Image-domain multi-material decomposition for dual-energy CT with non-convex sparsity regularization

2019 ◽  
Author(s):  
Qihui Lyu ◽  
Daniel O'Connor ◽  
Tianye Niu ◽  
Ke Sheng
Keyword(s):  
Dual Energy ◽  
Dual Energy Ct ◽  
Material Decomposition ◽  
Image Domain ◽  
Sparsity Regularization

Noise Suppression in Image-Domain Multi-Material Decomposition for Dual-Energy CT

2020 ◽  
Vol 67 (2) ◽  
pp. 523-535 ◽  
Author(s):  
Yangkang Jiang ◽  
Xiaoqun Zhang ◽  
Ke Sheng ◽  
Tianye Niu ◽  
Yi Xue ◽  
...  
Keyword(s):  
Noise Suppression ◽  
Dual Energy ◽  
Dual Energy Ct ◽  
Material Decomposition ◽  
Image Domain

scholarly journals Quantification of contrast agent materials using a new image- domain multi material decomposition algorithm based on dual energy CT

BJR|Open ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 20180008
Author(s):  
Fazel Mirzaei ◽  
Reza Faghihi
Keyword(s):  
Contrast Agent ◽  
Imaging Modality ◽  
Dual Energy ◽  
Decomposition Algorithm ◽  
Barycentric Coordinates ◽  
Dual Energy Ct ◽  
Material Decomposition ◽  
Clinical Images ◽  
Image Domain ◽  
Local Clustering

Objective: Dual-Energy CT (DECT) is an imaging modality in which the objects are scanned by two different energy spectra. Using these two measurements, two type of materials can be separated and density image pairs can be generated as well. Decomposing more than two materials is necessary in both clinical and industrial CT applications. Methods: In our MMD, barycentric coordinates were chosen using an innovative local clustering method. Local clustering increases precision in the barycentric coordinates assignment by decreasing search domain. Therefore the algorithm can be run in parallel. For optimizing coordinates selection, a fast bi-directional Hausdorff distance measurement is used. To deal with the significant obstacle of noise, we used Doubly Local Wiener Filter Directional Window (DLWFDW) algorithm. Results: Briefly, the proposed algorithm separates blood and fat ROIs with errors of less than 2 and 9 % respectively on the clinical images. Also, the ability to decompose different materials with different concentrations is evaluated employing the phantom data. The highest accuracy obtained in separating different materials with different concentrations was 93 % (for calcium plaque) and 97.1 % (for iodine contrast agent) respectively. The obtained results discussed in detail in the following results section. Conclusion: In this study, we propose a new material decomposition algorithm. It improves the MMD work flow by employing tools which are easy to implement. Furthermore, in this study, an effort has been made to turn the MMD algorithm into a semi-automatic algorithm by employing clustering concept in material coordinate’s assignment. The performance of the proposed method is comparable to existing methods from qualitative and quantitative aspects. Advances in knowledge: All decomposition methods have their own specific problems. Image- domain decomposition also has barriers and problems, including the need for a predetermined table for the separation of different materials with specified coordinates. In the present study, it attempts to solve this problem by using clustering methods and relying on the intervals between different materials in the attenuation domain.

scholarly journals Using edge-preserving algorithm with non-local mean for significantly improved image-domain material decomposition in dual-energy CT

2016 ◽  
Vol 61 (3) ◽  
pp. 1332-1351 ◽  
Author(s):  
Wei Zhao ◽  
Tianye Niu ◽  
Lei Xing ◽  
Yaoqin Xie ◽  
Guanglei Xiong ◽  
...  
Keyword(s):  
Dual Energy ◽  
Dual Energy Ct ◽  
Material Decomposition ◽  
Edge Preserving ◽  
Image Domain ◽  
Local Mean ◽  
Non Local

MO-FG-204-03: Using Edge-Preserving Algorithm for Significantly Improved Image-Domain Material Decomposition in Dual Energy CT

2015 ◽  
Vol 42 (6Part29) ◽  
pp. 3569-3569
Author(s):  
W Zhao ◽  
T Niu ◽  
L Xing ◽  
G Xiong ◽  
K Elmore ◽  
...  
Keyword(s):  
Dual Energy ◽  
Dual Energy Ct ◽  
Material Decomposition ◽  
Edge Preserving ◽  
Image Domain

Electronic Cleansing in Fecal-Tagging Dual-Energy CT Colonography Based on Material Decomposition and Virtual Colon Tagging

2015 ◽  
Vol 62 (2) ◽  
pp. 754-765 ◽  
Author(s):  
Wenli Cai ◽  
June-Goo Lee ◽  
Da Zhang ◽  
Se Hyung Kim ◽  
Michael Zalis ◽  
...  
Keyword(s):  
Ct Colonography ◽  
Dual Energy ◽  
Dual Energy Ct ◽  
Material Decomposition ◽  
Fecal Tagging
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