts are so severe that displacement binning is rarely used clinically. The purpose of this study is to investigate if sharing projections between respiratory bins and adjusting the location of respiratory bins in an optimal manner can reduce or eliminate streak artefacts in 4DCBCT images. We introduce a mathematical optimization framework and a heuristic solution method, which we will call the optimized projection allocation algorithm, to determine where to position the respiratory bins and which projections to source from neighbouring respiratory bins. Five 4DCBCT datasets from three patients were used to reconstruct 4DCBCT images. Projections were sorted into respiratory bins using equispaced, equal density and optimized projection allocation. The standard deviation of the angular separation between projections was used to assess streaking and the consistency of the segmented volume of a fiducial gold marker was used to assess motion blur. The standard deviation of the angular separation between projections using displacement binning and optimized projection allocation was 30%-50% smaller than conventional phase based binning and 59%-76% smaller than conventional displacement binning indicating more uniformly spaced projections and fewer streaking artefacts. The standard deviation in the marker volume was 20%-90% smaller when using optimized projection allocation than using conventional phase based binning suggesting more uniform marker segmentation and less motion blur. Images reconstructed using displacement binning and the optimized projection allocation algorithm were clearer, contained visibly fewer streak artefacts and produced more consistent marker segmentation than those reconstructed with either equispaced or equal-density binning. The optimized projection allocation algorithm significantly improves image quality in 4DCBCT images and provides, for the first time, a method to consistently generate high quality displacement binned 4DCBCT images in clinical applications.

Ultrahigh-resolution CT and DR scanner

NASA Astrophysics Data System (ADS)

DiBianca, Frank A.; Gupta, Vivek; Zou, Ping; Jordan, Lawrence M.; Laughter, Joseph S.; Zeman, Herbert D.; Sebes, Jeno I.

1999-05-01

A new technique called Variable-Resolution X-ray (VRX) detection that dramatically increases the spatial resolution in computed tomography (CT) and digital radiography (DR) is presented. The technique is based on a principle called 'projective compression' that allows the resolution element of a CT detector to scale with the subject or field size. For very large (40 - 50 cm) field sizes, resolution exceeding 2 cy/mm is possible and for very small fields, microscopy is attainable with resolution exceeding 100 cy/mm. Several effects that could limit the performance of VRX detectors are considered. Experimental measurements on a 16-channel, CdWO4 scintillator + photodiode test array yield a limiting MTF of 64 cy/mm (8(mu) ) in the highest-resolution configuration reported. Preliminary CT images have been made of small anatomical specimens and small animals using a storage phosphor screen in the VRX mode. Measured detector resolution of the CT projection data exceeds 20 cy/mm (less than 25 (mu) ); however, the final, reconstructed CT images produced thus far exhibit 10 cy/mm (50 (mu) ) resolution because of non-flatness of the storage phosphor plates, focal spot effects and the use of a rudimentary CT reconstruction algorithm. A 576-channel solid-state detector is being fabricated that is expected to achieve CT image resolution in excess of that of the 26-channel test array.

Phase retrieval for crystalline specimens

NASA Astrophysics Data System (ADS)

Arnal, Romain A.; Millane, Rick P.

2017-09-01

The recent availability of ultra-bright and ultra-short X-rays pulses from new sources called x-ray free-electron lasers (XFELs) has introduced a new paradigm in X-ray crystallography. Called "diffraction-before-destruction," this paradigm addresses the main problems that plague crystallography using synchrotron sources. However, the phase problem of coherent diffraction imaging remains: one has to retrieve the phase of the measured diffraction amplitude in order to reconstruct the object. Fibrous and membrane proteins that crystallize in 1D and 2D crystals can now potentially be used for data collection with free-electron lasers. The crystallographic phase problem with such crystalline specimens is eased as the Fourier amplitude can be sampled more finely than at the Bragg sampling along one or two directions. Here we characterise uniqueness of the phase problem for different types of crystalline specimen. Simulated ab initio phase retrieval using iterative projection algorithms for 2D crystals is presented.

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.

Evaluation of corrective reconstruction methods using a 3D cardiac-torso phantom and bull's-eye plots

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

Zhao, X.D.; Tsui, B.M.W.; Gregoriou, G.K.

The goal of the investigation was to study the effectiveness of the corrective reconstruction methods in cardiac SPECT using a realistic phantom and to qualitatively and quantitatively evaluate the reconstructed images using bull's-eye plots. A 3D mathematical phantom which realistically models the anatomical structures of the cardiac-torso region of patients was used. The phantom allows simulation of both the attenuation distribution and the uptake of radiopharmaceuticals in different organs. Also, the phantom can be easily modified to simulate different genders and variations in patient anatomy. Two-dimensional projection data were generated from the phantom and included the effects of attenuation andmore » detector response blurring. The reconstruction methods used in the study included the conventional filtered backprojection (FBP) with no attenuation compensation, and the first-order Chang algorithm, an iterative filtered backprojection algorithm (IFBP), the weighted least square conjugate gradient algorithm and the ML-EM algorithm with non-uniform attenuation compensation. The transaxial reconstructed images were rearranged into short-axis slices from which bull's-eye plots of the count density distribution in the myocardium were generated.« less

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.

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.

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.

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.

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.

Reconstruction of truncated TCT and SPECT data from a right-angle dual-camera system for myocardial SPECT

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

Tsui, B.M.W.; Frey, E.C.; Lalush, D.S.

1996-12-31

We investigated methods to accurately reconstruct 180{degrees} truncated TCT and SPECT projection data obtained from a right-angle dual-camera SPECT system for myocardial SPECT with attenuation compensation. The 180{degrees} data reconstruction methods would permit substantial savings in transmission data acquisition time. Simulation data from the 3D MCAT phantom and clinical data from large patients were used in the evaluation study. Different transmission reconstruction methods including the FBP, transmission ML-EM, transmission ML-SA, and BIT algorithms with and without using the body contour as support, were used in the TCT image reconstructions. The accuracy of both the TCT and attenuation compensated SPECT imagesmore » were evaluated for different degrees of truncation and noise levels. We found that using the FBP reconstructed TCT images resulted in higher count density in the left ventricular (LV) wall of the attenuation compensated SPECT images. The LV wall count density obtained using the iteratively reconstructed TCT images with and without support were similar to each other and were more accurate than that using the FBP. However, the TCT images obtained with support show fewer image artifacts than without support. Among the iterative reconstruction algorithms, the ML-SA algorithm provides the most accurate reconstruction but is the slowest. The BIT algorithm is the fastest but shows the most image artifacts. We conclude that accurate attenuation compensated images can be obtained with truncated 180{degrees} data from large patients using a right-angle dual-camera SPECT system.« less

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

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.

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 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.

Investigation of the reconstruction accuracy of guided wave tomography using full waveform inversion

NASA Astrophysics Data System (ADS)

Rao, Jing; Ratassepp, Madis; Fan, Zheng

2017-07-01

Guided wave tomography is a promising tool to accurately determine the remaining wall thicknesses of corrosion damages, which are among the major concerns for many industries. Full Waveform Inversion (FWI) algorithm is an attractive guided wave tomography method, which uses a numerical forward model to predict the waveform of guided waves when propagating through corrosion defects, and an inverse model to reconstruct the thickness map from the ultrasonic signals captured by transducers around the defect. This paper discusses the reconstruction accuracy of the FWI algorithm on plate-like structures by using simulations as well as experiments. It was shown that this algorithm can obtain a resolution of around 0.7 wavelengths for defects with smooth depth variations from the acoustic modeling data, and about 1.5-2 wavelengths from the elastic modeling data. Further analysis showed that the reconstruction accuracy is also dependent on the shape of the defect. It was demonstrated that the algorithm maintains the accuracy in the case of multiple defects compared to conventional algorithms based on Born approximation.

Performance of the CMS missing transverse momentum reconstruction in pp data at $$\\sqrt{s}$$ = 8 TeV

DOE PAGES

Khachatryan, Vardan

2015-02-12

The performance of missing transverse energy reconstruction algorithms is presented by our team using√s=8 TeV proton-proton (pp) data collected with the CMS detector. Events with anomalous missing transverse energy are studied, and the performance of algorithms used to identify and remove these events is presented. The scale and resolution for missing transverse energy, including the effects of multiple pp interactions (pileup), are measured using events with an identified Z boson or isolated photon, and are found to be well described by the simulation. Novel missing transverse energy reconstruction algorithms developed specifically to mitigate the effects of large numbers of pileupmore » interactions on the missing transverse energy resolution are presented. These algorithms significantly reduce the dependence of the missing transverse energy resolution on pileup interactions. Furthermore, an algorithm that provides an estimate of the significance of the missing transverse energy is presented, which is used to estimate the compatibility of the reconstructed missing transverse energy with a zero nominal value.« less

3D algebraic iterative reconstruction for cone-beam x-ray differential phase-contrast computed tomography.

PubMed

Fu, Jian; Hu, Xinhua; Velroyen, Astrid; Bech, Martin; Jiang, Ming; Pfeiffer, Franz

2015-01-01

Due to the potential of compact imaging systems with magnified spatial resolution and contrast, cone-beam x-ray differential phase-contrast computed tomography (DPC-CT) has attracted significant interest. The current proposed FDK reconstruction algorithm with the Hilbert imaginary filter will induce severe cone-beam artifacts when the cone-beam angle becomes large. In this paper, we propose an algebraic iterative reconstruction (AIR) method for cone-beam DPC-CT and report its experiment results. This approach considers the reconstruction process as the optimization of a discrete representation of the object function to satisfy a system of equations that describes the cone-beam DPC-CT imaging modality. Unlike the conventional iterative algorithms for absorption-based CT, it involves the derivative operation to the forward projections of the reconstructed intermediate image to take into account the differential nature of the DPC projections. This method is based on the algebraic reconstruction technique, reconstructs the image ray by ray, and is expected to provide better derivative estimates in iterations. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured with a three-grating interferometer and a mini-focus x-ray tube source. It is shown that the proposed method can reduce the cone-beam artifacts and performs better than FDK under large cone-beam angles. This algorithm is of interest for future cone-beam DPC-CT applications.

Assessment of dedicated low-dose cardiac micro-CT reconstruction algorithms using the left ventricular volume of small rodents as a performance measure.

PubMed

Maier, Joscha; Sawall, Stefan; Kachelrieß, Marc

2014-05-01

Phase-correlated microcomputed tomography (micro-CT) imaging plays an important role in the assessment of mouse models of cardiovascular diseases and the determination of functional parameters as the left ventricular volume. As the current gold standard, the phase-correlated Feldkamp reconstruction (PCF), shows poor performance in case of low dose scans, more sophisticated reconstruction algorithms have been proposed to enable low-dose imaging. In this study, the authors focus on the McKinnon-Bates (MKB) algorithm, the low dose phase-correlated (LDPC) reconstruction, and the high-dimensional total variation minimization reconstruction (HDTV) and investigate their potential to accurately determine the left ventricular volume at different dose levels from 50 to 500 mGy. The results were verified in phantom studies of a five-dimensional (5D) mathematical mouse phantom. Micro-CT data of eight mice, each administered with an x-ray dose of 500 mGy, were acquired, retrospectively gated for cardiac and respiratory motion and reconstructed using PCF, MKB, LDPC, and HDTV. Dose levels down to 50 mGy were simulated by using only a fraction of the projections. Contrast-to-noise ratio (CNR) was evaluated as a measure of image quality. Left ventricular volume was determined using different segmentation algorithms (Otsu, level sets, region growing). Forward projections of the 5D mouse phantom were performed to simulate a micro-CT scan. The simulated data were processed the same way as the real mouse data sets. Compared to the conventional PCF reconstruction, the MKB, LDPC, and HDTV algorithm yield images of increased quality in terms of CNR. While the MKB reconstruction only provides small improvements, a significant increase of the CNR is observed in LDPC and HDTV reconstructions. The phantom studies demonstrate that left ventricular volumes can be determined accurately at 500 mGy. For lower dose levels which were simulated for real mouse data sets, the HDTV algorithm shows the best performance. At 50 mGy, the deviation from the reference obtained at 500 mGy were less than 4%. Also the LDPC algorithm provides reasonable results with deviation less than 10% at 50 mGy while PCF and MKB reconstruction show larger deviations even at higher dose levels. LDPC and HDTV increase CNR and allow for quantitative evaluations even at dose levels as low as 50 mGy. The left ventricular volumes exemplarily illustrate that cardiac parameters can be accurately estimated at lowest dose levels if sophisticated algorithms are used. This allows to reduce dose by a factor of 10 compared to today's gold standard and opens new options for longitudinal studies of the heart.

Assessment of dedicated low-dose cardiac micro-CT reconstruction algorithms using the left ventricular volume of small rodents as a performance measure

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

Maier, Joscha, E-mail: joscha.maier@dkfz.de; Sawall, Stefan; Kachelrieß, Marc

2014-05-15

Purpose: Phase-correlated microcomputed tomography (micro-CT) imaging plays an important role in the assessment of mouse models of cardiovascular diseases and the determination of functional parameters as the left ventricular volume. As the current gold standard, the phase-correlated Feldkamp reconstruction (PCF), shows poor performance in case of low dose scans, more sophisticated reconstruction algorithms have been proposed to enable low-dose imaging. In this study, the authors focus on the McKinnon-Bates (MKB) algorithm, the low dose phase-correlated (LDPC) reconstruction, and the high-dimensional total variation minimization reconstruction (HDTV) and investigate their potential to accurately determine the left ventricular volume at different dose levelsmore » from 50 to 500 mGy. The results were verified in phantom studies of a five-dimensional (5D) mathematical mouse phantom. Methods: Micro-CT data of eight mice, each administered with an x-ray dose of 500 mGy, were acquired, retrospectively gated for cardiac and respiratory motion and reconstructed using PCF, MKB, LDPC, and HDTV. Dose levels down to 50 mGy were simulated by using only a fraction of the projections. Contrast-to-noise ratio (CNR) was evaluated as a measure of image quality. Left ventricular volume was determined using different segmentation algorithms (Otsu, level sets, region growing). Forward projections of the 5D mouse phantom were performed to simulate a micro-CT scan. The simulated data were processed the same way as the real mouse data sets. Results: Compared to the conventional PCF reconstruction, the MKB, LDPC, and HDTV algorithm yield images of increased quality in terms of CNR. While the MKB reconstruction only provides small improvements, a significant increase of the CNR is observed in LDPC and HDTV reconstructions. The phantom studies demonstrate that left ventricular volumes can be determined accurately at 500 mGy. For lower dose levels which were simulated for real mouse data sets, the HDTV algorithm shows the best performance. At 50 mGy, the deviation from the reference obtained at 500 mGy were less than 4%. Also the LDPC algorithm provides reasonable results with deviation less than 10% at 50 mGy while PCF and MKB reconstruction show larger deviations even at higher dose levels. Conclusions: LDPC and HDTV increase CNR and allow for quantitative evaluations even at dose levels as low as 50 mGy. The left ventricular volumes exemplarily illustrate that cardiac parameters can be accurately estimated at lowest dose levels if sophisticated algorithms are used. This allows to reduce dose by a factor of 10 compared to today's gold standard and opens new options for longitudinal studies of the heart.« less

Low-dose CT reconstruction via L1 dictionary learning regularization using iteratively reweighted least-squares.

PubMed

Zhang, Cheng; Zhang, Tao; Li, Ming; Peng, Chengtao; Liu, Zhaobang; Zheng, Jian

2016-06-18

In order to reduce the radiation dose of CT (computed tomography), compressed sensing theory has been a hot topic since it provides the possibility of a high quality recovery from the sparse sampling data. Recently, the algorithm based on DL (dictionary learning) was developed to deal with the sparse CT reconstruction problem. However, the existing DL algorithm focuses on the minimization problem with the L2-norm regularization term, which leads to reconstruction quality deteriorating while the sampling rate declines further. Therefore, it is essential to improve the DL method to meet the demand of more dose reduction. In this paper, we replaced the L2-norm regularization term with the L1-norm one. It is expected that the proposed L1-DL method could alleviate the over-smoothing effect of the L2-minimization and reserve more image details. The proposed algorithm solves the L1-minimization problem by a weighting strategy, solving the new weighted L2-minimization problem based on IRLS (iteratively reweighted least squares). Through the numerical simulation, the proposed algorithm is compared with the existing DL method (adaptive dictionary based statistical iterative reconstruction, ADSIR) and other two typical compressed sensing algorithms. It is revealed that the proposed algorithm is more accurate than the other algorithms especially when further reducing the sampling rate or increasing the noise. The proposed L1-DL algorithm can utilize more prior information of image sparsity than ADSIR. By transforming the L2-norm regularization term of ADSIR with the L1-norm one and solving the L1-minimization problem by IRLS strategy, L1-DL could reconstruct the image more exactly.

