scholarly journals Prior Image Guided Undersampled Dual Energy Reconstruction with Piecewise Polynomial Function Constraint

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Dufan Wu ◽  
Li Zhang ◽  
Liang Li ◽  
Le Shen ◽  
Yuxiang Xing
Keyword(s):  
Structural Information ◽  
Attenuation Factor ◽  
Sampling Rate ◽  
High Energy ◽  
Dual Energy ◽  
Low Energy ◽  
Dual Energy Ct ◽  
Piecewise Polynomial ◽  
Piecewise Polynomial Function ◽  
Factor Difference

Dual energy CT has the ability to give more information about the test object by reconstructing the attenuation factors under different energies. These images under different energies share identical structures but different attenuation factors. By referring to the fully sampled low-energy image, we show that it is possible to greatly reduce the sampling rate of the high-energy image in order to lower dose. To compensate the attenuation factor difference between the two modalities, we use piecewise polynomial fitting to fit the low-energy image to the high-energy image. During the reconstruction, the result is constrained by its distance to the fitted image, and the structural information thus can be preserved. An ASD-POCS-based optimization schedule is proposed to solve the problem, and numerical simulations are taken to verify the algorithm.

scholarly journals Combination of Dual-Energy X-ray Transmission and Variable Gas-Ejection for the In-Line Automatic Sorting of Many Types of Scrap in One Measurement

2021 ◽  
Vol 11 (10) ◽  
pp. 4349
Author(s):  
Tianzhong Xiong ◽  
Wenhua Ye ◽  
Xiang Xu
Keyword(s):  
High Efficiency ◽  
Nearest Neighbor ◽  
High Energy ◽  
Dual Energy ◽  
Center Of Gravity ◽  
Low Energy ◽  
Identification Accuracy ◽  
Flow Process ◽  
X Ray ◽  
Automatic Sorting

As an important part of pretreatment before recycling, sorting has a great impact on the quality, efficiency, cost and difficulty of recycling. In this paper, dual-energy X-ray transmission (DE-XRT) combined with variable gas-ejection is used to improve the quality and efficiency of in-line automatic sorting of waste non-ferrous metals. A method was proposed to judge the sorting ability, identify the types, and calculate the mass and center-of-gravity coordinates according to the shading of low-energy, the line scan direction coordinate and transparency natural logarithm ratio of low energy to high energy (R_value). The material identification was satisfied by the nearest neighbor algorithm of effective points in the material range to the R_value calibration surface. The flow-process of identification was also presented. Based on the thickness of the calibration surface, the material mass and center-of-gravity coordinates were calculated. The feasibility of controlling material falling points by variable gas-ejection was analyzed. The experimental verification of self-made materials showed that identification accuracy by count basis was 85%, mass and center-of-gravity coordinates calculation errors were both below 5%. The method proposed features high accuracy, high efficiency, and low operation cost and is of great application value even to other solid waste sorting, such as plastics, glass and ceramics.

Dose heterogeneity correction for low-energy brachytherapy sources using dual-energy CT images

2014 ◽  
Vol 59 (18) ◽  
pp. 5305-5316 ◽  
Author(s):  
S Mashouf ◽  
E Lechtman ◽  
P Lai ◽  
B M Keller ◽  
A Karotki ◽  
...  
Keyword(s):  
Ct Images ◽  
Dual Energy ◽  
Low Energy ◽  
Dual Energy Ct ◽  
Heterogeneity Correction

Dosimetric impact of dual-energy CT tissue segmentation for low-energy prostate brachytherapy: a Monte Carlo study

2018 ◽  
Vol 63 (2) ◽  
pp. 025013 ◽  
Author(s):  
Charlotte Remy ◽  
Arthur Lalonde ◽  
Dominic Béliveau-Nadeau ◽  
Jean-François Carrier ◽  
Hugo Bouchard
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Dual Energy ◽  
Low Energy ◽  
Dual Energy Ct ◽  
Prostate Brachytherapy ◽  
Tissue Segmentation

scholarly journals Modified kernel MLAA using autoencoder for PET-enabled dual-energy CT

2021 ◽  
Vol 379 (2204) ◽  
pp. 20200204 ◽  
Author(s):  
Siqi Li ◽  
Guobao Wang
Keyword(s):  
Image Reconstruction ◽  
Kernel Method ◽  
High Energy ◽  
Dual Energy ◽  
Feature Representation ◽  
Dual Energy Ct ◽  
Ct Image ◽  
Material Decomposition ◽  
X Ray ◽  
Image Prior

