ART-TV Algorithm for Diffuse Correlation Tomography Blood Flow Imaging

Zhang, Xiaojuan; Zhai, Lihong; Wang, Jianguo; Guo-hong, Lou; Shang, Yu; Gui, Zhiguo

doi:10.1109/access.2020.3009991

Cited by 5 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The traditional algorithms mainly include the Gauss–Newton algorithm [ 12 ], the Landweber iterative algorithm [ 13 ] and the Tikhonov regularization algorithm [ 14 ]. Zhang et al [ 15 ] proposed a combination of algebra reconstruction technique (ART) and total variation (TV) for the image reconstruction of diffuse correlation imaging (DCT). Sun et al [ 16 ] used an improved Tikhonov algorithm for lung cancer monitoring in electrical impedance tomography (EIT).…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction

Yang

Wang

Bin

et al. 2021

Entropy

View full text Add to dashboard Cite

Arterial stenosis will reduce the blood flow to various organs or tissues, causing cardiovascular diseases. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early cardiovascular disease prediction and monitoring due to their high cost and complex operation. In this paper, we studied the electromagnetic effect of arterial blood flow and proposed a method based on the deep neural network for arterial blood flow profile reconstruction. The potential difference and weight matrix are used as inputs to the method, and its output is an estimate of the internal blood flow velocity distribution for arterial blood flow profile reconstruction. Firstly, the weight matrix is input into the convolutional auto-encode (CAE) network to extract its features. Then, the weight matrix features and potential difference are combined to obtain the features of the blood velocity distribution. Finally, the velocity features are reconstructed into blood flow velocity distribution by a convolution neural network (CNN). All data sets are obtained from a model of the carotid artery with different rates of stenosis in a uniform magnetic field by COMSOL. The results show that the average root mean square error of the reconstruction results obtained by the proposed method is 0.0333, and the average correlation coefficient is 0.9721, which is better than the corresponding indicators of the Tikhonov, back propagation (BP) and CNN methods. The simulation results show that the proposed method can achieve high accuracy in blood flow profile reconstruction and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases.

show abstract

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction

Yang

Wang

Bin

et al. 2021

Entropy

View full text Add to dashboard Cite

show abstract

A non-axisymmetric temperature field reconstruction method based on the compressive sensing background oriented Schlieren method

Wu,

Pan,

Zhang

et al. 2024

Physics of Fluids

View full text Add to dashboard Cite

Measurement of the combustion temperature field is an extremely important issue in industrial production. Temperature is one of the key parameters in combustion studies. With the temperature field distribution of the combustion field obtained, heat transfer, heat convection, and heat radiation can be calculated directly and efficiently. Traditional background oriented Schlieren (BOS) is an effective method for non-axisymmetric temperature field measurements, but it requires simultaneous Schlieren imaging at multiple angles for tomographic reconstruction, which will greatly limit its application. In this paper, the compressive sensing algorithm is introduced into the temperature field reconstruction, which establishes the system of equations between the deflection angle and the refractive index gradient. Then, the reconstruction of the non-axisymmetric temperature field is realized by solving the underdetermined system of equations by the method of solving the sparse solution through the compressive sensing. First, light offsets across the non-axisymmetric temperature field are measured by the under-angled BOS system and image processing method. Second, the spatial refractive index field is reconstructed by the compressive sensing BOS method proposed in this paper. Finally, the spatial temperature field is obtained. The experimental results show that by comparing the iRadon reconstruction algorithm and the compressive sensing reconstruction algorithm, the temperature field reconstructed by the compressive sensing under the condition of the under-angled sampling of projection data had a higher accuracy than that reconstructed by the tomographic reconstruction algorithm under the same condition. The average error of the temperature field was reduced from 34.6 to 29.7 K under the same measurement conditions; however, the accuracy is better maintained by using the compressive sensing algorithm under the condition of undersampling projection.

show abstract