Parametric boundary reconstruction algorithm for industrial CT metrology application.

PubMed

Yin, Zhye; Khare, Kedar; De Man, Bruno

2009-01-01

High-energy X-ray computed tomography (CT) systems have been recently used to produce high-resolution images in various nondestructive testing and evaluation (NDT/NDE) applications. The accuracy of the dimensional information extracted from CT images is rapidly approaching the accuracy achieved with a coordinate measuring machine (CMM), the conventional approach to acquire the metrology information directly. On the other hand, CT systems generate the sinogram which is transformed mathematically to the pixel-based images. The dimensional information of the scanned object is extracted later by performing edge detection on reconstructed CT images. The dimensional accuracy of this approach is limited by the grid size of the pixel-based representation of CT images since the edge detection is performed on the pixel grid. Moreover, reconstructed CT images usually display various artifacts due to the underlying physical process and resulting object boundaries from the edge detection fail to represent the true boundaries of the scanned object. In this paper, a novel algorithm to reconstruct the boundaries of an object with uniform material composition and uniform density is presented. There are three major benefits in the proposed approach. First, since the boundary parameters are reconstructed instead of image pixels, the complexity of the reconstruction algorithm is significantly reduced. The iterative approach, which can be computationally intensive, will be practical with the parametric boundary reconstruction. Second, the object of interest in metrology can be represented more directly and accurately by the boundary parameters instead of the image pixels. By eliminating the extra edge detection step, the overall dimensional accuracy and process time can be improved. Third, since the parametric reconstruction approach shares the boundary representation with other conventional metrology modalities such as CMM, boundary information from other modalities can be directly incorporated as prior knowledge to improve the convergence of an iterative approach. In this paper, the feasibility of parametric boundary reconstruction algorithm is demonstrated with both simple and complex simulated objects. Finally, the proposed algorithm is applied to the experimental industrial CT system data.

First-order convex feasibility algorithms for x-ray CT

PubMed Central

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

2013-01-01

Purpose: Iterative image reconstruction (IIR) algorithms in computed tomography (CT) are based on algorithms for solving a particular optimization problem. Design of the IIR algorithm, therefore, is aided by knowledge of the solution to the optimization problem on which it is based. Often times, however, it is impractical to achieve accurate solution to the optimization of interest, which complicates design of IIR algorithms. This issue is particularly acute for CT with a limited angular-range scan, which leads to poorly conditioned system matrices and difficult to solve optimization problems. In this paper, we develop IIR algorithms which solve a certain type of optimization called convex feasibility. The convex feasibility approach can provide alternatives to unconstrained optimization approaches and at the same time allow for rapidly convergent algorithms for their solution—thereby facilitating the IIR algorithm design process. Methods: An accelerated version of the Chambolle−Pock (CP) algorithm is adapted to various convex feasibility problems of potential interest to IIR in CT. One of the proposed problems is seen to be equivalent to least-squares minimization, and two other problems provide alternatives to penalized, least-squares minimization. Results: The accelerated CP algorithms are demonstrated on a simulation of circular fan-beam CT with a limited scanning arc of 144°. The CP algorithms are seen in the empirical results to converge to the solution of their respective convex feasibility problems. Conclusions: Formulation of convex feasibility problems can provide a useful alternative to unconstrained optimization when designing IIR algorithms for CT. The approach is amenable to recent methods for accelerating first-order algorithms which may be particularly useful for CT with limited angular-range scanning. The present paper demonstrates the methodology, and future work will illustrate its utility in actual CT application. PMID:23464295

Cluster compression algorithm: A joint clustering/data compression concept

NASA Technical Reports Server (NTRS)

Hilbert, E. E.

1977-01-01

The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources.

Suppression of motion-induced streak artifacts along chords in fan-beam BPF-reconstructions of motion-contaminated projection data

NASA Astrophysics Data System (ADS)

King, Martin; Xia, Dan; Yu, Lifeng; Pan, Xiaochuan; Giger, Maryellen

2006-03-01

Usage of the backprojection filtration (BPF) algorithm for reconstructing images from motion-contaminated fan-beam data may result in motion-induced streak artifacts, which appear in the direction of the chords on which images are reconstructed. These streak artifacts, which are most pronounced along chords tangent to the edges of the moving object, may be suppressed by use of the weighted BPF (WBPF) algorithm, which can exploit the inherent redundancies in fan-beam data. More specifically, reconstructions using full-scan and short-scan data can allow for substantial suppression of these streaks, whereas those using reduced-scan data can allow for partial suppression. Since multiple different reconstructions of the same chord can be obtained by varying the amount of redundant data used, we have laid the groundwork for a possible method to characterize the amount of motion encoded within the data used for reconstructing an image on a particular chord. Furthermore, since motion artifacts in WBPF reconstructions using full-scan and short-scan data appear similar to those in corresponding fan-beam filtered backprojection (FFBP) reconstructions for the cases performed in this study, the BPF and WBPF algorithms potentially may be used to arrive at a more fundamental characterization of how motion artifacts appear in FFBP reconstructions.

High-definition computed tomography for coronary artery stents imaging: Initial evaluation of the optimal reconstruction algorithm.

PubMed

Cui, Xiaoming; Li, Tao; Li, Xin; Zhou, Weihua

2015-05-01

The aim of this study was to evaluate the in vivo performance of four image reconstruction algorithms in a high-definition CT (HDCT) scanner with improved spatial resolution for the evaluation of coronary artery stents and intrastent lumina. Thirty-nine consecutive patients with a total of 71 implanted coronary stents underwent coronary CT angiography (CCTA) on a HDCT (Discovery CT 750 HD; GE Healthcare) with the high-resolution scanning mode. Four different reconstruction algorithms (HD-stand, HD-detail; HD-stand-plus; HD-detail-plus) were applied to reconstruct the stented coronary arteries. Image quality for stent characterization was assessed. Image noise and intrastent luminal diameter were measured. The relationship between the measurement of inner stent diameter (ISD) and the true stent diameter (TSD) and stent type were analysed. The stent-dedicated kernel (HD-detail) offered the highest percentage (53.5%) of good image quality for stent characterization and the highest ratio (68.0±8.4%) of visible stent lumen/true stent lumen for luminal diameter measurement at the expense of an increased overall image noise. The Pearson correlation coefficient between the ISD and TSD measurement and spearman correlation coefficient between the ISD measurement and stent type were 0.83 and 0.48, respectively. Compared with standard reconstruction algorithms, high-definition CT imaging technique with dedicated high-resolution reconstruction algorithm provides more accurate stent characterization and intrastent luminal diameter measurement. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks

PubMed Central

Yamanaka, Ryota; Kitano, Hiroaki

2013-01-01

Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks. PMID:24278007

Compressive Sensing of Foot Gait Signals and Its Application for the Estimation of Clinically Relevant Time Series.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2016-07-01

A new signal reconstruction algorithm for compressive sensing based on the minimization of a pseudonorm which promotes block-sparse structure on the first-order difference of the signal is proposed. Involved optimization is carried out by using a sequential version of Fletcher-Reeves' conjugate-gradient algorithm, and the line search is based on Banach's fixed-point theorem. The algorithm is suitable for the reconstruction of foot gait signals which admit block-sparse structure on the first-order difference. An additional algorithm for the estimation of stride-interval, swing-interval, and stance-interval time series from the reconstructed foot gait signals is also proposed. This algorithm is based on finding zero crossing indices of the foot gait signal and using the resulting indices for the computation of time series. Extensive simulation results demonstrate that the proposed signal reconstruction algorithm yields improved signal-to-noise ratio and requires significantly reduced computational effort relative to several competing algorithms over a wide range of compression ratio. For a compression ratio in the range from 88% to 94%, the proposed algorithm is found to offer improved accuracy for the estimation of clinically relevant time-series parameters, namely, the mean value, variance, and spectral index of stride-interval, stance-interval, and swing-interval time series, relative to its nearest competitor algorithm. The improvement in performance for compression ratio as high as 94% indicates that the proposed algorithms would be useful for designing compressive sensing-based systems for long-term telemonitoring of human gait signals.

Efficient FPT Algorithms for (Strict) Compatibility of Unrooted Phylogenetic Trees.

PubMed

Baste, Julien; Paul, Christophe; Sau, Ignasi; Scornavacca, Celine

2017-04-01

In phylogenetics, a central problem is to infer the evolutionary relationships between a set of species X; these relationships are often depicted via a phylogenetic tree-a tree having its leaves labeled bijectively by elements of X and without degree-2 nodes-called the "species tree." One common approach for reconstructing a species tree consists in first constructing several phylogenetic trees from primary data (e.g., DNA sequences originating from some species in X), and then constructing a single phylogenetic tree maximizing the "concordance" with the input trees. The obtained tree is our estimation of the species tree and, when the input trees are defined on overlapping-but not identical-sets of labels, is called "supertree." In this paper, we focus on two problems that are central when combining phylogenetic trees into a supertree: the compatibility and the strict compatibility problems for unrooted phylogenetic trees. These problems are strongly related, respectively, to the notions of "containing as a minor" and "containing as a topological minor" in the graph community. Both problems are known to be fixed parameter tractable in the number of input trees k, by using their expressibility in monadic second-order logic and a reduction to graphs of bounded treewidth. Motivated by the fact that the dependency on k of these algorithms is prohibitively large, we give the first explicit dynamic programming algorithms for solving these problems, both running in time [Formula: see text], where n is the total size of the input.

CT image reconstruction with half precision floating-point values.

PubMed

Maaß, Clemens; Baer, Matthias; Kachelrieß, Marc

2011-07-01

Analytic CT image reconstruction is a computationally demanding task. Currently, the even more demanding iterative reconstruction algorithms find their way into clinical routine because their image quality is superior to analytic image reconstruction. The authors thoroughly analyze a so far unconsidered but valuable tool of tomorrow's reconstruction hardware (CPU and GPU) that allows implementing the forward projection and backprojection steps, which are the computationally most demanding parts of any reconstruction algorithm, much more efficiently. Instead of the standard 32 bit floating-point values (float), a recently standardized floating-point value with 16 bit (half) is adopted for data representation in image domain and in rawdata domain. The reduction in the total data amount reduces the traffic on the memory bus, which is the bottleneck of today's high-performance algorithms, by 50%. In CT simulations and CT measurements, float reconstructions (gold standard) and half reconstructions are visually compared via difference images and by quantitative image quality evaluation. This is done for analytical reconstruction (filtered backprojection) and iterative reconstruction (ordered subset SART). The magnitude of quantization noise, which is caused by a reduction in the data precision of both rawdata and image data during image reconstruction, is negligible. This is clearly shown for filtered backprojection and iterative ordered subset SART reconstruction. In filtered backprojection, the implementation of the backprojection should be optimized for low data precision if the image data are represented in half format. In ordered subset SART image reconstruction, no adaptations are necessary and the convergence speed remains unchanged. Half precision floating-point values allow to speed up CT image reconstruction without compromising image quality.

A low-complexity 2-point step size gradient projection method with selective function evaluations for smoothed total variation based CBCT reconstructions

NASA Astrophysics Data System (ADS)

Song, Bongyong; Park, Justin C.; Song, William Y.

2014-11-01

The Barzilai-Borwein (BB) 2-point step size gradient method is receiving attention for accelerating Total Variation (TV) based CBCT reconstructions. In order to become truly viable for clinical applications, however, its convergence property needs to be properly addressed. We propose a novel fast converging gradient projection BB method that requires ‘at most one function evaluation’ in each iterative step. This Selective Function Evaluation method, referred to as GPBB-SFE in this paper, exhibits the desired convergence property when it is combined with a ‘smoothed TV’ or any other differentiable prior. This way, the proposed GPBB-SFE algorithm offers fast and guaranteed convergence to the desired 3DCBCT image with minimal computational complexity. We first applied this algorithm to a Shepp-Logan numerical phantom. We then applied to a CatPhan 600 physical phantom (The Phantom Laboratory, Salem, NY) and a clinically-treated head-and-neck patient, both acquired from the TrueBeam™ system (Varian Medical Systems, Palo Alto, CA). Furthermore, we accelerated the reconstruction by implementing the algorithm on NVIDIA GTX 480 GPU card. We first compared GPBB-SFE with three recently proposed BB-based CBCT reconstruction methods available in the literature using Shepp-Logan numerical phantom with 40 projections. It is found that GPBB-SFE shows either faster convergence speed/time or superior convergence property compared to existing BB-based algorithms. With the CatPhan 600 physical phantom, the GPBB-SFE algorithm requires only 3 function evaluations in 30 iterations and reconstructs the standard, 364-projection FDK reconstruction quality image using only 60 projections. We then applied the algorithm to a clinically-treated head-and-neck patient. It was observed that the GPBB-SFE algorithm requires only 18 function evaluations in 30 iterations. Compared with the FDK algorithm with 364 projections, the GPBB-SFE algorithm produces visibly equivalent quality CBCT image for the head-and-neck patient with only 180 projections, in 131.7 s, further supporting its clinical applicability.

A low-complexity 2-point step size gradient projection method with selective function evaluations for smoothed total variation based CBCT reconstructions.

PubMed

Song, Bongyong; Park, Justin C; Song, William Y

2014-11-07

The Barzilai-Borwein (BB) 2-point step size gradient method is receiving attention for accelerating Total Variation (TV) based CBCT reconstructions. In order to become truly viable for clinical applications, however, its convergence property needs to be properly addressed. We propose a novel fast converging gradient projection BB method that requires 'at most one function evaluation' in each iterative step. This Selective Function Evaluation method, referred to as GPBB-SFE in this paper, exhibits the desired convergence property when it is combined with a 'smoothed TV' or any other differentiable prior. This way, the proposed GPBB-SFE algorithm offers fast and guaranteed convergence to the desired 3DCBCT image with minimal computational complexity. We first applied this algorithm to a Shepp-Logan numerical phantom. We then applied to a CatPhan 600 physical phantom (The Phantom Laboratory, Salem, NY) and a clinically-treated head-and-neck patient, both acquired from the TrueBeam™ system (Varian Medical Systems, Palo Alto, CA). Furthermore, we accelerated the reconstruction by implementing the algorithm on NVIDIA GTX 480 GPU card. We first compared GPBB-SFE with three recently proposed BB-based CBCT reconstruction methods available in the literature using Shepp-Logan numerical phantom with 40 projections. It is found that GPBB-SFE shows either faster convergence speed/time or superior convergence property compared to existing BB-based algorithms. With the CatPhan 600 physical phantom, the GPBB-SFE algorithm requires only 3 function evaluations in 30 iterations and reconstructs the standard, 364-projection FDK reconstruction quality image using only 60 projections. We then applied the algorithm to a clinically-treated head-and-neck patient. It was observed that the GPBB-SFE algorithm requires only 18 function evaluations in 30 iterations. Compared with the FDK algorithm with 364 projections, the GPBB-SFE algorithm produces visibly equivalent quality CBCT image for the head-and-neck patient with only 180 projections, in 131.7 s, further supporting its clinical applicability.

Image reconstruction

NASA Astrophysics Data System (ADS)

Vasilenko, Georgii Ivanovich; Taratorin, Aleksandr Markovich

Linear, nonlinear, and iterative image-reconstruction (IR) algorithms are reviewed. Theoretical results are presented concerning controllable linear filters, the solution of ill-posed functional minimization problems, and the regularization of iterative IR algorithms. Attention is also given to the problem of superresolution and analytical spectrum continuation, the solution of the phase problem, and the reconstruction of images distorted by turbulence. IR in optical and optical-digital systems is discussed with emphasis on holographic techniques.

A software tool of digital tomosynthesis application for patient positioning in radiotherapy.

