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

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Hsuan-Ming Huang ◽  
Ing-Tsung Hsiao
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Low Dose ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Acceleration Techniques ◽  
Reconstruction Methods ◽  
Speed Up ◽  
Phantom Studies

Background and Objective. 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.Methods. 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. 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.Conclusions. 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.

scholarly journals New algorithms for computed tomography image reconstruction to eliminate artifacts

2021 ◽  
Author(s):  
Eli Lechtman
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Tomography Image ◽  
Ct Simulation ◽  
Novel Algorithms ◽  
Computed Tomography Image

Computed tomography (CT) relies on computational algorithms to reconstruct images from CT projections. Current filtered backprojection reconstruction methods have inherent limitations in situations with sharp density gradients and limited beam views. In this thesis two novel reconstruction algorithms were introduced: the Algebraic Image Reconstruction (AIR) algorithm, and the Geometric Image Reconstruction Algorithm (GIRA). A CT simulation was developed to test these novel algorithms and compare their images to filtered backprojection images. AIR and GIRA each demonstrated their proof of principle in these preliminary tests. AIR and its extension, the Parsed AIR algorithm (PAIR), were able to reconstruct optimal images compared to filtered backprojection after empirically determining parameters relevant to the algorithms. While GIRA reconstructed optimal images in preliminary tests, reconstruction was complicated by error propagation for larger imaging domains. The initial success of these novel approaches justifies continued research and development to determine their feasibility for practical CT image reconstruction.

scholarly journals New algorithms for computed tomography image reconstruction to eliminate artifacts

2021 ◽  
Author(s):  
Eli Lechtman
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Tomography Image ◽  
Ct Simulation ◽  
Novel Algorithms ◽  
Computed Tomography Image

Computed tomography (CT) relies on computational algorithms to reconstruct images from CT projections. Current filtered backprojection reconstruction methods have inherent limitations in situations with sharp density gradients and limited beam views. In this thesis two novel reconstruction algorithms were introduced: the Algebraic Image Reconstruction (AIR) algorithm, and the Geometric Image Reconstruction Algorithm (GIRA). A CT simulation was developed to test these novel algorithms and compare their images to filtered backprojection images. AIR and GIRA each demonstrated their proof of principle in these preliminary tests. AIR and its extension, the Parsed AIR algorithm (PAIR), were able to reconstruct optimal images compared to filtered backprojection after empirically determining parameters relevant to the algorithms. While GIRA reconstructed optimal images in preliminary tests, reconstruction was complicated by error propagation for larger imaging domains. The initial success of these novel approaches justifies continued research and development to determine their feasibility for practical CT image reconstruction.

scholarly journals Manual kidney stone size measurements in computed tomography are most accurate using multiplanar image reformatations and bone window settings

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Robert Peter Reimer ◽  
Konstantin Klein ◽  
Miriam Rinneburger ◽  
David Zopfs ◽  
Simon Lennartz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Treatment Decision ◽  
Reconstruction Algorithm ◽  
Slice Thickness ◽  
Reconstruction Algorithms ◽  
Stone Size ◽  
Bone Window ◽  
3D Printed ◽  
Measurement Strategies ◽  
Digital Caliper

AbstractComputed tomography in suspected urolithiasis provides information about the presence, location and size of stones. Particularly stone size is a key parameter in treatment decision; however, data on impact of reformatation and measurement strategies is sparse. This study aimed to investigate the influence of different image reformatations, slice thicknesses and window settings on stone size measurements. Reference stone sizes of 47 kidney stones representative for clinically encountered compositions were measured manually using a digital caliper (Man-M). Afterwards stones were placed in a 3D-printed, semi-anthropomorphic phantom, and scanned using a low dose protocol (CTDIvol 2 mGy). Images were reconstructed using hybrid-iterative and model-based iterative reconstruction algorithms (HIR, MBIR) with different slice thicknesses. Two independent readers measured largest stone diameter on axial (2 mm and 5 mm) and multiplanar reformatations (based upon 0.67 mm reconstructions) using different window settings (soft-tissue and bone). Statistics were conducted using ANOVA ± correction for multiple comparisons. Overall stone size in CT was underestimated compared to Man-M (8.8 ± 2.9 vs. 7.7 ± 2.7 mm, p < 0.05), yet closely correlated (r = 0.70). Reconstruction algorithm and slice thickness did not significantly impact measurements (p > 0.05), while image reformatations and window settings did (p < 0.05). CT measurements using multiplanar reformatation with a bone window setting showed closest agreement with Man-M (8.7 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05, r = 0.83). Manual CT-based stone size measurements are most accurate using multiplanar image reformatation with a bone window setting, while measurements on axial planes with different slice thicknesses underestimate true stone size. Therefore, this procedure is recommended when impacting treatment decision.

scholarly journals Evaluation of Algebraic Iterative Image Reconstruction Methods for Tetrahedron Beam Computed Tomography Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-14
Author(s):  
Joshua Kim ◽  
Huaiqun Guan ◽  
David Gersten ◽  
Tiezhi Zhang
Keyword(s):  
Computed Tomography ◽  
Iterative Reconstruction ◽  
Cone Angle ◽  
Iterative Algorithms ◽  
Projection Data ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Large Cone Angle ◽  
Using Data ◽  
Dual Detector

Tetrahedron beam computed tomography (TBCT) performs volumetric imaging using a stack of fan beams generated by a multiple pixel X-ray source. While the TBCT system was designed to overcome the scatter and detector issues faced by cone beam computed tomography (CBCT), it still suffers the same large cone angle artifacts as CBCT due to the use of approximate reconstruction algorithms. It has been shown that iterative reconstruction algorithms are better able to model irregular system geometries and that algebraic iterative algorithms in particular have been able to reduce cone artifacts appearing at large cone angles. In this paper, the SART algorithm is modified for the use with the different TBCT geometries and is tested using both simulated projection data and data acquired using the TBCT benchtop system. The modified SART reconstruction algorithms were able to mitigate the effects of using data generated at large cone angles and were also able to reconstruct CT images without the introduction of artifacts due to either the longitudinal or transverse truncation in the data sets. Algebraic iterative reconstruction can be especially useful for dual-source dual-detector TBCT, wherein the cone angle is the largest in the center of the field of view.

scholarly journals Muon Tracing and Image Reconstruction Algorithms for Cosmic Ray Muon Computed Tomography

2019 ◽  
Vol 28 (1) ◽  
pp. 426-435 ◽  
Author(s):  
Zhengzhi Liu ◽  
Stylianos Chatzidakis ◽  
John M. Scaglione ◽  
Can Liao ◽  
Haori Yang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Cosmic Ray ◽  
Reconstruction Algorithms ◽  
Image Reconstruction Algorithms

Block-matching sparsity regularization-based image reconstruction for low-dose computed tomography

2018 ◽  
Vol 45 (6) ◽  
pp. 2439-2452 ◽  
Author(s):  
Ailong Cai ◽  
Lei Li ◽  
Zhizhong Zheng ◽  
Linyuan Wang ◽  
Bin Yan
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Low Dose ◽  
Block Matching ◽  
Sparsity Regularization ◽  
Low Dose Computed Tomography

scholarly journals A Benchmark Dataset and Deep Learning-Based Image Reconstruction for Electrical Capacitance Tomography

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3701 ◽  
Author(s):  
Jin Zheng ◽  
Jinku Li ◽  
Yi Li ◽  
Lihui Peng
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Benchmark Dataset ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Image Reconstruction Algorithms ◽  
Capacitance Tomography ◽  
The Relationship

Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.

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