scholarly journals Improving reproducibility in synchrotron tomography using implementation-adapted filters

2021 ◽  
Vol 28 (5) ◽  
Author(s):  
Poulami Somanya Ganguly ◽  
Daniël M. Pelt ◽  
Doga Gürsoy ◽  
Francesco de Carlo ◽  
K. Joost Batenburg
Keyword(s):  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Computationally Efficient ◽  
Real World Data ◽  
Processing Pipeline ◽  
Software Packages ◽  
Synchrotron Tomography ◽  
Mathematical Formulas ◽  
Computationally Efficient Algorithms ◽  
Tomography Data

For reconstructing large tomographic datasets fast, filtered backprojection-type or Fourier-based algorithms are still the method of choice, as they have been for decades. These robust and computationally efficient algorithms have been integrated in a broad range of software packages. The continuous mathematical formulas used for image reconstruction in such algorithms are unambiguous. However, variations in discretization and interpolation result in quantitative differences between reconstructed images, and corresponding segmentations, obtained from different software. This hinders reproducibility of experimental results, making it difficult to ensure that results and conclusions from experiments can be reproduced at different facilities or using different software. In this paper, a way to reduce such differences by optimizing the filter used in analytical algorithms is proposed. These filters can be computed using a wrapper routine around a black-box implementation of a reconstruction algorithm, and lead to quantitatively similar reconstructions. Use cases for this approach are demonstrated by computing implementation-adapted filters for several open-source implementations and applying them to simulated phantoms and real-world data acquired at the synchrotron. Our contribution to a reproducible reconstruction step forms a building block towards a fully reproducible synchrotron tomography data processing pipeline.

Nondestructive Inspection of Thin, Low-Z Samples using Multiplexed Compton Scatter Tomography

1997 ◽  
Vol 503 ◽  
Author(s):  
B. L. Evans ◽  
J. B. Martin ◽  
L. W. Burggraf
Keyword(s):  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Line Broadening ◽  
Compton Scatter ◽  
X Ray ◽  
Tomography System ◽  
Transmission Computed Tomography ◽  
High Purity Germanium ◽  
Scattered Energy

ABSTRACTThe viability of a Compton scattering tomography system for nondestructively inspecting thin, low Z samples for corrosion is examined. This technique differs from conventional x-ray backscatter NDI because it does not rely on narrow collimation of source and detectors to examine small volumes in the sample. Instead, photons of a single energy are backscattered from the sample and their scattered energy spectra are measured at multiple detector locations, and these spectra are then used to reconstruct an image of the object. This multiplexed Compton scatter tomography technique interrogates multiple volume elements simultaneously. Thin samples less than 1 cm thick and made of low Z materials are best imaged with gamma rays at or below 100 keV energy. At this energy, Compton line broadening becomes an important resolution limitation. An analytical model has been developed to simulate the signals collected in a demonstration system consisting of an array of planar high-purity germanium detectors. A technique for deconvolving the effects of Compton broadening and detector energy resolution from signals with additive noise is also presented. A filtered backprojection image reconstruction algorithm with similarities to that used in conventional transmission computed tomography is developed. A simulation of a 360–degree inspection gives distortion-free results. In a simulation of a single-sided inspection, a 5 mm × 5 mm corrosion flaw with 50% density is readily identified in 1-cm thick aluminum phantom when the signal to noise ratio in the data exceeds 28.

scholarly journals Different algorithms, different models

2021 ◽  
Author(s):  
Martyna Daria Swiatczak
Keyword(s):  
Comparative Analysis ◽  
Real World ◽  
Qualitative Comparative Analysis ◽  
Comparative Methods ◽  
Data Sets ◽  
Simulation Studies ◽  
Threshold Values ◽  
Real World Data ◽  
Software Packages ◽  
Methodological Approaches