PubMed

Yan, Hui; Dai, Jian-Rong

2016-03-08

Digital Tomosynthesis (DTS) is an image modality in reconstructing tomographic images from two-dimensional kV projections covering a narrow scan angles. Comparing with conventional cone-beam CT (CBCT), it requires less time and radiation dose in data acquisition. It is feasible to apply this technique in patient positioning in radiotherapy. To facilitate its clinical application, a software tool was developed and the reconstruction processes were accelerated by graphic process-ing unit (GPU). Two reconstruction and two registration processes are required for DTS application which is different from conventional CBCT application which requires one image reconstruction process and one image registration process. The reconstruction stage consists of productions of two types of DTS. One type of DTS is reconstructed from cone-beam (CB) projections covering a narrow scan angle and is named onboard DTS (ODTS), which represents the real patient position in treatment room. Another type of DTS is reconstructed from digitally reconstructed radiography (DRR) and is named reference DTS (RDTS), which represents the ideal patient position in treatment room. Prior to the reconstruction of RDTS, The DRRs are reconstructed from planning CT using the same acquisition setting of CB projections. The registration stage consists of two matching processes between ODTS and RDTS. The target shift in lateral and longitudinal axes are obtained from the matching between ODTS and RDTS in coronal view, while the target shift in longitudinal and vertical axes are obtained from the matching between ODTS and RDTS in sagittal view. In this software, both DRR and DTS reconstruction algorithms were implemented on GPU environments for acceleration purpose. The comprehensive evaluation of this software tool was performed including geometric accuracy, image quality, registration accuracy, and reconstruction efficiency. The average correlation coefficient between DRR/DTS generated by GPU-based algorithm and CPU-based algorithm is 0.99. Based on the measurements of cube phantom on DTS, the geometric errors are within 0.5 mm in three axes. For both cube phantom and pelvic phantom, the registration errors are within 0.5 mm in three axes. Compared with reconstruction performance of CPU-based algorithms, the performances of DRR and DTS reconstructions are improved by a factor of 15 to 20. A GPU-based software tool was developed for DTS application for patient positioning of radiotherapy. The geometric and registration accuracy met the clinical requirement in patient setup of radiotherapy. The high performance of DRR and DTS reconstruction algorithms was achieved by the GPU-based computation environments. It is a useful software tool for researcher and clinician in evaluating DTS application in patient positioning of radiotherapy.

Practical algorithms for simulation and reconstruction of digital in-line holograms.

PubMed

Latychevskaia, Tatiana; Fink, Hans-Werner

2015-03-20

Here we present practical methods for simulation and reconstruction of in-line digital holograms recorded with plane and spherical waves. The algorithms described here are applicable to holographic imaging of an object exhibiting absorption as well as phase-shifting properties. Optimal parameters, related to distances, sampling rate, and other factors for successful simulation and reconstruction of holograms are evaluated and criteria for the achievable resolution are worked out. Moreover, we show that the numerical procedures for the reconstruction of holograms recorded with plane and spherical waves are identical under certain conditions. Experimental examples of holograms and their reconstructions are also discussed.

4D Cone-beam CT reconstruction using a motion model based on principal component analysis

PubMed Central

Staub, David; Docef, Alen; Brock, Robert S.; Vaman, Constantin; Murphy, Martin J.

2011-01-01

Purpose: To provide a proof of concept validation of a novel 4D cone-beam CT (4DCBCT) reconstruction algorithm and to determine the best methods to train and optimize the algorithm. Methods: The algorithm animates a patient fan-beam CT (FBCT) with a patient specific parametric motion model in order to generate a time series of deformed CTs (the reconstructed 4DCBCT) that track the motion of the patient anatomy on a voxel by voxel scale. The motion model is constrained by requiring that projections cast through the deformed CT time series match the projections of the raw patient 4DCBCT. The motion model uses a basis of eigenvectors that are generated via principal component analysis (PCA) of a training set of displacement vector fields (DVFs) that approximate patient motion. The eigenvectors are weighted by a parameterized function of the patient breathing trace recorded during 4DCBCT. The algorithm is demonstrated and tested via numerical simulation. Results: The algorithm is shown to produce accurate reconstruction results for the most complicated simulated motion, in which voxels move with a pseudo-periodic pattern and relative phase shifts exist between voxels. The tests show that principal component eigenvectors trained on DVFs from a novel 2D/3D registration method give substantially better results than eigenvectors trained on DVFs obtained by conventionally registering 4DCBCT phases reconstructed via filtered backprojection. Conclusions: Proof of concept testing has validated the 4DCBCT reconstruction approach for the types of simulated data considered. In addition, the authors found the 2D/3D registration approach to be our best choice for generating the DVF training set, and the Nelder-Mead simplex algorithm the most robust optimization routine. PMID:22149852

[Reconstruction of Vehicle-human Crash Accident and Injury Analysis Based on 3D Laser Scanning, Multi-rigid-body Reconstruction and Optimized Genetic Algorithm].

PubMed

Sun, J; Wang, T; Li, Z D; Shao, Y; Zhang, Z Y; Feng, H; Zou, D H; Chen, Y J

2017-12-01

To reconstruct a vehicle-bicycle-cyclist crash accident and analyse the injuries using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, and to provide biomechanical basis for the forensic identification of death cause. The vehicle was measured by 3D laser scanning technology. The multi-rigid-body models of cyclist, bicycle and vehicle were developed based on the measurements. The value range of optimal variables was set. A multi-objective genetic algorithm and the nondominated sorting genetic algorithm were used to find the optimal solutions, which were compared to the record of the surveillance video around the accident scene. The reconstruction result of laser scanning on vehicle was satisfactory. In the optimal solutions found by optimization method of genetic algorithm, the dynamical behaviours of dummy, bicycle and vehicle corresponded to that recorded by the surveillance video. The injury parameters of dummy were consistent with the situation and position of the real injuries on the cyclist in accident. The motion status before accident, damage process by crash and mechanical analysis on the injury of the victim can be reconstructed using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, which have application value in the identification of injury manner and analysis of death cause in traffic accidents. Copyright© by the Editorial Department of Journal of Forensic Medicine

Application of Curved MPR Algorithm to High Resolution 3 Dimensional T2 Weighted CISS Images for Virtual Uncoiling of Membranous Cochlea as an Aid for Cochlear Morphometry.

PubMed

Kumar, Joish Upendra; Kavitha, Y

2017-02-01

With the use of various surgical techniques, types of implants, the preoperative assessment of cochlear dimensions is becoming increasingly relevant prior to cochlear implantation. High resolution CISS protocol MRI gives a better assessment of membranous cochlea, cochlear nerve, and membranous labyrinth. Curved Multiplanar Reconstruction (MPR) algorithm provides better images that can be used for measuring dimensions of membranous cochlea. To ascertain the value of curved multiplanar reconstruction algorithm in high resolution 3-Dimensional T2 Weighted Gradient Echo Constructive Interference Steady State (3D T2W GRE CISS) imaging for accurate morphometry of membranous cochlea. Fourteen children underwent MRI for inner ear assessment. High resolution 3D T2W GRE CISS sequence was used to obtain images of cochlea. Curved MPR reconstruction algorithm was used to virtually uncoil the membranous cochlea on the volume images and cochlear measurements were done. Virtually uncoiled images of membranous cochlea of appropriate resolution were obtained from the volume data obtained from the high resolution 3D T2W GRE CISS images, after using curved MPR reconstruction algorithm mean membranous cochlear length in the children was 27.52 mm. Maximum apical turn diameter of membranous cochlea was 1.13 mm, mid turn diameter was 1.38 mm, basal turn diameter was 1.81 mm. Curved MPR reconstruction algorithm applied to CISS protocol images facilitates in getting appropriate quality images of membranous cochlea for accurate measurements.

Online Tracking Algorithms on GPUs for the P̅ANDA Experiment at FAIR

NASA Astrophysics Data System (ADS)

Bianchi, L.; Herten, A.; Ritman, J.; Stockmanns, T.; Adinetz, A.; Kraus, J.; Pleiter, D.

2015-12-01

P̅ANDA is a future hadron and nuclear physics experiment at the FAIR facility in construction in Darmstadt, Germany. In contrast to the majority of current experiments, PANDA's strategy for data acquisition is based on event reconstruction from free-streaming data, performed in real time entirely by software algorithms using global detector information. This paper reports the status of the development of algorithms for the reconstruction of charged particle tracks, optimized online data processing applications, using General-Purpose Graphic Processing Units (GPU). Two algorithms for trackfinding, the Triplet Finder and the Circle Hough, are described, and details of their GPU implementations are highlighted. Average track reconstruction times of less than 100 ns are obtained running the Triplet Finder on state-of- the-art GPU cards. In addition, a proof-of-concept system for the dispatch of data to tracking algorithms using Message Queues is presented.

RF tomography of metallic objects in free space: preliminary results

NASA Astrophysics Data System (ADS)

Li, Jia; Ewing, Robert L.; Berdanier, Charles; Baker, Christopher

2015-05-01

RF tomography has great potential in defense and homeland security applications. A distributed sensing research facility is under development at Air Force Research Lab. To develop a RF tomographic imaging system for the facility, preliminary experiments have been performed in an indoor range with 12 radar sensors distributed on a circle of 3m radius. Ultra-wideband pulses are used to illuminate single and multiple metallic targets. The echoes received by distributed sensors were processed and combined for tomography reconstruction. Traditional matched filter algorithm and truncated singular value decomposition (SVD) algorithm are compared in terms of their complexity, accuracy, and suitability for distributed processing. A new algorithm is proposed for shape reconstruction, which jointly estimates the object boundary and scatter points on the waveform's propagation path. The results show that the new algorithm allows accurate reconstruction of object shape, which is not available through the matched filter and truncated SVD algorithms.

Three-dimensional slum urban reconstruction in Envisat and Google Earth Egypt

NASA Astrophysics Data System (ADS)

Marghany, M.; Genderen, J. v.

2014-02-01

This study aims to aim to investigate the capability of ENVISAT ASAR satellite and Google Earth data for three-dimensional (3-D) slum urban reconstruction in developed country such as Egypt. The main objective of this work is to utilize 3-D automatic detection algorithm for urban slum in ENVISAT ASAR and Google Erath images were acquired in Cairo, Egypt using Fuzzy B-spline algorithm. The results show that fuzzy algorithm is the best indicator for chaotic urban slum as it can discriminate them from its surrounding environment. The combination of Fuzzy and B-spline then used to reconstruct 3-D of urban slam. The results show that urban slums, road network, and infrastructures are perfectly discriminated. It can therefore be concluded that fuzzy algorithm is an appropriate algorithm for chaotic urban slum automatic detection in ENVSIAT ASAR and Google Earth data.

Fast reconstruction of off-axis digital holograms based on digital spatial multiplexing.

PubMed

Sha, Bei; Liu, Xuan; Ge, Xiao-Lu; Guo, Cheng-Shan

2014-09-22

A method for fast reconstruction of off-axis digital holograms based on digital multiplexing algorithm is proposed. Instead of the existed angular multiplexing (AM), the new method utilizes a spatial multiplexing (SM) algorithm, in which four off-axis holograms recorded in sequence are synthesized into one SM function through multiplying each hologram with a tilted plane wave and then adding them up. In comparison with the conventional methods, the SM algorithm simplifies two-dimensional (2-D) Fourier transforms (FTs) of four N*N arrays into a 1.25-D FTs of one N*N arrays. Experimental results demonstrate that, using the SM algorithm, the computational efficiency can be improved and the reconstructed wavefronts keep the same quality as those retrieved based on the existed AM method. This algorithm may be useful in design of a fast preview system of dynamic wavefront imaging in digital holography.

Reconstructing signals from noisy data with unknown signal and noise covariance.

PubMed

Oppermann, Niels; Robbers, Georg; Ensslin, Torsten A

2011-10-01

We derive a method to reconstruct Gaussian signals from linear measurements with Gaussian noise. This new algorithm is intended for applications in astrophysics and other sciences. The starting point of our considerations is the principle of minimum Gibbs free energy, which was previously used to derive a signal reconstruction algorithm handling uncertainties in the signal covariance. We extend this algorithm to simultaneously uncertain noise and signal covariances using the same principles in the derivation. The resulting equations are general enough to be applied in many different contexts. We demonstrate the performance of the algorithm by applying it to specific example situations and compare it to algorithms not allowing for uncertainties in the noise covariance. The results show that the method we suggest performs very well under a variety of circumstances and is indeed qualitatively superior to the other methods in cases where uncertainty in the noise covariance is present.

Automated selection of the optimal cardiac phase for single-beat coronary CT angiography reconstruction.

PubMed

Stassi, D; Dutta, S; Ma, H; Soderman, A; Pazzani, D; Gros, E; Okerlund, D; Schmidt, T G

2016-01-01

Reconstructing a low-motion cardiac phase is expected to improve coronary artery visualization in coronary computed tomography angiography (CCTA) exams. This study developed an automated algorithm for selecting the optimal cardiac phase for CCTA reconstruction. The algorithm uses prospectively gated, single-beat, multiphase data made possible by wide cone-beam imaging. The proposed algorithm differs from previous approaches because the optimal phase is identified based on vessel image quality (IQ) directly, compared to previous approaches that included motion estimation and interphase processing. Because there is no processing of interphase information, the algorithm can be applied to any sampling of image phases, making it suited for prospectively gated studies where only a subset of phases are available. An automated algorithm was developed to select the optimal phase based on quantitative IQ metrics. For each reconstructed slice at each reconstructed phase, an image quality metric was calculated based on measures of circularity and edge strength of through-plane vessels. The image quality metric was aggregated across slices, while a metric of vessel-location consistency was used to ignore slices that did not contain through-plane vessels. The algorithm performance was evaluated using two observer studies. Fourteen single-beat cardiac CT exams (Revolution CT, GE Healthcare, Chalfont St. Giles, UK) reconstructed at 2% intervals were evaluated for best systolic (1), diastolic (6), or systolic and diastolic phases (7) by three readers and the algorithm. Pairwise inter-reader and reader-algorithm agreement was evaluated using the mean absolute difference (MAD) and concordance correlation coefficient (CCC) between the reader and algorithm-selected phases. A reader-consensus best phase was determined and compared to the algorithm selected phase. In cases where the algorithm and consensus best phases differed by more than 2%, IQ was scored by three readers using a five point Likert scale. There was no statistically significant difference between inter-reader and reader-algorithm agreement for either MAD or CCC metrics (p > 0.1). The algorithm phase was within 2% of the consensus phase in 15/21 of cases. The average absolute difference between consensus and algorithm best phases was 2.29% ± 2.47%, with a maximum difference of 8%. Average image quality scores for the algorithm chosen best phase were 4.01 ± 0.65 overall, 3.33 ± 1.27 for right coronary artery (RCA), 4.50 ± 0.35 for left anterior descending (LAD) artery, and 4.50 ± 0.35 for left circumflex artery (LCX). Average image quality scores for the consensus best phase were 4.11 ± 0.54 overall, 3.44 ± 1.03 for RCA, 4.39 ± 0.39 for LAD, and 4.50 ± 0.18 for LCX. There was no statistically significant difference (p > 0.1) between the image quality scores of the algorithm phase and the consensus phase. The proposed algorithm was statistically equivalent to a reader in selecting an optimal cardiac phase for CCTA exams. When reader and algorithm phases differed by >2%, image quality as rated by blinded readers was statistically equivalent. By detecting the optimal phase for CCTA reconstruction, the proposed algorithm is expected to improve coronary artery visualization in CCTA exams.

GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration.

PubMed

Sharp, G C; Kandasamy, N; Singh, H; Folkert, M

2007-10-07

This paper shows how to significantly accelerate cone-beam CT reconstruction and 3D deformable image registration using the stream-processing model. We describe data-parallel designs for the Feldkamp, Davis and Kress (FDK) reconstruction algorithm, and the demons deformable registration algorithm, suitable for use on a commodity graphics processing unit. The streaming versions of these algorithms are implemented using the Brook programming environment and executed on an NVidia 8800 GPU. Performance results using CT data of a preserved swine lung indicate that the GPU-based implementations of the FDK and demons algorithms achieve a substantial speedup--up to 80 times for FDK and 70 times for demons when compared to an optimized reference implementation on a 2.8 GHz Intel processor. In addition, the accuracy of the GPU-based implementations was found to be excellent. Compared with CPU-based implementations, the RMS differences were less than 0.1 Hounsfield unit for reconstruction and less than 0.1 mm for deformable registration.