Combined use of PET and dual-energy CT provides complementary information for multi-parametric imaging. PET-enabled dual-energy CT combines a low-energy X-ray CT image with a high-energy γ -ray CT (GCT) image reconstructed from time-of-flight PET emission data to enable dual-energy CT material decomposition on a PET/CT scanner. The maximum-likelihood attenuation and activity (MLAA) algorithm has been used for GCT reconstruction but suffers from noise. Kernel MLAA exploits an X-ray CT image prior through the kernel framework to guide GCT reconstruction and has demonstrated substantial improvements in noise suppression. However, similar to other kernel methods for image reconstruction, the existing kernel MLAA uses image intensity-based features to construct the kernel representation, which is not always robust and may lead to suboptimal reconstruction with artefacts. In this paper, we propose a modified kernel method by using an autoencoder convolutional neural network (CNN) to extract an intrinsic feature set from the X-ray CT image prior. A computer simulation study was conducted to compare the autoencoder CNN-derived feature representation with raw image patches for evaluation of kernel MLAA for GCT image reconstruction and dual-energy multi-material decomposition. The results show that the autoencoder kernel MLAA method can achieve a significant image quality improvement for GCT and material decomposition as compared to the existing kernel MLAA algorithm. A weakness of the proposed method is its potential over-smoothness in a bone region, indicating the importance of further optimization in future work. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

scholarly journals Dual-energy CT characterization of winter sports injuries

2020 ◽  
Vol 93 (1106) ◽  
pp. 20190620
Author(s):  
Jonathan Hickle ◽  
Frances Walstra ◽  
Peter Duggan ◽  
Hugue Ouellette ◽  
Peter Munk ◽  
...  
Keyword(s):  
High Speed ◽  
Sports Injuries ◽  
Imaging Modality ◽  
High Energy ◽  
Dual Energy ◽  
Dual Energy Ct ◽  
Cross Sectional ◽  
Winter Sports ◽  
Conventional Ct

CT is a readily available imaging modality for cross-sectional characterization of acute musculoskeletal injuries in trauma. Dual-energy CT provides several additional benefits over conventional CT, namely assessment for bone marrow edema, metal artifact reduction, and enhanced assessment of ligamentous injuries. Winter sports such as skiing, snowboarding, and skating can result in high speed and high energy injury mechanisms; dual-energy CT is well suited for the characterization of those injuries.

Simulation study on potential accuracy gains from dual energy CT tissue segmentation for low-energy brachytherapy Monte Carlo dose calculations

2011 ◽  
Vol 56 (19) ◽  
pp. 6257-6278 ◽  
Author(s):  
Guillaume Landry ◽  
Patrick V Granton ◽  
Brigitte Reniers ◽  
Michel C Öllers ◽  
Luc Beaulieu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Simulation Study ◽  
Dual Energy ◽  
Low Energy ◽  
Dual Energy Ct ◽  
Dose Calculations ◽  
Tissue Segmentation ◽  
Potential Accuracy

scholarly journals Virtual Monoenergetic Images of Dual-Energy CT—Impact on Repeatability, Reproducibility, and Classification in Radiomics

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4710
Author(s):  
André Euler ◽  
Fabian Christopher Laqua ◽  
Davide Cester ◽  
Niklas Lohaus ◽  
Thomas Sartoretti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Penalized Regression ◽  
High Energy ◽  
Dual Energy ◽  
Dual Energy Ct ◽  
Virtual Monoenergetic Images ◽  
Repeatability And Reproducibility ◽  
The Impact ◽  
Monoenergetic Images

The purpose of this study was to (i) evaluate the test–retest repeatability and reproducibility of radiomic features in virtual monoenergetic images (VMI) from dual-energy CT (DECT) depending on VMI energy (40, 50, 75, 120, 190 keV), radiation dose (5 and 15 mGy), and DECT approach (dual-source and split-filter DECT) in a phantom (ex vivo), and (ii) to assess the impact of VMI energy and feature repeatability on machine-learning-based classification in vivo in 72 patients with 72 hypodense liver lesions. Feature repeatability and reproducibility were determined by concordance–correlation–coefficient (CCC) and dynamic range (DR) ≥0.9. Test–retest repeatability was high within the same VMI energies and scan conditions (percentage of repeatable features ranging from 74% for SFDE mode at 40 keV and 15 mGy to 86% for DSDE at 190 keV and 15 mGy), while reproducibility varied substantially across different VMI energies and DECTs (percentage of reproducible features ranging from 32.8% for SFDE at 5 mGy comparing 40 with 190 keV to 99.2% for DSDE at 15 mGy comparing 40 with 50 keV). No major differences were observed between the two radiation doses (<10%) in all pair-wise comparisons. In vivo, machine learning classification using penalized regression and random forests resulted in the best discrimination of hemangiomas and metastases at low-energy VMI (40 keV), and for cysts at high-energy VMI (120 keV). Feature selection based on feature repeatability did not improve classification performance. Our results demonstrate the high repeatability of radiomics features when keeping scan and reconstruction conditions constant. Reproducibility diminished when using different VMI energies or DECT approaches. The choice of optimal VMI energy improved lesion classification in vivo and should hence be adapted to the specific task.

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