Optimization For 3D Reconstruction Of Coronary Artery Tree By Two-stage Levenberg-Marquardt Algorithm

2021 ◽  
Author(s):  
Zeyu Fu ◽  
Zhuang Fu ◽  
Zening Gong ◽  
Xin Feng ◽  
Haoran Gu ◽  
...  
Keyword(s):  
Coronary Artery ◽  
3D Reconstruction ◽  
Two Stage ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Coronary Artery Tree

scholarly journals 3D Reconstruction with Coronary Artery Based on Curve Descriptor and Projection Geometry-Constrained Vasculature Matching

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 38
Author(s):  
Jijun Tong ◽  
Shuai Xu ◽  
Fangliang Wang ◽  
Pengjia Qi
Keyword(s):  
Coronary Artery ◽  
3D Reconstruction ◽  
Trust Region Method ◽  
Trust Region ◽  
Geometric Transformation ◽  
Error Matrix ◽  
Point Pair ◽  
Marquardt Algorithm ◽  
Projection Geometry ◽  
Point Set

This paper presents a novel method based on a curve descriptor and projection geometry constrained for vessel matching. First, an LM (Leveberg–Marquardt) algorithm is proposed to optimize the matrix of geometric transformation. Combining with parameter adjusting and the trust region method, the error between 3D reconstructed vessel projection and the actual vessel can be minimized. Then, CBOCD (curvature and brightness order curve descriptor) is proposed to indicate the degree of the self-occlusion of blood vessels during angiography. Next, the error matrix constructed from the error of epipolar matching is used in point pairs matching of the vascular through dynamic programming. Finally, the recorded radius of vessels helps to construct ellipse cross-sections and samples on it to get a point set around the centerline and the point set is converted to mesh for reconstructing the surface of vessels. The validity and applicability of the proposed methods have been verified through experiments that result in the significant improvement of 3D reconstruction accuracy in terms of average back-projection errors. Simultaneously, due to precise point-pair matching, the smoothness of the reconstructed 3D coronary artery is guaranteed.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

scholarly journals A Voxel-Map Quantitative Analysis Approach for Atherosclerotic Noncalcified Plaques of the Coronary Artery Tree

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Ying Li ◽  
Wei Chen ◽  
Kaijun Liu ◽  
Yi Wu ◽  
Yonglin Chen ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Three Dimensional ◽  
Coronary Intervention ◽  
Quantitative Information ◽  
Atherosclerotic Plaques ◽  
Anatomical Location ◽  
Contrast Enhanced ◽  
Percutaneous Coronary ◽  
Map Analysis ◽  
Coronary Artery Tree

Noncalcified plaques (NCPs) are associated with the presence of lipid-core plaques that are prone to rupture. Thus, it is important to detect and monitor the development of NCPs. Contrast-enhanced coronary Computed Tomography Angiography (CTA) is a potential imaging technique to identify atherosclerotic plaques in the whole coronary tree, but it fails to provide information about vessel walls. In order to overcome the limitations of coronary CTA and provide more meaningful quantitative information for percutaneous coronary intervention (PCI), we proposed a Voxel-Map based on mathematical morphology to quantitatively analyze the noncalcified plaques on a three-dimensional coronary artery wall model (3D-CAWM). This approach is a combination of Voxel-Map analysis techniques, plaque locating, and anatomical location related labeling, which show more detailed and comprehensive coronary tree wall visualization.

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