Imaging of voids by means of a physical-optics-based shape-reconstruction algorithm.

PubMed

Liseno, Angelo; Pierri, Rocco

2004-06-01

We analyze the performance of a shape-reconstruction algorithm for the retrieval of voids starting from the electromagnetic scattered field. Such an algorithm exploits the physical optics (PO) approximation to obtain a linear unknown-data relationship and performs inversions by means of the singular-value-decomposition approach. In the case of voids, in addition to a geometrical optics reflection, the presence of the lateral wave phenomenon must be considered. We analyze the effect of the presence of lateral waves on the reconstructions. For the sake of shape reconstruction, we can regard the PO algorithm as one of assuming the electric and magnetic field on the illuminated side as constant in amplitude and linear in phase, as far as the dependence on the frequency is concerned. Therefore we analyze how much the lateral wave phenomenon impairs such an assumption, and we show inversions for both one single and two circular voids, for different values of the background permittivity.

Common-mask guided image reconstruction (c-MGIR) for enhanced 4D cone-beam computed tomography

NASA Astrophysics Data System (ADS)

Park, Justin C.; Zhang, Hao; Chen, Yunmei; Fan, Qiyong; Li, Jonathan G.; Liu, Chihray; Lu, Bo

2015-12-01

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.

Penalized maximum likelihood reconstruction for x-ray differential phase-contrast tomography

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

Brendel, Bernhard, E-mail: bernhard.brendel@philips.com; Teuffenbach, Maximilian von; Noël, Peter B.

2016-01-15

Purpose: The purpose of this work is to propose a cost function with regularization to iteratively reconstruct attenuation, phase, and scatter images simultaneously from differential phase contrast (DPC) acquisitions, without the need of phase retrieval, and examine its properties. Furthermore this reconstruction method is applied to an acquisition pattern that is suitable for a DPC tomographic system with continuously rotating gantry (sliding window acquisition), overcoming the severe smearing in noniterative reconstruction. Methods: We derive a penalized maximum likelihood reconstruction algorithm to directly reconstruct attenuation, phase, and scatter image from the measured detector values of a DPC acquisition. The proposed penaltymore » comprises, for each of the three images, an independent smoothing prior. Image quality of the proposed reconstruction is compared to images generated with FBP and iterative reconstruction after phase retrieval. Furthermore, the influence between the priors is analyzed. Finally, the proposed reconstruction algorithm is applied to experimental sliding window data acquired at a synchrotron and results are compared to reconstructions based on phase retrieval. Results: The results show that the proposed algorithm significantly increases image quality in comparison to reconstructions based on phase retrieval. No significant mutual influence between the proposed independent priors could be observed. Further it could be illustrated that the iterative reconstruction of a sliding window acquisition results in images with substantially reduced smearing artifacts. Conclusions: Although the proposed cost function is inherently nonconvex, it can be used to reconstruct images with less aliasing artifacts and less streak artifacts than reconstruction methods based on phase retrieval. Furthermore, the proposed method can be used to reconstruct images of sliding window acquisitions with negligible smearing artifacts.« less

Effects of refractive index mismatch in optical CT imaging of polymer gel dosimeters.

PubMed

Manjappa, Rakesh; Makki S, Sharath; Kumar, Rajesh; Kanhirodan, Rajan

2015-02-01

Proposing an image reconstruction technique, algebraic reconstruction technique-refraction correction (ART-rc). The proposed method takes care of refractive index mismatches present in gel dosimeter scanner at the boundary, and also corrects for the interior ray refraction. Polymer gel dosimeters with high dose regions have higher refractive index and optical density compared to the background medium, these changes in refractive index at high dose results in interior ray bending. The inclusion of the effects of refraction is an important step in reconstruction of optical density in gel dosimeters. The proposed ray tracing algorithm models the interior multiple refraction at the inhomogeneities. Jacob's ray tracing algorithm has been modified to calculate the pathlengths of the ray that traverses through the higher dose regions. The algorithm computes the length of the ray in each pixel along its path and is used as the weight matrix. Algebraic reconstruction technique and pixel based reconstruction algorithms are used for solving the reconstruction problem. The proposed method is tested with numerical phantoms for various noise levels. The experimental dosimetric results are also presented. The results show that the proposed scheme ART-rc is able to reconstruct optical density inside the dosimeter better than the results obtained using filtered backprojection and conventional algebraic reconstruction approaches. The quantitative improvement using ART-rc is evaluated using gamma-index. The refraction errors due to regions of different refractive indices are discussed. The effects of modeling of interior refraction in the dose region are presented. The errors propagated due to multiple refraction effects have been modeled and the improvements in reconstruction using proposed model is presented. The refractive index of the dosimeter has a mismatch with the surrounding medium (for dry air or water scanning). The algorithm reconstructs the dose profiles by estimating refractive indices of multiple inhomogeneities having different refractive indices and optical densities embedded in the dosimeter. This is achieved by tracking the path of the ray that traverses through the dosimeter. Extensive simulation studies have been carried out and results are found to be matching that of experimental results.

Effects of refractive index mismatch in optical CT imaging of polymer gel dosimeters

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

Manjappa, Rakesh; Makki S, Sharath; Kanhirodan, Rajan, E-mail: rajan@physics.iisc.ernet.in

2015-02-15

Purpose: Proposing an image reconstruction technique, algebraic reconstruction technique-refraction correction (ART-rc). The proposed method takes care of refractive index mismatches present in gel dosimeter scanner at the boundary, and also corrects for the interior ray refraction. Polymer gel dosimeters with high dose regions have higher refractive index and optical density compared to the background medium, these changes in refractive index at high dose results in interior ray bending. Methods: The inclusion of the effects of refraction is an important step in reconstruction of optical density in gel dosimeters. The proposed ray tracing algorithm models the interior multiple refraction at themore » inhomogeneities. Jacob’s ray tracing algorithm has been modified to calculate the pathlengths of the ray that traverses through the higher dose regions. The algorithm computes the length of the ray in each pixel along its path and is used as the weight matrix. Algebraic reconstruction technique and pixel based reconstruction algorithms are used for solving the reconstruction problem. The proposed method is tested with numerical phantoms for various noise levels. The experimental dosimetric results are also presented. Results: The results show that the proposed scheme ART-rc is able to reconstruct optical density inside the dosimeter better than the results obtained using filtered backprojection and conventional algebraic reconstruction approaches. The quantitative improvement using ART-rc is evaluated using gamma-index. The refraction errors due to regions of different refractive indices are discussed. The effects of modeling of interior refraction in the dose region are presented. Conclusions: The errors propagated due to multiple refraction effects have been modeled and the improvements in reconstruction using proposed model is presented. The refractive index of the dosimeter has a mismatch with the surrounding medium (for dry air or water scanning). The algorithm reconstructs the dose profiles by estimating refractive indices of multiple inhomogeneities having different refractive indices and optical densities embedded in the dosimeter. This is achieved by tracking the path of the ray that traverses through the dosimeter. Extensive simulation studies have been carried out and results are found to be matching that of experimental results.« less

A photoacoustic imaging reconstruction method based on directional total variation with adaptive directivity.

PubMed

Wang, Jin; Zhang, Chen; Wang, Yuanyuan

2017-05-30

In photoacoustic tomography (PAT), total variation (TV) based iteration algorithm is reported to have a good performance in PAT image reconstruction. However, classical TV based algorithm fails to preserve the edges and texture details of the image because it is not sensitive to the direction of the image. Therefore, it is of great significance to develop a new PAT reconstruction algorithm to effectively solve the drawback of TV. In this paper, a directional total variation with adaptive directivity (DDTV) model-based PAT image reconstruction algorithm, which weightedly sums the image gradients based on the spatially varying directivity pattern of the image is proposed to overcome the shortcomings of TV. The orientation field of the image is adaptively estimated through a gradient-based approach. The image gradients are weighted at every pixel based on both its anisotropic direction and another parameter, which evaluates the estimated orientation field reliability. An efficient algorithm is derived to solve the iteration problem associated with DDTV and possessing directivity of the image adaptively updated for each iteration step. Several texture images with various directivity patterns are chosen as the phantoms for the numerical simulations. The 180-, 90- and 30-view circular scans are conducted. Results obtained show that the DDTV-based PAT reconstructed algorithm outperforms the filtered back-projection method (FBP) and TV algorithms in the quality of reconstructed images with the peak signal-to-noise rations (PSNR) exceeding those of TV and FBP by about 10 and 18 dB, respectively, for all cases. The Shepp-Logan phantom is studied with further discussion of multimode scanning, convergence speed, robustness and universality aspects. In-vitro experiments are performed for both the sparse-view circular scanning and linear scanning. The results further prove the effectiveness of the DDTV, which shows better results than that of the TV with sharper image edges and clearer texture details. Both numerical simulation and in vitro experiments confirm that the DDTV provides a significant quality improvement of PAT reconstructed images for various directivity patterns.

Load identification approach based on basis pursuit denoising algorithm

NASA Astrophysics Data System (ADS)

Ginsberg, D.; Ruby, M.; Fritzen, C. P.

2015-07-01

The information of the external loads is of great interest in many fields of structural analysis, such as structural health monitoring (SHM) systems or assessment of damage after extreme events. However, in most cases it is not possible to measure the external forces directly, so they need to be reconstructed. Load reconstruction refers to the problem of estimating an input to a dynamic system when the system output and the impulse response functions are usually the knowns. Generally, this leads to a so called ill-posed inverse problem, which involves solving an underdetermined linear system of equations. For most practical applications it can be assumed that the applied loads are not arbitrarily distributed in time and space, at least some specific characteristics about the external excitation are known a priori. In this contribution this knowledge was used to develop a more suitable force reconstruction method, which allows identifying the time history and the force location simultaneously by employing significantly fewer sensors compared to other reconstruction approaches. The properties of the external force are used to transform the ill-posed problem into a sparse recovery task. The sparse solution is acquired by solving a minimization problem known as basis pursuit denoising (BPDN). The possibility of reconstructing loads based on noisy structural measurement signals will be demonstrated by considering two frequently occurring loading conditions: harmonic excitation and impact events, separately and combined. First a simulation study of a simple plate structure is carried out and thereafter an experimental investigation of a real beam is performed.

GPU-accelerated track reconstruction in the ALICE High Level Trigger

NASA Astrophysics Data System (ADS)

Rohr, David; Gorbunov, Sergey; Lindenstruth, Volker; ALICE Collaboration

2017-10-01

ALICE (A Large Heavy Ion Experiment) is one of the four major experiments at the Large Hadron Collider (LHC) at CERN. The High Level Trigger (HLT) is an online compute farm which reconstructs events measured by the ALICE detector in real-time. The most compute-intensive part is the reconstruction of particle trajectories called tracking and the most important detector for tracking is the Time Projection Chamber (TPC). The HLT uses a GPU-accelerated algorithm for TPC tracking that is based on the Cellular Automaton principle and on the Kalman filter. The GPU tracking has been running in 24/7 operation since 2012 in LHC Run 1 and 2. In order to better leverage the potential of the GPUs, and speed up the overall HLT reconstruction, we plan to bring more reconstruction steps (e.g. the tracking for other detectors) onto the GPUs. There are several tasks running so far on the CPU that could benefit from cooperation with the tracking, which is hardly feasible at the moment due to the delay of the PCI Express transfers. Moving more steps onto the GPU, and processing them on the GPU at once, will reduce PCI Express transfers and free up CPU resources. On top of that, modern GPUs and GPU programming APIs provide new features which are not yet exploited by the TPC tracking. We present our new developments for GPU reconstruction, both with a focus on the online reconstruction on GPU for the online offline computing upgrade in ALICE during LHC Run 3, and also taking into account how the current HLT in Run 2 can profit from these improvements.

Fast alternating projection methods for constrained tomographic reconstruction

PubMed Central

Liu, Li; Han, Yongxin

2017-01-01

The alternating projection algorithms are easy to implement and effective for large-scale complex optimization problems, such as constrained reconstruction of X-ray computed tomography (CT). A typical method is to use projection onto convex sets (POCS) for data fidelity, nonnegative constraints combined with total variation (TV) minimization (so called TV-POCS) for sparse-view CT reconstruction. However, this type of method relies on empirically selected parameters for satisfactory reconstruction and is generally slow and lack of convergence analysis. In this work, we use a convex feasibility set approach to address the problems associated with TV-POCS and propose a framework using full sequential alternating projections or POCS (FS-POCS) to find the solution in the intersection of convex constraints of bounded TV function, bounded data fidelity error and non-negativity. The rationale behind FS-POCS is that the mathematically optimal solution of the constrained objective function may not be the physically optimal solution. The breakdown of constrained reconstruction into an intersection of several feasible sets can lead to faster convergence and better quantification of reconstruction parameters in a physical meaningful way than that in an empirical way of trial-and-error. In addition, for large-scale optimization problems, first order methods are usually used. Not only is the condition for convergence of gradient-based methods derived, but also a primal-dual hybrid gradient (PDHG) method is used for fast convergence of bounded TV. The newly proposed FS-POCS is evaluated and compared with TV-POCS and another convex feasibility projection method (CPTV) using both digital phantom and pseudo-real CT data to show its superior performance on reconstruction speed, image quality and quantification. PMID:28253298

Blockwise conjugate gradient methods for image reconstruction in volumetric CT.

PubMed

Qiu, W; Titley-Peloquin, D; Soleimani, M

2012-11-01

Cone beam computed tomography (CBCT) enables volumetric image reconstruction from 2D projection data and plays an important role in image guided radiation therapy (IGRT). Filtered back projection is still the most frequently used algorithm in applications. The algorithm discretizes the scanning process (forward projection) into a system of linear equations, which must then be solved to recover images from measured projection data. The conjugate gradients (CG) algorithm and its variants can be used to solve (possibly regularized) linear systems of equations Ax=b and linear least squares problems minx∥b-Ax∥2, especially when the matrix A is very large and sparse. Their applications can be found in a general CT context, but in tomography problems (e.g. CBCT reconstruction) they have not widely been used. Hence, CBCT reconstruction using the CG-type algorithm LSQR was implemented and studied in this paper. In CBCT reconstruction, the main computational challenge is that the matrix A usually is very large, and storing it in full requires an amount of memory well beyond the reach of commodity computers. Because of these memory capacity constraints, only a small fraction of the weighting matrix A is typically used, leading to a poor reconstruction. In this paper, to overcome this difficulty, the matrix A is partitioned and stored blockwise, and blockwise matrix-vector multiplications are implemented within LSQR. This implementation allows us to use the full weighting matrix A for CBCT reconstruction without further enhancing computer standards. Tikhonov regularization can also be implemented in this fashion, and can produce significant improvement in the reconstructed images. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Direct single-shot phase retrieval from the diffraction pattern of separated objects

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

Leshem, Ben; Xu, Rui; Dallal, Yehonatan

The non-crystallographic phase problem arises in numerous scientific and technological fields. An important application is coherent diffractive imaging. Recent advances in X-ray free-electron lasers allow capturing of the diffraction pattern from a single nanoparticle before it disintegrates, in so-called ‘diffraction before destruction’ experiments. Presently, the phase is reconstructed by iterative algorithms, imposing a non-convex computational challenge, or by Fourier holography, requiring a well-characterized reference field. Here we present a convex scheme for single-shot phase retrieval for two (or more) sufficiently separated objects, demonstrated in two dimensions. In our approach, the objects serve as unknown references to one another, reducing themore » phase problem to a solvable set of linear equations. We establish our method numerically and experimentally in the optical domain and demonstrate a proof-of-principle single-shot coherent diffractive imaging using X-ray free-electron lasers pulses. Lastly, our scheme alleviates several limitations of current methods, offering a new pathway towards direct reconstruction of complex objects.« less

Direct single-shot phase retrieval from the diffraction pattern of separated objects

DOE PAGES

Leshem, Ben; Xu, Rui; Dallal, Yehonatan; ...

