A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography

2013 ◽  
Vol 40 (11) ◽  
pp. 111916 ◽  
Author(s):  
C. Cai ◽  
T. Rodet ◽  
S. Legoupil ◽  
A. Mohammad-Djafari
Keyword(s):  
Computed Tomography ◽  
Dual Energy ◽  
Material Decomposition ◽  
Bayesian Reconstruction ◽  
Dual Energy Computed Tomography ◽  
Decomposition Model

Improvement with the Multi-material Decomposition Framework in Dual-energy Computed Tomography: A Phantom Study

2020 ◽  
Vol 77 (6) ◽  
pp. 515-523
Author(s):  
Haenghwa Lee ◽  
Hee-Joung Kim ◽  
Donghoon Lee ◽  
Dohyeon Kim ◽  
Seungyeon Choi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Dual Energy ◽  
Phantom Study ◽  
Material Decomposition ◽  
Dual Energy Computed Tomography

Dual-Energy Computed Tomography with Material Decomposition: Before and after Thrombolysis in Massive PTE

2018 ◽  
Vol 31 (1) ◽  
Author(s):  
Paula de Castro Carvalho Gorgulho ◽  
Bruno Soares da Silva Rangel ◽  
Sicilia Pacheco e Silva ◽  
Ilan Gottlieb ◽  
Gustavo Luiz Gouvêa de Almeida Junior
Keyword(s):  
Computed Tomography ◽  
Dual Energy ◽  
Material Decomposition ◽  
Dual Energy Computed Tomography ◽  
Before And After

Plantar Plate Tear Diagnosis Using Dual-Energy Computed Tomography Collagen Material Decomposition Application

2013 ◽  
Vol 37 (3) ◽  
pp. 478-480 ◽  
Author(s):  
Christopher J. Stevens ◽  
Darra T. Murphy ◽  
Jeffrey R. Korzan ◽  
Savvas Nicolaou ◽  
Peter L. Munk ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Dual Energy ◽  
Material Decomposition ◽  
Dual Energy Computed Tomography ◽  
Plantar Plate

scholarly journals Image Decomposition Algorithm for Dual-Energy Computed Tomography via Fully Convolutional Network

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yifu Xu ◽  
Bin Yan ◽  
Jingfang Zhang ◽  
Jian Chen ◽  
Lei Zeng ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Dual Energy ◽  
Feature Representation ◽  
Basic Material ◽  
Neural Net ◽  
Material Decomposition ◽  
Convolutional Network ◽  
Decomposition Problem ◽  
Dual Energy Computed Tomography ◽  
Noise Robust

Background. Dual-energy computed tomography (DECT) has been widely used due to improved substances identification from additional spectral information. The quality of material-specific image produced by DECT attaches great importance to the elaborated design of the basis material decomposition method. Objective. The aim of this work is to develop and validate a data-driven algorithm for the image-based decomposition problem. Methods. A deep neural net, consisting of a fully convolutional net (FCN) and a fully connected net, is proposed to solve the material decomposition problem. The former net extracts the feature representation of input reconstructed images, and the latter net calculates the decomposed basic material coefficients from the joint feature vector. The whole model was trained and tested using a modified clinical dataset. Results. The proposed FCN delivers image with about 60% smaller bias and 70% lower standard deviation than the competing algorithms, suggesting its better material separation capability. Moreover, FCN still yields excellent performance in case of photon noise. Conclusions. Our deep cascaded network features high decomposition accuracies and noise robust property. The experimental results have shown the strong function fitting ability of the deep neural network. Deep learning paradigm could be a promising way to solve the nonlinear problem in DECT.

Achilles Tendinopathy and Partial Tear Diagnosis Using Dual-Energy Computed Tomography Collagen Material Decomposition Application

2013 ◽  
Vol 37 (3) ◽  
pp. 475-477 ◽  
Author(s):  
Paul I. Mallinson ◽  
Chris Stevens ◽  
Clemens Reisinger ◽  
Savvas Nicolaou ◽  
Peter L. Munk ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Achilles Tendinopathy ◽  
Dual Energy ◽  
Material Decomposition ◽  
Dual Energy Computed Tomography ◽  
Partial Tear
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