scholarly journals Principles for an Implementation of a Complete CT Reconstruction Tool Chain for Arbitrary Sized Data Sets and Its GPU Optimization

2022 ◽  
Vol 8 (1) ◽  
pp. 12
Author(s):  
Jürgen Hofmann ◽  
Alexander Flisch ◽  
Robert Zboray
Keyword(s):  
Rotation Axis ◽  
Cone Beam Ct ◽  
Optimization Techniques ◽  
Data Sets ◽  
Ct Reconstruction ◽  
Beam Hardening ◽  
Tool Chain ◽  
Gpu Optimization ◽  
Speed Up ◽  
Kernel Optimization

This article describes the implementation of an efficient and fast in-house computed tomography (CT) reconstruction framework. The implementation principles of this cone-beam CT reconstruction tool chain are described here. The article mainly covers the core part of CT reconstruction, the filtered backprojection and its speed up on GPU hardware. Methods and implementations of tools for artifact reduction such as ring artifacts, beam hardening, algorithms for the center of rotation determination and tilted rotation axis correction are presented. The framework allows the reconstruction of CT images of arbitrary data size. Strategies on data splitting and GPU kernel optimization techniques applied for the backprojection process are illustrated by a few examples.

scholarly journals Beam Hardening Artifact Reduction in X-Ray CT Reconstruction of 3D Printed Metal Parts Leveraging Deep Learning and CAD Models

2020 ◽  
Author(s):  
Amirkoushyar Ziabari ◽  
Singanallur Venkatakrishnan ◽  
Michael Kirka ◽  
Paul Brackman ◽  
Ryan Dehoff ◽  
...  
Keyword(s):  
Computer Aided Design ◽  
Critical Role ◽  
Simulated Data ◽  
Data Sets ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Beam Hardening ◽  
X Ray ◽  
Cad Models ◽  
Aided Design

Abstract Nondestructive evaluation (NDE) of additively manufactured (AM) parts is important for understanding the impacts of various process parameters and qualifying the built part. X-ray computed tomography (XCT) has played a critical role in rapid NDE and characterization of AM parts. However, XCT of metal AM parts can be challenging because of artifacts produced by standard reconstruction algorithms as a result of a confounding effect called “beam hardening.” Beam hardening artifacts complicate the analysis of XCT images and adversely impact the process of detecting defects, such as pores and cracks, which is key to ensuring the quality of the parts being printed. In this work, we propose a novel framework based on using available computer-aided design (CAD) models for parts to be manufactured, accurate XCT simulations, and a deep-neural network to produce high-quality XCT reconstructions from data that are affected by noise and beam hardening. Using extensive experiments with simulated data sets, we demonstrate that our method can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art. We also present promising preliminary results of applying the deep networks trained using CAD models to experimental data obtained from XCT of an AM jet-engine turbine blade.

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

2014 ◽  
Vol 64 (12) ◽  
pp. 1907-1911
Author(s):  
Uikyu Je ◽  
Hyosung Cho ◽  
Minsik Lee ◽  
Jieun Oh ◽  
Yeonok Park ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Low Dose ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Reconstruction ◽  
X Ray ◽  
X Ray Imaging ◽  
Limited Angle

scholarly journals Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
S. Sakinah S. Ahmad ◽  
Witold Pedrycz
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Data Sets ◽  
Swarm Optimization

The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.

Modelling of Piezoceramic Patches for Actuator Placement Strategies

2014 ◽  
Author(s):  
Michael Rose
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Structural Model ◽  
Electrical Coupling ◽  
Element Model ◽  
Dynamics Simulation ◽  
Tool Chain ◽  
Active Layers ◽  
Speed Up ◽  
Actuator Placement

Piezoceramic Patches are commonly used as actuator devices in smart structures if the induced forces are sufficient for the application. To model these devices in a structural dynamics simulation, a finite element model can be augmented by active layers. This needs a suitable element meshing, taking care of the actual shapes and positions of the active patches in use. If many different setups have to be evaluated, which is naturally the case for placement strategies for suitable actuator positions, this approach is quite cumbersome. To ease and speed up the augmentation of fixed finite element models with piezoceramic patches, so called modal correction methods have been successfully used in this context. These approximative methods avoid the remeshing and the reassembling of the underlying finite element model by adapting the modal description of the structural model with the mass, stiffness and electrical coupling effects of the applied patches. In this paper different aspects of this modelling approach are discussed especially for a tool chain to optimize patch locations in an ASAC simulation environment.

Beam hardening correction for sparse-view CT reconstruction

2015 ◽  
Author(s):  
Wenlei Liu ◽  
Junyan Rong ◽  
Peng Gao ◽  
Qimei Liao ◽  
HongBing Lu
Keyword(s):  
Ct Reconstruction ◽  
Beam Hardening ◽  
Beam Hardening Correction
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