2016-02-22

The non-crystallographic phase problem arises in numerous scientific and technological fields. An important application is coherent diffractive imaging. Recent advances in X-ray free-electron lasers allow capturing of the diffraction pattern from a single nanoparticle before it disintegrates, in so-called ‘diffraction before destruction’ experiments. Presently, the phase is reconstructed by iterative algorithms, imposing a non-convex computational challenge, or by Fourier holography, requiring a well-characterized reference field. Here we present a convex scheme for single-shot phase retrieval for two (or more) sufficiently separated objects, demonstrated in two dimensions. In our approach, the objects serve as unknown references to one another, reducing themore » phase problem to a solvable set of linear equations. We establish our method numerically and experimentally in the optical domain and demonstrate a proof-of-principle single-shot coherent diffractive imaging using X-ray free-electron lasers pulses. Lastly, our scheme alleviates several limitations of current methods, offering a new pathway towards direct reconstruction of complex objects.« less

Localized lossless authentication watermark (LAW)

NASA Astrophysics Data System (ADS)

Celik, Mehmet U.; Sharma, Gaurav; Tekalp, A. Murat; Saber, Eli S.

2003-06-01

A novel framework is proposed for lossless authentication watermarking of images which allows authentication and recovery of original images without any distortions. This overcomes a significant limitation of traditional authentication watermarks that irreversibly alter image data in the process of watermarking and authenticate the watermarked image rather than the original. In particular, authenticity is verified before full reconstruction of the original image, whose integrity is inferred from the reversibility of the watermarking procedure. This reduces computational requirements in situations when either the verification step fails or the zero-distortion reconstruction is not required. A particular instantiation of the framework is implemented using a hierarchical authentication scheme and the lossless generalized-LSB data embedding mechanism. The resulting algorithm, called localized lossless authentication watermark (LAW), can localize tampered regions of the image; has a low embedding distortion, which can be removed entirely if necessary; and supports public/private key authentication and recovery options. The effectiveness of the framework and the instantiation is demonstrated through examples.

Resolution Study of a Hyperspectral Sensor using Computed Tomography in the Presence of Noise

DTIC Science & Technology

2012-06-14

diffraction efficiency is dependent on wavelength. Compared to techniques developed by later work, simple algebraic reconstruction techniques were used...spectral di- mension, using computed tomography (CT) techniques with only a finite number of diverse images. CTHIS require a reconstruction algorithm in...many frames are needed to reconstruct the spectral cube of a simple object using a theoretical lower bound. In this research a new algorithm is derived

Reconstruction of internal density distributions in porous bodies from laser ultrasonic data

NASA Technical Reports Server (NTRS)

Lu, Yichi; Goldman, Jeffrey A.; Wadley, Haydn N. G.

1992-01-01

It is presently shown that, for density-reconstruction problems in which information about the inhomogeneity is known a priori, the nonlinear least-squares algorithm yields satisfactory results on the basis of limited projection data. The back-projection algorithm, which obviates assumptions about the objective function to be reconstructed, does not recover the boundary of the inhomogeneity when the number of projections is limited and ray-bending is ignored.

A Comparison of Four-Image Reconstruction Algorithms for 3-D PET Imaging of MDAPET Camera Using Phantom Data

NASA Astrophysics Data System (ADS)

Baghaei, H.; Wong, Wai-Hoi; Uribe, J.; Li, Hongdi; Wang, Yu; Liu, Yaqiang; Xing, Tao; Ramirez, R.; Xie, Shuping; Kim, Soonseok

2004-10-01

We compared two fully three-dimensional (3-D) image reconstruction algorithms and two 3-D rebinning algorithms followed by reconstruction with a two-dimensional (2-D) filtered-backprojection algorithm for 3-D positron emission tomography (PET) imaging. The two 3-D image reconstruction algorithms were ordered-subsets expectation-maximization (3D-OSEM) and 3-D reprojection (3DRP) algorithms. The two rebinning algorithms were Fourier rebinning (FORE) and single slice rebinning (SSRB). The 3-D projection data used for this work were acquired with a high-resolution PET scanner (MDAPET) with an intrinsic transaxial resolution of 2.8 mm. The scanner has 14 detector rings covering an axial field-of-view of 38.5 mm. We scanned three phantoms: 1) a uniform cylindrical phantom with inner diameter of 21.5 cm; 2) a uniform 11.5-cm cylindrical phantom with four embedded small hot lesions with diameters of 3, 4, 5, and 6 mm; and 3) the 3-D Hoffman brain phantom with three embedded small hot lesion phantoms with diameters of 3, 5, and 8.6 mm in a warm background. Lesions were placed at different radial and axial distances. We evaluated the different reconstruction methods for MDAPET camera by comparing the noise level of images, contrast recovery, and hot lesion detection, and visually compared images. We found that overall the 3D-OSEM algorithm, especially when images post filtered with the Metz filter, produced the best results in terms of contrast-noise tradeoff, and detection of hot spots, and reproduction of brain phantom structures. Even though the MDAPET camera has a relatively small maximum axial acceptance (/spl plusmn/5 deg), images produced with the 3DRP algorithm had slightly better contrast recovery and reproduced the structures of the brain phantom slightly better than the faster 2-D rebinning methods.

A Novel 2D Image Compression Algorithm Based on Two Levels DWT and DCT Transforms with Enhanced Minimize-Matrix-Size Algorithm for High Resolution Structured Light 3D Surface Reconstruction

NASA Astrophysics Data System (ADS)

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

2015-09-01

Image compression techniques are widely used on 2D image 2D video 3D images and 3D video. There are many types of compression techniques and among the most popular are JPEG and JPEG2000. In this research, we introduce a new compression method based on applying a two level discrete cosine transform (DCT) and a two level discrete wavelet transform (DWT) in connection with novel compression steps for high-resolution images. The proposed image compression algorithm consists of four steps. (1) Transform an image by a two level DWT followed by a DCT to produce two matrices: DC- and AC-Matrix, or low and high frequency matrix, respectively, (2) apply a second level DCT on the DC-Matrix to generate two arrays, namely nonzero-array and zero-array, (3) apply the Minimize-Matrix-Size algorithm to the AC-Matrix and to the other high-frequencies generated by the second level DWT, (4) apply arithmetic coding to the output of previous steps. A novel decompression algorithm, Fast-Match-Search algorithm (FMS), is used to reconstruct all high-frequency matrices. The FMS-algorithm computes all compressed data probabilities by using a table of data, and then using a binary search algorithm for finding decompressed data inside the table. Thereafter, all decoded DC-values with the decoded AC-coefficients are combined in one matrix followed by inverse two levels DCT with two levels DWT. The technique is tested by compression and reconstruction of 3D surface patches. Additionally, this technique is compared with JPEG and JPEG2000 algorithm through 2D and 3D root-mean-square-error following reconstruction. The results demonstrate that the proposed compression method has better visual properties than JPEG and JPEG2000 and is able to more accurately reconstruct surface patches in 3D.

Diagnostic Performance of a Novel Coronary CT Angiography Algorithm: Prospective Multicenter Validation of an Intracycle CT Motion Correction Algorithm for Diagnostic Accuracy.

PubMed

Andreini, Daniele; Lin, Fay Y; Rizvi, Asim; Cho, Iksung; Heo, Ran; Pontone, Gianluca; Bartorelli, Antonio L; Mushtaq, Saima; Villines, Todd C; Carrascosa, Patricia; Choi, Byoung Wook; Bloom, Stephen; Wei, Han; Xing, Yan; Gebow, Dan; Gransar, Heidi; Chang, Hyuk-Jae; Leipsic, Jonathon; Min, James K

2018-06-01

Motion artifact can reduce the diagnostic accuracy of coronary CT angiography (CCTA) for coronary artery disease (CAD). The purpose of this study was to compare the diagnostic performance of an algorithm dedicated to correcting coronary motion artifact with the performance of standard reconstruction methods in a prospective international multicenter study. Patients referred for clinically indicated invasive coronary angiography (ICA) for suspected CAD prospectively underwent an investigational CCTA examination free from heart rate-lowering medications before they underwent ICA. Blinded core laboratory interpretations of motion-corrected and standard reconstructions for obstructive CAD (≥ 50% stenosis) were compared with ICA findings. Segments unevaluable owing to artifact were considered obstructive. The primary endpoint was per-subject diagnostic accuracy of the intracycle motion correction algorithm for obstructive CAD found at ICA. Among 230 patients who underwent CCTA with the motion correction algorithm and standard reconstruction, 92 (40.0%) had obstructive CAD on the basis of ICA findings. At a mean heart rate of 68.0 ± 11.7 beats/min, the motion correction algorithm reduced the number of nondiagnostic scans compared with standard reconstruction (20.4% vs 34.8%; p < 0.001). Diagnostic accuracy for obstructive CAD with the motion correction algorithm (62%; 95% CI, 56-68%) was not significantly different from that of standard reconstruction on a per-subject basis (59%; 95% CI, 53-66%; p = 0.28) but was superior on a per-vessel basis: 77% (95% CI, 74-80%) versus 72% (95% CI, 69-75%) (p = 0.02). The motion correction algorithm was superior in subgroups of patients with severely obstructive (≥ 70%) stenosis, heart rate ≥ 70 beats/min, and vessels in the atrioventricular groove. The motion correction algorithm studied reduces artifacts and improves diagnostic performance for obstructive CAD on a per-vessel basis and in selected subgroups on a per-subject basis.

Optimization-based image reconstruction from sparse-view data in offset-detector CBCT

NASA Astrophysics Data System (ADS)

Bian, Junguo; Wang, Jiong; Han, Xiao; Sidky, Emil Y.; Shao, Lingxiong; Pan, Xiaochuan

2013-01-01

The field of view (FOV) of a cone-beam computed tomography (CBCT) unit in a single-photon emission computed tomography (SPECT)/CBCT system can be increased by offsetting the CBCT detector. Analytic-based algorithms have been developed for image reconstruction from data collected at a large number of densely sampled views in offset-detector CBCT. However, the radiation dose involved in a large number of projections can be of a health concern to the imaged subject. CBCT-imaging dose can be reduced by lowering the number of projections. As analytic-based algorithms are unlikely to reconstruct accurate images from sparse-view data, we investigate and characterize in the work optimization-based algorithms, including an adaptive steepest descent-weighted projection onto convex sets (ASD-WPOCS) algorithms, for image reconstruction from sparse-view data collected in offset-detector CBCT. Using simulated data and real data collected from a physical pelvis phantom and patient, we verify and characterize properties of the algorithms under study. Results of our study suggest that optimization-based algorithms such as ASD-WPOCS may be developed for yielding images of potential utility from a number of projections substantially smaller than those used currently in clinical SPECT/CBCT imaging, thus leading to a dose reduction in CBCT imaging.

Dense Matching Comparison Between Census and a Convolutional Neural Network Algorithm for Plant Reconstruction

NASA Astrophysics Data System (ADS)

Xia, Y.; Tian, J.; d'Angelo, P.; Reinartz, P.

2018-05-01

3D reconstruction of plants is hard to implement, as the complex leaf distribution highly increases the difficulty level in dense matching. Semi-Global Matching has been successfully applied to recover the depth information of a scene, but may perform variably when different matching cost algorithms are used. In this paper two matching cost computation algorithms, Census transform and an algorithm using a convolutional neural network, are tested for plant reconstruction based on Semi-Global Matching. High resolution close-range photogrammetric images from a handheld camera are used for the experiment. The disparity maps generated based on the two selected matching cost methods are comparable with acceptable quality, which shows the good performance of Census and the potential of neural networks to improve the dense matching.

Trigger and Reconstruction Algorithms for the Japanese Experiment Module- Extreme Universe Space Observatory (JEM-EUSO)

NASA Technical Reports Server (NTRS)

Adams, J. H., Jr.; Andreev, Valeri; Christl, M. J.; Cline, David B.; Crawford, Hank; Judd, E. G.; Pennypacker, Carl; Watts, J. W.

2007-01-01

The JEM-EUSO collaboration intends to study high energy cosmic ray showers using a large downward looking telescope mounted on the Japanese Experiment Module of the International Space Station. The telescope focal plane is instrumented with approx.300k pixels operating as a digital camera, taking snapshots at approx. 1MHz rate. We report an investigation of the trigger and reconstruction efficiency of various algorithms based on time and spatial analysis of the pixel images. Our goal is to develop trigger and reconstruction algorithms that will allow the instrument to detect energies low enough to connect smoothly to ground-based observations.

A spectral image processing algorithm for evaluating the influence of the illuminants on the reconstructed reflectance

NASA Astrophysics Data System (ADS)

Toadere, Florin

2017-12-01

A spectral image processing algorithm that allows the illumination of the scene with different illuminants together with the reconstruction of the scene's reflectance is presented. Color checker spectral image and CIE A (warm light 2700 K), D65 (cold light 6500 K) and Cree TW Series LED T8 (4000 K) are employed for scene illumination. Illuminants used in the simulations have different spectra and, as a result of their illumination, the colors of the scene change. The influence of the illuminants on the reconstruction of the scene's reflectance is estimated. Demonstrative images and reflectance showing the operation of the algorithm are illustrated.

Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning.

PubMed

Zhang, Zhilin; Jung, Tzyy-Ping; Makeig, Scott; Rao, Bhaskar D

2013-02-01

Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as nonsparsity and strong noise contamination, current CS algorithms generally fail in this application. This paper proposes to use the block sparse Bayesian learning framework to compress/reconstruct nonsparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows that the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage.

Multiview photometric stereo.

PubMed

Hernández Esteban, Carlos; Vogiatzis, George; Cipolla, Roberto

2008-03-01

This paper addresses the problem of obtaining complete, detailed reconstructions of textureless shiny objects. We present an algorithm which uses silhouettes of the object, as well as images obtained under changing illumination conditions. In contrast with previous photometric stereo techniques, ours is not limited to a single viewpoint but produces accurate reconstructions in full 3D. A number of images of the object are obtained from multiple viewpoints, under varying lighting conditions. Starting from the silhouettes, the algorithm recovers camera motion and constructs the object's visual hull. This is then used to recover the illumination and initialise a multi-view photometric stereo scheme to obtain a closed surface reconstruction. There are two main contributions in this paper: Firstly we describe a robust technique to estimate light directions and intensities and secondly, we introduce a novel formulation of photometric stereo which combines multiple viewpoints and hence allows closed surface reconstructions. The algorithm has been implemented as a practical model acquisition system. Here, a quantitative evaluation of the algorithm on synthetic data is presented together with complete reconstructions of challenging real objects. Finally, we show experimentally how even in the case of highly textured objects, this technique can greatly improve on correspondence-based multi-view stereo results.

Reconstruction of sparse-view X-ray computed tomography using adaptive iterative algorithms.

PubMed

Liu, Li; Lin, Weikai; Jin, Mingwu

2015-01-01

In this paper, we propose two reconstruction algorithms for sparse-view X-ray computed tomography (CT). Treating the reconstruction problems as data fidelity constrained total variation (TV) minimization, both algorithms adapt the alternate two-stage strategy: projection onto convex sets (POCS) for data fidelity and non-negativity constraints and steepest descent for TV minimization. The novelty of this work is to determine iterative parameters automatically from data, thus avoiding tedious manual parameter tuning. In TV minimization, the step sizes of steepest descent are adaptively adjusted according to the difference from POCS update in either the projection domain or the image domain, while the step size of algebraic reconstruction technique (ART) in POCS is determined based on the data noise level. In addition, projection errors are used to compare with the error bound to decide whether to perform ART so as to reduce computational costs. The performance of the proposed methods is studied and evaluated using both simulated and physical phantom data. Our methods with automatic parameter tuning achieve similar, if not better, reconstruction performance compared to a representative two-stage algorithm. Copyright © 2014 Elsevier Ltd. All rights reserved.

Dental cone-beam CT reconstruction from limited-angle view data based on compressed-sensing (CS) theory for fast, low-dose X-ray imaging

NASA Astrophysics Data System (ADS)

Je, Uikyu; Cho, Hyosung; Lee, Minsik; Oh, Jieun; Park, Yeonok; Hong, Daeki; Park, Cheulkyu; Cho, Heemoon; Choi, Sungil; Koo, Yangseo

2014-06-01

Recently, reducing radiation doses has become an issue of critical importance in the broader radiological community. As a possible technical approach, especially, in dental cone-beam computed tomography (CBCT), reconstruction from limited-angle view data (< 360°) would enable fast scanning with reduced doses to the patient. In this study, we investigated and implemented an efficient reconstruction algorithm based on compressed-sensing (CS) theory for the scan geometry and performed systematic simulation works to investigate the image characteristics. We also performed experimental works by applying the algorithm to a commercially-available dental CBCT system to demonstrate its effectiveness for image reconstruction in incomplete data problems. We successfully reconstructed CBCT images with incomplete projections acquired at selected scan angles of 120, 150, 180, and 200° with a fixed angle step of 1.2° and evaluated the reconstruction quality quantitatively. Both simulation and experimental demonstrations of the CS-based reconstruction from limited-angle view data show that the algorithm can be applied directly to current dental CBCT systems for reducing the imaging doses and further improving the image quality.

