scholarly journals Impact of iron deposit on the accuracy of quantifying liver fat fraction using multi‐material decomposition algorithm in dual‐energy spectral computed tomography

2021 ◽  
Author(s):  
Dandan Du ◽  
Xingwang Wu ◽  
Jinchuan Wang ◽  
Hua Chen ◽  
Jian Song ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Dual Energy ◽  
Decomposition Algorithm ◽  
Liver Fat ◽  
Iron Deposit ◽  
Material Decomposition ◽  
Spectral Computed Tomography ◽  
Fat Fraction

Quantification of Cisplatin Using a Modified 3-Material Decomposition Algorithm at Third-Generation Dual-Source Dual-Energy Computed Tomography

2018 ◽  
Vol 53 (11) ◽  
pp. 673-680 ◽  
Author(s):  
Keitaro Sofue ◽  
Toshihide Itoh ◽  
Satoru Takahashi ◽  
Bernhard Schmidt ◽  
Ryuji Shimada ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Dual Energy ◽  
Decomposition Algorithm ◽  
Third Generation ◽  
Material Decomposition ◽  
Dual Energy Computed Tomography ◽  
Dual Source

scholarly journals Dual Energy Computed Tomography of Internal Carotid Artery: A Modified Dual-Energy Algorithm for Calcified Plaque Removal, Compared With Digital Subtraction Angiography

2021 ◽  
Vol 11 ◽  
Author(s):  
Hongying Qu ◽  
Yongan Gao ◽  
Meiling Li ◽  
Shuo Zhai ◽  
Miao Zhang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Carotid Artery ◽  
Internal Carotid Artery ◽  
Internal Carotid ◽  
Dual Energy ◽  
Decomposition Algorithm ◽  
Material Decomposition ◽  
Calcium Removal ◽  
Ica Stenosis ◽  
Modified Algorithm

Background: Atherosclerotic disease of the internal carotid artery (ICA) is a common reason for ischemic stroke. Computed tomography angiography (CTA) is a common tool for evaluation of internal carotid artery (ICA) stenosis. However, blooming artifacts caused by calcified plaques might lead to overestimation of the stenosis grade. Furthermore, the intracranial ICA is more vulnerable to calcification than other ICA segments. The proposed technique, dual-energy computed tomography (DECT) with a modified three-material decomposition algorithm may facilitate the removal of calcified plaques and thus increase diagnostic accuracy.Objectives: The objective of the study is to assess the accuracy of the modified three-material decomposition algorithm for grading intracranial ICA stenosis after calcified plaque removal, with digital subtraction angiography (DSA) used as a reference standard.Materials and Methods: In total, 41 patients underwent DECT angiography and DSA. The three-material decomposition DECT algorithm for calcium removal was applied. We evaluated 64 instances of calcified stenosis using conventional CTA, the previous non-modified calcium removal DECT technique, the modified DECT algorithm, and DSA. The correlation coefficient (r2) between the results generated by the modified algorithm and DSA was also calculated.Results: The virtual non-calcium images (VNCa) produced by the previous non-modified calcium removal algorithm were named VNCa 1, and those produced by the modified algorithm were named VNCa 2. The assigned degree of stenosis of VNCa 1 (mean stenosis: 39.33 ± 19.76%) differed significantly from that of conventional CTA images (mean stenosis: 59.03 ± 25.96%; P = 0.001), DSA (13.19 ± 17.12%, P < 0.001). VNCa 1 also significantly differed from VNCa 2 (mean stenosis: 15.35 ± 18.70%, P < 0.001). In addition, there was a significant difference between the degree of stenosis of VNCa 2 and conventional CTA images (P < 0.001). No significant differences were observed between VNCa 2 and DSA (P = 0.076). The correlation coefficient (r2) between the stenosis degree of the VNCa 2 and DSA images was 0.991.Conclusions: The proposed DECT with a modified three-material decomposition algorithm for calcium removal has high sensitivity for the detection of relevant stenoses, and its results were more strongly correlated with DSA than with those of conventional CTA or the previous non-modified algorithm. Further, it overcomes CTA's previous problem of overestimating the degree of stenosis because of blooming artifacts caused by calcified plaques. It is useful to account for calcified plaques while evaluating carotid stenosis.

Clinical Application of Dual-Energy Spectral Computed Tomography in Detecting Cholesterol Gallstones From Surrounding Bile

2017 ◽  
Vol 24 (4) ◽  
pp. 478-482 ◽  
Author(s):  
Chuang-bo Yang ◽  
Shuang Zhang ◽  
Yong-jun Jia ◽  
Hai-feng Duan ◽  
Guang-ming Ma ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Clinical Application ◽  
Dual Energy ◽  
Cholesterol Gallstones ◽  
Spectral Computed Tomography

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

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.

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