AbstractThis study assesses the extent to which the two main Configurational Comparative Methods (CCMs), i.e. Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), produce different models. It further explains how this non-identity is due to the different algorithms upon which both methods are based, namely QCA’s Quine–McCluskey algorithm and the CNA algorithm. I offer an overview of the fundamental differences between QCA and CNA and demonstrate both underlying algorithms on three data sets of ascending proximity to real-world data. Subsequent simulation studies in scenarios of varying sample sizes and degrees of noise in the data show high overall ratios of non-identity between the QCA parsimonious solution and the CNA atomic solution for varying analytical choices, i.e. different consistency and coverage threshold values and ways to derive QCA’s parsimonious solution. Clarity on the contrasts between the two methods is supposed to enable scholars to make more informed decisions on their methodological approaches, enhance their understanding of what is happening behind the results generated by the software packages, and better navigate the interpretation of results. Clarity on the non-identity between the underlying algorithms and their consequences for the results is supposed to provide a basis for a methodological discussion about which method and which variants thereof are more successful in deriving which search target.

Computationally efficient algorithms for location area planning in future cellular systems

2000 ◽  
Vol 23 (13) ◽  
pp. 1263-1280 ◽  
Author(s):  
P. Demestichas ◽  
N. Georgantas ◽  
E. Tzifa ◽  
V. Demesticha ◽  
M. Striki ◽  
...  
Keyword(s):  
Efficient Algorithms ◽  
Cellular Systems ◽  
Computationally Efficient ◽  
Location Area ◽  
Computationally Efficient Algorithms ◽  
Area Planning

scholarly journals Characterizing Longitudinal Changes in the Impedance Spectra of In-Vivo Peripheral Nerve Electrodes

Micromachines ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 587 ◽  
Author(s):  
Malgorzata Straka ◽  
Benjamin Shafer ◽  
Srikanth Vasudevan ◽  
Cristin Welle ◽  
Loren Rieth
Keyword(s):  
Electrochemical Impedance ◽  
Iridium Oxide ◽  
Support Vector ◽  
Impedance Spectra ◽  
Computationally Efficient ◽  
Wide Range ◽  
Tissue Interface ◽  
Computationally Efficient Algorithms ◽  
Physical Changes

Characterizing the aging processes of electrodes in vivo is essential in order to elucidate the changes of the electrode–tissue interface and the device. However, commonly used impedance measurements at 1 kHz are insufficient for determining electrode viability, with measurements being prone to false positives. We implanted cohorts of five iridium oxide (IrOx) and six platinum (Pt) Utah arrays into the sciatic nerve of rats, and collected the electrochemical impedance spectroscopy (EIS) up to 12 weeks or until array failure. We developed a method to classify the shapes of the magnitude and phase spectra, and correlated the classifications to circuit models and electrochemical processes at the interface likely responsible. We found categories of EIS characteristic of iridium oxide tip metallization, platinum tip metallization, tip metal degradation, encapsulation degradation, and wire breakage in the lead. We also fitted the impedance spectra as features to a fine-Gaussian support vector machine (SVM) algorithm for both IrOx and Pt tipped arrays, with a prediction accuracy for categories of 95% and 99%, respectively. Together, this suggests that these simple and computationally efficient algorithms are sufficient to explain the majority of variance across a wide range of EIS data describing Utah arrays. These categories were assessed over time, providing insights into the degradation and failure mechanisms for both the electrode–tissue interface and wire bundle. Methods developed in this study will allow for a better understanding of how EIS can characterize the physical changes to electrodes in vivo.

scholarly journals Using Relion software within Scipion framework

2020 ◽  
Author(s):  
Grigory Sharov ◽  
Dustin R. Morado ◽  
Marta Carroni ◽  
José Miguel de la Rosa-Trevín
Keyword(s):  
Image Processing ◽  
User Interfaces ◽  
Published Data ◽  
New Developments ◽  
Processing Pipeline ◽  
Software Packages ◽  
File Formats ◽  
Processing Framework ◽  
Taking Care

Scipion is a modular image processing framework integrating several software packages under a unified interface while taking care of file formats and conversions. Here new developments and capabilities of the Scipion plugin for the Relion software are presented and illustrated with the image processing pipeline of published data. The user interfaces of Scipion and Relion are compared and the key differences highlighted, allowing this manuscript to be used as a guide for both new and experienced users of these software. Different streaming image processing options are also discussed demonstrating the flexibility of the Scipion framework.SynopsisAn overview of the Scipion plugin for the Relion software is presented and various capabilities of image processing within Scipion framework are discussed.

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