Evaluation of Electroencephalography Source Localization Algorithms with Multiple Cortical Sources.

PubMed

Bradley, Allison; Yao, Jun; Dewald, Jules; Richter, Claus-Peter

2016-01-01

Source localization algorithms often show multiple active cortical areas as the source of electroencephalography (EEG). Yet, there is little data quantifying the accuracy of these results. In this paper, the performance of current source density source localization algorithms for the detection of multiple cortical sources of EEG data has been characterized. EEG data were generated by simulating multiple cortical sources (2-4) with the same strength or two sources with relative strength ratios of 1:1 to 4:1, and adding noise. These data were used to reconstruct the cortical sources using current source density (CSD) algorithms: sLORETA, MNLS, and LORETA using a p-norm with p equal to 1, 1.5 and 2. Precision (percentage of the reconstructed activity corresponding to simulated activity) and Recall (percentage of the simulated sources reconstructed) of each of the CSD algorithms were calculated. While sLORETA has the best performance when only one source is present, when two or more sources are present LORETA with p equal to 1.5 performs better. When the relative strength of one of the sources is decreased, all algorithms have more difficulty reconstructing that source. However, LORETA 1.5 continues to outperform other algorithms. If only the strongest source is of interest sLORETA is recommended, while LORETA with p equal to 1.5 is recommended if two or more of the cortical sources are of interest. These results provide guidance for choosing a CSD algorithm to locate multiple cortical sources of EEG and for interpreting the results of these algorithms.

Evaluation of Electroencephalography Source Localization Algorithms with Multiple Cortical Sources

PubMed Central

Bradley, Allison; Yao, Jun; Dewald, Jules; Richter, Claus-Peter

2016-01-01

Background Source localization algorithms often show multiple active cortical areas as the source of electroencephalography (EEG). Yet, there is little data quantifying the accuracy of these results. In this paper, the performance of current source density source localization algorithms for the detection of multiple cortical sources of EEG data has been characterized. Methods EEG data were generated by simulating multiple cortical sources (2–4) with the same strength or two sources with relative strength ratios of 1:1 to 4:1, and adding noise. These data were used to reconstruct the cortical sources using current source density (CSD) algorithms: sLORETA, MNLS, and LORETA using a p-norm with p equal to 1, 1.5 and 2. Precision (percentage of the reconstructed activity corresponding to simulated activity) and Recall (percentage of the simulated sources reconstructed) of each of the CSD algorithms were calculated. Results While sLORETA has the best performance when only one source is present, when two or more sources are present LORETA with p equal to 1.5 performs better. When the relative strength of one of the sources is decreased, all algorithms have more difficulty reconstructing that source. However, LORETA 1.5 continues to outperform other algorithms. If only the strongest source is of interest sLORETA is recommended, while LORETA with p equal to 1.5 is recommended if two or more of the cortical sources are of interest. These results provide guidance for choosing a CSD algorithm to locate multiple cortical sources of EEG and for interpreting the results of these algorithms. PMID:26809000

Parallel image reconstruction for 3D positron emission tomography from incomplete 2D projection data

NASA Astrophysics Data System (ADS)

Guerrero, Thomas M.; Ricci, Anthony R.; Dahlbom, Magnus; Cherry, Simon R.; Hoffman, Edward T.

1993-07-01

The problem of excessive computational time in 3D Positron Emission Tomography (3D PET) reconstruction is defined, and we present an approach for solving this problem through the construction of an inexpensive parallel processing system and the adoption of the FAVOR algorithm. Currently, the 3D reconstruction of the 610 images of a total body procedure would require 80 hours and the 3D reconstruction of the 620 images of a dynamic study would require 110 hours. An inexpensive parallel processing system for 3D PET reconstruction is constructed from the integration of board level products from multiple vendors. The system achieves its computational performance through the use of 6U VME four i860 processor boards, the processor boards from five manufacturers are discussed from our perspective. The new 3D PET reconstruction algorithm FAVOR, FAst VOlume Reconstructor, that promises a substantial speed improvement is adopted. Preliminary results from parallelizing FAVOR are utilized in formulating architectural improvements for this problem. In summary, we are addressing the problem of excessive computational time in 3D PET image reconstruction, through the construction of an inexpensive parallel processing system and the parallelization of a 3D reconstruction algorithm that uses the incomplete data set that is produced by current PET systems.

A shape-based quality evaluation and reconstruction method for electrical impedance tomography.

PubMed

Antink, Christoph Hoog; Pikkemaat, Robert; Malmivuo, Jaakko; Leonhardt, Steffen

2015-06-01

Linear methods of reconstruction play an important role in medical electrical impedance tomography (EIT) and there is a wide variety of algorithms based on several assumptions. With the Graz consensus reconstruction algorithm for EIT (GREIT), a novel linear reconstruction algorithm as well as a standardized framework for evaluating and comparing methods of reconstruction were introduced that found widespread acceptance in the community. In this paper, we propose a two-sided extension of this concept by first introducing a novel method of evaluation. Instead of being based on point-shaped resistivity distributions, we use 2759 pairs of real lung shapes for evaluation that were automatically segmented from human CT data. Necessarily, the figures of merit defined in GREIT were adjusted. Second, a linear method of reconstruction that uses orthonormal eigenimages as training data and a tunable desired point spread function are proposed. Using our novel method of evaluation, this approach is compared to the classical point-shaped approach. Results show that most figures of merit improve with the use of eigenimages as training data. Moreover, the possibility of tuning the reconstruction by modifying the desired point spread function is shown. Finally, the reconstruction of real EIT data shows that higher contrasts and fewer artifacts can be achieved in ventilation- and perfusion-related images.

Performance comparison of two resolution modeling PET reconstruction algorithms in terms of physical figures of merit used in quantitative imaging.

PubMed

Matheoud, R; Ferrando, O; Valzano, S; Lizio, D; Sacchetti, G; Ciarmiello, A; Foppiano, F; Brambilla, M

2015-07-01

Resolution modeling (RM) of PET systems has been introduced in iterative reconstruction algorithms for oncologic PET. The RM recovers the loss of resolution and reduces the associated partial volume effect. While these methods improved the observer performance, particularly in the detection of small and faint lesions, their impact on quantification accuracy still requires thorough investigation. The aim of this study was to characterize the performances of the RM algorithms under controlled conditions simulating a typical (18)F-FDG oncologic study, using an anthropomorphic phantom and selected physical figures of merit, used for image quantification. Measurements were performed on Biograph HiREZ (B_HiREZ) and Discovery 710 (D_710) PET/CT scanners and reconstructions were performed using the standard iterative reconstructions and the RM algorithms associated to each scanner: TrueX and SharpIR, respectively. RM determined a significant improvement in contrast recovery for small targets (≤17 mm diameter) only for the D_710 scanner. The maximum standardized uptake value (SUVmax) increased when RM was applied using both scanners. The SUVmax of small targets was on average lower with the B_HiREZ than with the D_710. Sharp IR improved the accuracy of SUVmax determination, whilst TrueX showed an overestimation of SUVmax for sphere dimensions greater than 22 mm. The goodness of fit of adaptive threshold algorithms worsened significantly when RM algorithms were employed for both scanners. Differences in general quantitative performance were observed for the PET scanners analyzed. Segmentation of PET images using adaptive threshold algorithms should not be undertaken in conjunction with RM reconstructions. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Evaluation of an iterative model-based CT reconstruction algorithm by intra-patient comparison of standard and ultra-low-dose examinations.

PubMed

Noël, Peter B; Engels, Stephan; Köhler, Thomas; Muenzel, Daniela; Franz, Daniela; Rasper, Michael; Rummeny, Ernst J; Dobritz, Martin; Fingerle, Alexander A

2018-01-01

Background The explosive growth of computer tomography (CT) has led to a growing public health concern about patient and population radiation dose. A recently introduced technique for dose reduction, which can be combined with tube-current modulation, over-beam reduction, and organ-specific dose reduction, is iterative reconstruction (IR). Purpose To evaluate the quality, at different radiation dose levels, of three reconstruction algorithms for diagnostics of patients with proven liver metastases under tumor follow-up. Material and Methods A total of 40 thorax-abdomen-pelvis CT examinations acquired from 20 patients in a tumor follow-up were included. All patients were imaged using the standard-dose and a specific low-dose CT protocol. Reconstructed slices were generated by using three different reconstruction algorithms: a classical filtered back projection (FBP); a first-generation iterative noise-reduction algorithm (iDose4); and a next generation model-based IR algorithm (IMR). Results The overall detection of liver lesions tended to be higher with the IMR algorithm than with FBP or iDose4. The IMR dataset at standard dose yielded the highest overall detectability, while the low-dose FBP dataset showed the lowest detectability. For the low-dose protocols, a significantly improved detectability of the liver lesion can be reported compared to FBP or iDose 4 ( P = 0.01). The radiation dose decreased by an approximate factor of 5 between the standard-dose and the low-dose protocol. Conclusion The latest generation of IR algorithms significantly improved the diagnostic image quality and provided virtually noise-free images for ultra-low-dose CT imaging.

Comparison of the effects of model-based iterative reconstruction and filtered back projection algorithms on software measurements in pulmonary subsolid nodules.

PubMed

Cohen, Julien G; Kim, Hyungjin; Park, Su Bin; van Ginneken, Bram; Ferretti, Gilbert R; Lee, Chang Hyun; Goo, Jin Mo; Park, Chang Min

2017-08-01

To evaluate the differences between filtered back projection (FBP) and model-based iterative reconstruction (MBIR) algorithms on semi-automatic measurements in subsolid nodules (SSNs). Unenhanced CT scans of 73 SSNs obtained using the same protocol and reconstructed with both FBP and MBIR algorithms were evaluated by two radiologists. Diameter, mean attenuation, mass and volume of whole nodules and their solid components were measured. Intra- and interobserver variability and differences between FBP and MBIR were then evaluated using Bland-Altman method and Wilcoxon tests. Longest diameter, volume and mass of nodules and those of their solid components were significantly higher using MBIR (p < 0.05) with mean differences of 1.1% (limits of agreement, -6.4 to 8.5%), 3.2% (-20.9 to 27.3%) and 2.9% (-16.9 to 22.7%) and 3.2% (-20.5 to 27%), 6.3% (-51.9 to 64.6%), 6.6% (-50.1 to 63.3%), respectively. The limits of agreement between FBP and MBIR were within the range of intra- and interobserver variability for both algorithms with respect to the diameter, volume and mass of nodules and their solid components. There were no significant differences in intra- or interobserver variability between FBP and MBIR (p > 0.05). Semi-automatic measurements of SSNs significantly differed between FBP and MBIR; however, the differences were within the range of measurement variability. • Intra- and interobserver reproducibility of measurements did not differ between FBP and MBIR. • Differences in SSNs' semi-automatic measurement induced by reconstruction algorithms were not clinically significant. • Semi-automatic measurement may be conducted regardless of reconstruction algorithm. • SSNs' semi-automated classification agreement (pure vs. part-solid) did not significantly differ between algorithms.

Updates to Multi-Dimensional Flux Reconstruction for Hypersonic Simulations on Tetrahedral Grids

NASA Technical Reports Server (NTRS)

Gnoffo, Peter A.

2010-01-01

The quality of simulated hypersonic stagnation region heating with tetrahedral meshes is investigated by using an updated three-dimensional, upwind reconstruction algorithm for the inviscid flux vector. An earlier implementation of this algorithm provided improved symmetry characteristics on tetrahedral grids compared to conventional reconstruction methods. The original formulation however displayed quantitative differences in heating and shear that were as large as 25% compared to a benchmark, structured-grid solution. The primary cause of this discrepancy is found to be an inherent inconsistency in the formulation of the flux limiter. The inconsistency is removed by employing a Green-Gauss formulation of primitive gradients at nodes to replace the previous Gram-Schmidt algorithm. Current results are now in good agreement with benchmark solutions for two challenge problems: (1) hypersonic flow over a three-dimensional cylindrical section with special attention to the uniformity of the solution in the spanwise direction and (2) hypersonic flow over a three-dimensional sphere. The tetrahedral cells used in the simulation are derived from a structured grid where cell faces are bisected across the diagonal resulting in a consistent pattern of diagonals running in a biased direction across the otherwise symmetric domain. This grid is known to accentuate problems in both shock capturing and stagnation region heating encountered with conventional, quasi-one-dimensional inviscid flux reconstruction algorithms. Therefore the test problems provide a sensitive indicator for algorithmic effects on heating. Additional simulations on a sharp, double cone and the shuttle orbiter are then presented to demonstrate the capabilities of the new algorithm on more geometrically complex flows with tetrahedral grids. These results provide the first indication that pure tetrahedral elements utilizing the updated, three-dimensional, upwind reconstruction algorithm may be used for the simulation of heating and shear in hypersonic flows in upwind, finite volume formulations.

A simplified surgical algorithm for flap reconstruction of eyebrow defects.

PubMed

Liu, Hai-Peng; Shao, Ying; Yu, Xiao-Jie; Zhang, Duo

2017-04-01

Partial or total eyebrow defects after trauma or tumor excisions have been repaired by several surgical technique and algorithms. However, these algorithms are often complicated and difficult to apply clinically. We therefore established a simplified surgical algorithm for the treatment of eyebrow defects using flap reconstruction. During the period between January 2009 and December 2015, a total of 21 Chinese patients (12 males, 9 females) with eyebrow defects were treated with eyebrow flap reconstruction. The ages ranged from 12 to 51 years. The patients included 13 cases located on the left and 8 cases on the right eyebrow. These defects were caused by trauma (5 patients) and tumor excision (16 patients). Among them, 6 patients were treated using superficial temporal artery island flap, while 15 patients were treated using the V-Y advancement pedicle flap based on the orbicularis oculi muscle. The minimum defect area was 0.8 × 1.0 cm and maximum area was 2.3 × 4.3 cm. All patients were followed up for 6 months to 5 years postoperatively. The clinical effects of eyebrow reconstruction were evaluated using a designated scoring system. All 21 flaps survived without significant complications and the shapes of the reconstructed eyebrows were continuous, symmetrical and with good integrity. According to the rating scale, there were 13 excellent, 8 good reconstructions among all patients. After an average of 9 months of follow-up, all patients had no recurrence of tumors and no infection or scarring. Based upon our experience with 21 patients who underwent eyebrow reconstruction for various eyebrow defects, we believe that our simplified surgical algorithm can serve as a model for the treatment of patients with eyebrow defects. Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Reconstruction Using Locoregional Flaps for Large Skull Base Defects.

PubMed

Hatano, Takaharu; Motomura, Hisashi; Ayabe, Shinobu

2015-06-01

We present a modified locoregional flap for the reconstruction of large anterior skull base defects that should be reconstructed with a free flap according to Yano's algorithm. No classification of skull base defects had been proposed for a long time. Yano et al suggested a new classification in 2012. The lb defect of Yano's classification extends horizontally from the cribriform plate to the orbital roof. According to Yano's algorithm for subsequent skull base reconstructive procedures, a lb defect should be reconstructed with a free flap such as an anterolateral thigh free flap or rectus abdominis myocutaneous free flap. However, our modified locoregional flap has also enabled reconstruction of lb defects. In this case series, we used a locoregional flap for lb defects. No major postoperative complications occurred. We present our modified locoregional flap that enables reconstruction of lb defects.

Filtered gradient reconstruction algorithm for compressive spectral imaging

NASA Astrophysics Data System (ADS)

Mejia, Yuri; Arguello, Henry

2017-04-01

Compressive sensing matrices are traditionally based on random Gaussian and Bernoulli entries. Nevertheless, they are subject to physical constraints, and their structure unusually follows a dense matrix distribution, such as the case of the matrix related to compressive spectral imaging (CSI). The CSI matrix represents the integration of coded and shifted versions of the spectral bands. A spectral image can be recovered from CSI measurements by using iterative algorithms for linear inverse problems that minimize an objective function including a quadratic error term combined with a sparsity regularization term. However, current algorithms are slow because they do not exploit the structure and sparse characteristics of the CSI matrices. A gradient-based CSI reconstruction algorithm, which introduces a filtering step in each iteration of a conventional CSI reconstruction algorithm that yields improved image quality, is proposed. Motivated by the structure of the CSI matrix, Φ, this algorithm modifies the iterative solution such that it is forced to converge to a filtered version of the residual ΦTy, where y is the compressive measurement vector. We show that the filtered-based algorithm converges to better quality performance results than the unfiltered version. Simulation results highlight the relative performance gain over the existing iterative algorithms.

An Efficient Augmented Lagrangian Method for Statistical X-Ray CT Image Reconstruction.

PubMed

Li, Jiaojiao; Niu, Shanzhou; Huang, Jing; Bian, Zhaoying; Feng, Qianjin; Yu, Gaohang; Liang, Zhengrong; Chen, Wufan; Ma, Jianhua

2015-01-01

Statistical iterative reconstruction (SIR) for X-ray computed tomography (CT) under the penalized weighted least-squares criteria can yield significant gains over conventional analytical reconstruction from the noisy measurement. However, due to the nonlinear expression of the objective function, most exiting algorithms related to the SIR unavoidably suffer from heavy computation load and slow convergence rate, especially when an edge-preserving or sparsity-based penalty or regularization is incorporated. In this work, to address abovementioned issues of the general algorithms related to the SIR, we propose an adaptive nonmonotone alternating direction algorithm in the framework of augmented Lagrangian multiplier method, which is termed as "ALM-ANAD". The algorithm effectively combines an alternating direction technique with an adaptive nonmonotone line search to minimize the augmented Lagrangian function at each iteration. To evaluate the present ALM-ANAD algorithm, both qualitative and quantitative studies were conducted by using digital and physical phantoms. Experimental results show that the present ALM-ANAD algorithm can achieve noticeable gains over the classical nonlinear conjugate gradient algorithm and state-of-the-art split Bregman algorithm in terms of noise reduction, contrast-to-noise ratio, convergence rate, and universal quality index metrics.

Quantitative 3D high resolution transmission ultrasound tomography: creating clinically relevant images (Conference Presentation)

NASA Astrophysics Data System (ADS)

Wiskin, James; Klock, John; Iuanow, Elaine; Borup, Dave T.; Terry, Robin; Malik, Bilal H.; Lenox, Mark

2017-03-01

There has been a great deal of research into ultrasound tomography for breast imaging over the past 35 years. Few successful attempts have been made to reconstruct high-resolution images using transmission ultrasound. To this end, advances have been made in 2D and 3D algorithms that utilize either time of arrival or full wave data to reconstruct images with high spatial and contrast resolution suitable for clinical interpretation. The highest resolution and quantitative accuracy result from inverse scattering applied to full wave data in 3D. However, this has been prohibitively computationally expensive, meaning that full inverse scattering ultrasound tomography has not been considered clinically viable. Here we show the results of applying a nonlinear inverse scattering algorithm to 3D data in a clinically useful time frame. This method yields Quantitative Transmission (QT) ultrasound images with high spatial and contrast resolution. We reconstruct sound speeds for various 2D and 3D phantoms and verify these values with independent measurements. The data are fully 3D as is the reconstruction algorithm, with no 2D approximations. We show that 2D reconstruction algorithms can introduce artifacts into the QT breast image which are avoided by using a full 3D algorithm and data. We show high resolution gross and microscopic anatomic correlations comparing cadaveric breast QT images with MRI to establish imaging capability and accuracy. Finally, we show reconstructions of data from volunteers, as well as an objective visual grading analysis to confirm clinical imaging capability and accuracy.

Total variation iterative constraint algorithm for limited-angle tomographic reconstruction of non-piecewise-constant structures

NASA Astrophysics Data System (ADS)

Krauze, W.; Makowski, P.; Kujawińska, M.

2015-06-01

Standard tomographic algorithms applied to optical limited-angle tomography result in the reconstructions that have highly anisotropic resolution and thus special algorithms are developed. State of the art approaches utilize the Total Variation (TV) minimization technique. These methods give very good results but are applicable to piecewise constant structures only. In this paper, we propose a novel algorithm for 3D limited-angle tomography - Total Variation Iterative Constraint method (TVIC) which enhances the applicability of the TV regularization to non-piecewise constant samples, like biological cells. This approach consists of two parts. First, the TV minimization is used as a strong regularizer to create a sharp-edged image converted to a 3D binary mask which is then iteratively applied in the tomographic reconstruction as a constraint in the object domain. In the present work we test the method on a synthetic object designed to mimic basic structures of a living cell. For simplicity, the test reconstructions were performed within the straight-line propagation model (SIRT3D solver from the ASTRA Tomography Toolbox), but the strategy is general enough to supplement any algorithm for tomographic reconstruction that supports arbitrary geometries of plane-wave projection acquisition. This includes optical diffraction tomography solvers. The obtained reconstructions present resolution uniformity and general shape accuracy expected from the TV regularization based solvers, but keeping the smooth internal structures of the object at the same time. Comparison between three different patterns of object illumination arrangement show very small impact of the projection acquisition geometry on the image quality.

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.

Choice of reconstructed tissue properties affects interpretation of lung EIT images.

PubMed

Grychtol, Bartłomiej; Adler, Andy

2014-06-01

Electrical impedance tomography (EIT) estimates an image of change in electrical properties within a body from stimulations and measurements at surface electrodes. There is significant interest in EIT as a tool to monitor and guide ventilation therapy in mechanically ventilated patients. In lung EIT, the EIT inverse problem is commonly linearized and only changes in electrical properties are reconstructed. Early algorithms reconstructed changes in resistivity, while most recent work using the finite element method reconstructs conductivity. Recently, we demonstrated that EIT images of ventilation can be misleading if the electrical contrasts within the thorax are not taken into account during the image reconstruction process. In this paper, we explore the effect of the choice of the reconstructed electrical properties (resistivity or conductivity) on the resulting EIT images. We show in simulation and experimental data that EIT images reconstructed with the same algorithm but with different parametrizations lead to large and clinically significant differences in the resulting images, which persist even after attempts to eliminate the impact of the parameter choice by recovering volume changes from the EIT images. Since there is no consensus among the most popular reconstruction algorithms and devices regarding the parametrization, this finding has implications for potential clinical use of EIT. We propose a program of research to develop reconstruction techniques that account for both the relationship between air volume and electrical properties of the lung and artefacts introduced by the linearization.

An experimental comparison of various methods of nearfield acoustic holography

DOE PAGES

Chelliah, Kanthasamy; Raman, Ganesh; Muehleisen, Ralph T.

2017-05-19

An experimental comparison of four different methods of nearfield acoustic holography (NAH) is presented in this study for planar acoustic sources. The four NAH methods considered in this study are based on: (1) spatial Fourier transform, (2) equivalent sources model, (3) boundary element methods and (4) statistically optimized NAH. Two dimensional measurements were obtained at different distances in front of a tonal sound source and the NAH methods were used to reconstruct the sound field at the source surface. Reconstructed particle velocity and acoustic pressure fields presented in this study showed that the equivalent sources model based algorithm along withmore » Tikhonov regularization provided the best localization of the sources. Reconstruction errors were found to be smaller for the equivalent sources model based algorithm and the statistically optimized NAH algorithm. Effect of hologram distance on the performance of various algorithms is discussed in detail. The study also compares the computational time required by each algorithm to complete the comparison. Four different regularization parameter choice methods were compared. The L-curve method provided more accurate reconstructions than the generalized cross validation and the Morozov discrepancy principle. Finally, the performance of fixed parameter regularization was comparable to that of the L-curve method.« less

An experimental comparison of various methods of nearfield acoustic holography

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

Chelliah, Kanthasamy; Raman, Ganesh; Muehleisen, Ralph T.

An experimental comparison of four different methods of nearfield acoustic holography (NAH) is presented in this study for planar acoustic sources. The four NAH methods considered in this study are based on: (1) spatial Fourier transform, (2) equivalent sources model, (3) boundary element methods and (4) statistically optimized NAH. Two dimensional measurements were obtained at different distances in front of a tonal sound source and the NAH methods were used to reconstruct the sound field at the source surface. Reconstructed particle velocity and acoustic pressure fields presented in this study showed that the equivalent sources model based algorithm along withmore » Tikhonov regularization provided the best localization of the sources. Reconstruction errors were found to be smaller for the equivalent sources model based algorithm and the statistically optimized NAH algorithm. Effect of hologram distance on the performance of various algorithms is discussed in detail. The study also compares the computational time required by each algorithm to complete the comparison. Four different regularization parameter choice methods were compared. The L-curve method provided more accurate reconstructions than the generalized cross validation and the Morozov discrepancy principle. Finally, the performance of fixed parameter regularization was comparable to that of the L-curve method.« less

Novel edge treatment method for improving the transmission reconstruction quality in Tomographic Gamma Scanning.

PubMed

Han, Miaomiao; Guo, Zhirong; Liu, Haifeng; Li, Qinghua

2018-05-01

Tomographic Gamma Scanning (TGS) is a method used for the nondestructive assay of radioactive wastes. In TGS, the actual irregular edge voxels are regarded as regular cubic voxels in the traditional treatment method. In this study, in order to improve the performance of TGS, a novel edge treatment method is proposed that considers the actual shapes of these voxels. The two different edge voxel treatment methods were compared by computing the pixel-level relative errors and normalized mean square errors (NMSEs) between the reconstructed transmission images and the ideal images. Both methods were coupled with two different interative algorithms comprising Algebraic Reconstruction Technique (ART) with a non-negativity constraint and Maximum Likelihood Expectation Maximization (MLEM). The results demonstrated that the traditional method for edge voxel treatment can introduce significant error and that the real irregular edge voxel treatment method can improve the performance of TGS by obtaining better transmission reconstruction images. With the real irregular edge voxel treatment method, MLEM algorithm and ART algorithm can be comparable when assaying homogenous matrices, but MLEM algorithm is superior to ART algorithm when assaying heterogeneous matrices. Copyright © 2018 Elsevier Ltd. All rights reserved.

Accelerating the reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning using CUDA.

PubMed

Jiansen Li; Jianqi Sun; Ying Song; Yanran Xu; Jun Zhao

2014-01-01

An effective way to improve the data acquisition speed of magnetic resonance imaging (MRI) is using under-sampled k-space data, and dictionary learning method can be used to maintain the reconstruction quality. Three-dimensional dictionary trains the atoms in dictionary in the form of blocks, which can utilize the spatial correlation among slices. Dual-dictionary learning method includes a low-resolution dictionary and a high-resolution dictionary, for sparse coding and image updating respectively. However, the amount of data is huge for three-dimensional reconstruction, especially when the number of slices is large. Thus, the procedure is time-consuming. In this paper, we first utilize the NVIDIA Corporation's compute unified device architecture (CUDA) programming model to design the parallel algorithms on graphics processing unit (GPU) to accelerate the reconstruction procedure. The main optimizations operate in the dictionary learning algorithm and the image updating part, such as the orthogonal matching pursuit (OMP) algorithm and the k-singular value decomposition (K-SVD) algorithm. Then we develop another version of CUDA code with algorithmic optimization. Experimental results show that more than 324 times of speedup is achieved compared with the CPU-only codes when the number of MRI slices is 24.

Fast magnetic resonance imaging based on high degree total variation

NASA Astrophysics Data System (ADS)

Wang, Sujie; Lu, Liangliang; Zheng, Junbao; Jiang, Mingfeng

2018-04-01

In order to eliminating the artifacts and "staircase effect" of total variation in Compressive Sensing MRI, high degree total variation model is proposed for dynamic MRI reconstruction. the high degree total variation regularization term is used as a constraint to reconstruct the magnetic resonance image, and the iterative weighted MM algorithm is proposed to solve the convex optimization problem of the reconstructed MR image model, In addtion, one set of cardiac magnetic resonance data is used to verify the proposed algorithm for MRI. The results show that the high degree total variation method has a better reconstruction effect than the total variation and the total generalized variation, which can obtain higher reconstruction SNR and better structural similarity.

A penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular tomography.

PubMed

Shang, Shang; Bai, Jing; Song, Xiaolei; Wang, Hongkai; Lau, Jaclyn

2007-01-01

Conjugate gradient method is verified to be efficient for nonlinear optimization problems of large-dimension data. In this paper, a penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular tomography (FMT) is presented. The algorithm combines the linear conjugate gradient method and the nonlinear conjugate gradient method together based on a restart strategy, in order to take advantage of the two kinds of conjugate gradient methods and compensate for the disadvantages. A quadratic penalty method is adopted to gain a nonnegative constraint and reduce the illposedness of the problem. Simulation studies show that the presented algorithm is accurate, stable, and fast. It has a better performance than the conventional conjugate gradient-based reconstruction algorithms. It offers an effective approach to reconstruct fluorochrome information for FMT.

Direct Estimation of Kinetic Parametric Images for Dynamic PET

PubMed Central

Wang, Guobao; Qi, Jinyi

2013-01-01

Dynamic positron emission tomography (PET) can monitor spatiotemporal distribution of radiotracer in vivo. The spatiotemporal information can be used to estimate parametric images of radiotracer kinetics that are of physiological and biochemical interests. Direct estimation of parametric images from raw projection data allows accurate noise modeling and has been shown to offer better image quality than conventional indirect methods, which reconstruct a sequence of PET images first and then perform tracer kinetic modeling pixel-by-pixel. Direct reconstruction of parametric images has gained increasing interests with the advances in computing hardware. Many direct reconstruction algorithms have been developed for different kinetic models. In this paper we review the recent progress in the development of direct reconstruction algorithms for parametric image estimation. Algorithms for linear and nonlinear kinetic models are described and their properties are discussed. PMID:24396500

Automatic focusing in digital holography and its application to stretched holograms.

PubMed

Memmolo, P; Distante, C; Paturzo, M; Finizio, A; Ferraro, P; Javidi, B

2011-05-15

The searching and recovering of the correct reconstruction distance in digital holography (DH) can be a cumbersome and subjective procedure. Here we report on an algorithm for automatically estimating the in-focus image and recovering the correct reconstruction distance for speckle holograms. We have tested the approach in determining the reconstruction distances of stretched digital holograms. Stretching a hologram with a variable elongation parameter makes it possible to change the in-focus distance of the reconstructed image. In this way, the proposed algorithm can be verified at different distances by dispensing the recording of different holograms. Experimental results are shown with the aim of demonstrating the usefulness of the proposed method, and a comparative analysis has been performed with respect to other existing algorithms developed for DH. © 2011 Optical Society of America

A boostrap algorithm for temporal signal reconstruction in the presence of noise from its fractional Fourier transformed intensity spectra

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

Tan, Cheng-Yang; /Fermilab

2011-02-01

A bootstrap algorithm for reconstructing the temporal signal from four of its fractional Fourier intensity spectra in the presence of noise is described. An optical arrangement is proposed which realises the bootstrap method for the measurement of ultrashort laser pulses. The measurement of short laser pulses which are less than 1 ps is an ongoing challenge in optical physics. One reason is that no oscilloscope exists today which can directly measure the time structure of these pulses and so it becomes necessary to invent other techniques which indirectly provide the necessary information for temporal pulse reconstruction. One method called FROGmore » (frequency resolved optical gating) has been in use since 19911 and is one of the popular methods for recovering these types of short pulses. The idea behind FROG is the use of multiple time-correlated pulse measurements in the frequency domain for the reconstruction. Multiple data sets are required because only intensity information is recorded and not phase, and thus by collecting multiple data sets, there is enough redundant measurements to yield the original time structure, but not necessarily uniquely (or even up to an arbitrary constant phase offset). The objective of this paper is to describe another method which is simpler than FROG. Instead of collecting many auto-correlated data sets, only two spectral intensity measurements of the temporal signal are needed in the absence of noise. The first can be from the intensity components of its usual Fourier transform and the second from its FrFT (fractional Fourier transform). In the presence of noise, a minimum of four measurements are required with the same FrFT order but with two different apertures. Armed with these two or four measurements, a unique solution up to a constant phase offset can be constructed.« less

Exact Fan-Beam Reconstruction With Arbitrary Object Translations and Truncated Projections

NASA Astrophysics Data System (ADS)

Hoskovec, Jan; Clackdoyle, Rolf; Desbat, Laurent; Rit, Simon

2016-06-01

This article proposes a new method for reconstructing two-dimensional (2D) computed tomography (CT) images from truncated and motion contaminated sinograms. The type of motion considered here is a sequence of rigid translations which are assumed to be known. The algorithm first identifies the sufficiency of angular coverage in each 2D point of the CT image to calculate the Hilbert transform from the local “virtual” trajectory which accounts for the motion and the truncation. By taking advantage of data redundancy in the full circular scan, our method expands the reconstructible region beyond the one obtained with chord-based methods. The proposed direct reconstruction algorithm is based on the Differentiated Back-Projection with Hilbert filtering (DBP-H). The motion is taken into account during backprojection which is the first step of our direct reconstruction, before taking the derivatives and inverting the finite Hilbert transform. The algorithm has been tested in a proof-of-concept study on Shepp-Logan phantom simulations with several motion cases and detector sizes.

Parallel computing of a digital hologram and particle searching for microdigital-holographic particle-tracking velocimetry

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

Satake, Shin-ichi; Kanamori, Hiroyuki; Kunugi, Tomoaki

2007-02-01

We have developed a parallel algorithm for microdigital-holographic particle-tracking velocimetry. The algorithm is used in (1) numerical reconstruction of a particle image computer using a digital hologram, and (2) searching for particles. The numerical reconstruction from the digital hologram makes use of the Fresnel diffraction equation and the FFT (fast Fourier transform),whereas the particle search algorithm looks for local maximum graduation in a reconstruction field represented by a 3D matrix. To achieve high performance computing for both calculations (reconstruction and particle search), two memory partitions are allocated to the 3D matrix. In this matrix, the reconstruction part consists of horizontallymore » placed 2D memory partitions on the x-y plane for the FFT, whereas, the particle search part consists of vertically placed 2D memory partitions set along the z axes.Consequently, the scalability can be obtained for the proportion of processor elements,where the benchmarks are carried out for parallel computation by a SGI Altix machine.« less

Algorithmic aspects for the reconstruction of spatio-spectral data cubes in the perspective of the SKA

NASA Astrophysics Data System (ADS)

Mary, D.; Ferrari, A.; Ferrari, C.; Deguignet, J.; Vannier, M.

2016-12-01

With millions of receivers leading to TerraByte data cubes, the story of the giant SKA telescope is also that of collaborative efforts from radioastronomy, signal processing, optimization and computer sciences. Reconstructing SKA cubes poses two challenges. First, the majority of existing algorithms work in 2D and cannot be directly translated into 3D. Second, the reconstruction implies solving an inverse problem and it is not clear what ultimate limit we can expect on the error of this solution. This study addresses (of course partially) both challenges. We consider an extremely simple data acquisition model, and we focus on strategies making it possible to implement 3D reconstruction algorithms that use state-of-the-art image/spectral regularization. The proposed approach has two main features: (i) reduced memory storage with respect to a previous approach; (ii) efficient parallelization and ventilation of the computational load over the spectral bands. This work will allow to implement and compare various 3D reconstruction approaches in a large scale framework.

Algorithms for bioluminescence tomography incorporating anatomical information and reconstruction of tissue optical properties

PubMed Central

Naser, Mohamed A.; Patterson, Michael S.

2010-01-01

Reconstruction algorithms are presented for a two-step solution of the bioluminescence tomography (BLT) problem. In the first step, a priori anatomical information provided by x-ray computed tomography or by other methods is used to solve the continuous wave (cw) diffuse optical tomography (DOT) problem. A Taylor series expansion approximates the light fluence rate dependence on the optical properties of each region where first and second order direct derivatives of the light fluence rate with respect to scattering and absorption coefficients are obtained and used for the reconstruction. In the second step, the reconstructed optical properties at different wavelengths are used to calculate the Green’s function of the system. Then an iterative minimization solution based on the L1 norm shrinks the permissible regions where the sources are allowed by selecting points with higher probability to contribute to the source distribution. This provides an efficient BLT reconstruction algorithm with the ability to determine relative source magnitudes and positions in the presence of noise. PMID:21258486

Volumetric quantification of lung nodules in CT with iterative reconstruction (ASiR and MBIR).

PubMed

Chen, Baiyu; Barnhart, Huiman; Richard, Samuel; Robins, Marthony; Colsher, James; Samei, Ehsan

2013-11-01

Volume quantifications of lung nodules with multidetector computed tomography (CT) images provide useful information for monitoring nodule developments. The accuracy and precision of the volume quantification, however, can be impacted by imaging and reconstruction parameters. This study aimed to investigate the impact of iterative reconstruction algorithms on the accuracy and precision of volume quantification with dose and slice thickness as additional variables. Repeated CT images were acquired from an anthropomorphic chest phantom with synthetic nodules (9.5 and 4.8 mm) at six dose levels, and reconstructed with three reconstruction algorithms [filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASiR), and model based iterative reconstruction (MBIR)] into three slice thicknesses. The nodule volumes were measured with two clinical software (A: Lung VCAR, B: iNtuition), and analyzed for accuracy and precision. Precision was found to be generally comparable between FBP and iterative reconstruction with no statistically significant difference noted for different dose levels, slice thickness, and segmentation software. Accuracy was found to be more variable. For large nodules, the accuracy was significantly different between ASiR and FBP for all slice thicknesses with both software, and significantly different between MBIR and FBP for 0.625 mm slice thickness with Software A and for all slice thicknesses with Software B. For small nodules, the accuracy was more similar between FBP and iterative reconstruction, with the exception of ASIR vs FBP at 1.25 mm with Software A and MBIR vs FBP at 0.625 mm with Software A. The systematic difference between the accuracy of FBP and iterative reconstructions highlights the importance of extending current segmentation software to accommodate the image characteristics of iterative reconstructions. In addition, a calibration process may help reduce the dependency of accuracy on reconstruction algorithms, such that volumes quantified from scans of different reconstruction algorithms can be compared. The little difference found between the precision of FBP and iterative reconstructions could be a result of both iterative reconstruction's diminished noise reduction at the edge of the nodules as well as the loss of resolution at high noise levels with iterative reconstruction. The findings do not rule out potential advantage of IR that might be evident in a study that uses a larger number of nodules or repeated scans.

The 2016 interferometric imaging beauty contest

NASA Astrophysics Data System (ADS)

Sanchez-Bermudez, J.; Thiébaut, E.; Hofmann, K.-H.; Heininger, M.; Schertl, D.; Weigelt, G.; Millour, F.; Schutz, A.; Ferrari, A.; Vannier, M.; Mary, D.; Young, J.

2016-08-01

Image reconstruction in optical interferometry has gained considerable importance for astrophysical studies during the last decade. This has been mainly due to improvements in the imaging capabilities of existing interferometers and the expectation of new facilities in the coming years. However, despite the advances made so far, image synthesis in optical interferometry is still an open field of research. Since 2004, the community has organized a biennial contest to formally test the different methods and algorithms for image reconstruction. In 2016, we celebrated the 7th edition of the "Interferometric Imaging Beauty Contest". This initiative represented an open call to participate in the reconstruction of a selected set of simulated targets with a wavelength-dependent morphology as they could be observed by the 2nd generation of VLTI instruments. This contest represents a unique opportunity to benchmark, in a systematic way, the current advances and limitations in the field, as well as to discuss possible future approaches. In this contribution, we summarize: (a) the rules of the 2016 contest; (b) the different data sets used and the selection procedure; (c) the methods and results obtained by each one of the participants; and (d) the metric used to select the best reconstructed images. Finally, we named Karl-Heinz Hofmann and the group of the Max-Planck-Institut fur Radioastronomie as winners of this edition of the contest.

Experimental study of stochastic noise propagation in SPECT images reconstructed using the conjugate gradient algorithm.

PubMed

Mariano-Goulart, D; Fourcade, M; Bernon, J L; Rossi, M; Zanca, M

2003-01-01

Thanks to an experimental study based on simulated and physical phantoms, the propagation of the stochastic noise in slices reconstructed using the conjugate gradient algorithm has been analysed versus iterations. After a first increase corresponding to the reconstruction of the signal, the noise stabilises before increasing linearly with iterations. The level of the plateau as well as the slope of the subsequent linear increase depends on the noise in the projection data.

Experimental verification of a 4D MLEM reconstruction algorithm used for in-beam PET measurements in particle therapy

NASA Astrophysics Data System (ADS)

Stützer, K.; Bert, C.; Enghardt, W.; Helmbrecht, S.; Parodi, K.; Priegnitz, M.; Saito, N.; Fiedler, F.

2013-08-01

In-beam positron emission tomography (PET) has been proven to be a reliable technique in ion beam radiotherapy for the in situ and non-invasive evaluation of the correct dose deposition in static tumour entities. In the presence of intra-fractional target motion an appropriate time-resolved (four-dimensional, 4D) reconstruction algorithm has to be used to avoid reconstructed activity distributions suffering from motion-related blurring artefacts and to allow for a dedicated dose monitoring. Four-dimensional reconstruction algorithms from diagnostic PET imaging that can properly handle the typically low counting statistics of in-beam PET data have been adapted and optimized for the characteristics of the double-head PET scanner BASTEI installed at GSI Helmholtzzentrum Darmstadt, Germany (GSI). Systematic investigations with moving radioactive sources demonstrate the more effective reduction of motion artefacts by applying a 4D maximum likelihood expectation maximization (MLEM) algorithm instead of the retrospective co-registration of phasewise reconstructed quasi-static activity distributions. Further 4D MLEM results are presented from in-beam PET measurements of irradiated moving phantoms which verify the accessibility of relevant parameters for the dose monitoring of intra-fractionally moving targets. From in-beam PET listmode data sets acquired together with a motion surrogate signal, valuable images can be generated by the 4D MLEM reconstruction for different motion patterns and motion-compensated beam delivery techniques.

Reconstruction of extended Petri nets from time series data and its application to signal transduction and to gene regulatory networks

PubMed Central

2011-01-01

Background Network inference methods reconstruct mathematical models of molecular or genetic networks directly from experimental data sets. We have previously reported a mathematical method which is exclusively data-driven, does not involve any heuristic decisions within the reconstruction process, and deliveres all possible alternative minimal networks in terms of simple place/transition Petri nets that are consistent with a given discrete time series data set. Results We fundamentally extended the previously published algorithm to consider catalysis and inhibition of the reactions that occur in the underlying network. The results of the reconstruction algorithm are encoded in the form of an extended Petri net involving control arcs. This allows the consideration of processes involving mass flow and/or regulatory interactions. As a non-trivial test case, the phosphate regulatory network of enterobacteria was reconstructed using in silico-generated time-series data sets on wild-type and in silico mutants. Conclusions The new exact algorithm reconstructs extended Petri nets from time series data sets by finding all alternative minimal networks that are consistent with the data. It suggested alternative molecular mechanisms for certain reactions in the network. The algorithm is useful to combine data from wild-type and mutant cells and may potentially integrate physiological, biochemical, pharmacological, and genetic data in the form of a single model. PMID:21762503

Jane: a new tool for the cophylogeny reconstruction problem.

PubMed

Conow, Chris; Fielder, Daniel; Ovadia, Yaniv; Libeskind-Hadas, Ran

2010-02-03

This paper describes the theory and implementation of a new software tool, called Jane, for the study of historical associations. This problem arises in parasitology (associations of hosts and parasites), molecular systematics (associations of orderings and genes), and biogeography (associations of regions and orderings). The underlying problem is that of reconciling pairs of trees subject to biologically plausible events and costs associated with these events. Existing software tools for this problem have strengths and limitations, and the new Jane tool described here provides functionality that complements existing tools. The Jane software tool uses a polynomial time dynamic programming algorithm in conjunction with a genetic algorithm to find very good, and often optimal, solutions even for relatively large pairs of trees. The tool allows the user to provide rich timing information on both the host and parasite trees. In addition the user can limit host switch distance and specify multiple host switch costs by specifying regions in the host tree and costs for host switches between pairs of regions. Jane also provides a graphical user interface that allows the user to interactively experiment with modifications to the solutions found by the program. Jane is shown to be a useful tool for cophylogenetic reconstruction. Its functionality complements existing tools and it is therefore likely to be of use to researchers in the areas of parasitology, molecular systematics, and biogeography.

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

Stassi, D.; Ma, H.; Schmidt, T. G., E-mail: taly.gilat-schmidt@marquette.edu

Purpose: Reconstructing a low-motion cardiac phase is expected to improve coronary artery visualization in coronary computed tomography angiography (CCTA) exams. This study developed an automated algorithm for selecting the optimal cardiac phase for CCTA reconstruction. The algorithm uses prospectively gated, single-beat, multiphase data made possible by wide cone-beam imaging. The proposed algorithm differs from previous approaches because the optimal phase is identified based on vessel image quality (IQ) directly, compared to previous approaches that included motion estimation and interphase processing. Because there is no processing of interphase information, the algorithm can be applied to any sampling of image phases, makingmore » it suited for prospectively gated studies where only a subset of phases are available. Methods: An automated algorithm was developed to select the optimal phase based on quantitative IQ metrics. For each reconstructed slice at each reconstructed phase, an image quality metric was calculated based on measures of circularity and edge strength of through-plane vessels. The image quality metric was aggregated across slices, while a metric of vessel-location consistency was used to ignore slices that did not contain through-plane vessels. The algorithm performance was evaluated using two observer studies. Fourteen single-beat cardiac CT exams (Revolution CT, GE Healthcare, Chalfont St. Giles, UK) reconstructed at 2% intervals were evaluated for best systolic (1), diastolic (6), or systolic and diastolic phases (7) by three readers and the algorithm. Pairwise inter-reader and reader-algorithm agreement was evaluated using the mean absolute difference (MAD) and concordance correlation coefficient (CCC) between the reader and algorithm-selected phases. A reader-consensus best phase was determined and compared to the algorithm selected phase. In cases where the algorithm and consensus best phases differed by more than 2%, IQ was scored by three readers using a five point Likert scale. Results: There was no statistically significant difference between inter-reader and reader-algorithm agreement for either MAD or CCC metrics (p > 0.1). The algorithm phase was within 2% of the consensus phase in 15/21 of cases. The average absolute difference between consensus and algorithm best phases was 2.29% ± 2.47%, with a maximum difference of 8%. Average image quality scores for the algorithm chosen best phase were 4.01 ± 0.65 overall, 3.33 ± 1.27 for right coronary artery (RCA), 4.50 ± 0.35 for left anterior descending (LAD) artery, and 4.50 ± 0.35 for left circumflex artery (LCX). Average image quality scores for the consensus best phase were 4.11 ± 0.54 overall, 3.44 ± 1.03 for RCA, 4.39 ± 0.39 for LAD, and 4.50 ± 0.18 for LCX. There was no statistically significant difference (p > 0.1) between the image quality scores of the algorithm phase and the consensus phase. Conclusions: The proposed algorithm was statistically equivalent to a reader in selecting an optimal cardiac phase for CCTA exams. When reader and algorithm phases differed by >2%, image quality as rated by blinded readers was statistically equivalent. By detecting the optimal phase for CCTA reconstruction, the proposed algorithm is expected to improve coronary artery visualization in CCTA exams.« less

Wavefront sensing with a thin diffuser

NASA Astrophysics Data System (ADS)

Berto, Pascal; Rigneault, Hervé; Guillon, Marc

2017-12-01

We propose and implement a broadband, compact, and low-cost wavefront sensing scheme by simply placing a thin diffuser in the close vicinity of a camera. The local wavefront gradient is determined from the local translation of the speckle pattern. The translation vector map is computed thanks to a fast diffeomorphic image registration algorithm and integrated to reconstruct the wavefront profile. The simple translation of speckle grains under local wavefront tip/tilt is ensured by the so-called "memory effect" of the diffuser. Quantitative wavefront measurements are experimentally demonstrated both for the few first Zernike polynomials and for phase-imaging applications requiring high resolution. We finally provided a theoretical description of the resolution limit that is supported experimentally.