Real-time ultrasound image reconstruction as an inverse problem on a GPU

2018 ◽  
Vol 17 (3) ◽  
pp. 543-554
Author(s):  
Paulo R. Bueno ◽  
Marcelo V. W. Zibetti ◽  
Joaquim M. Maia
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Real Time ◽  
Ultrasound Image

scholarly journals GPU-Based Real-Time Volumetric Ultrasound Image Reconstruction for a Ring Array

2013 ◽  
Vol 32 (7) ◽  
pp. 1258-1264 ◽  
Author(s):  
Jung Woo Choe ◽  
A. Nikoozadeh ◽  
O. Oralkan ◽  
B. T. Khuri-Yakub
Keyword(s):  
Image Reconstruction ◽  
Real Time ◽  
Ultrasound Image ◽  
Volumetric Ultrasound

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

scholarly journals Quantitative imaging and automated fuel pin identification for passive gamma emission tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ming Fang ◽  
Yoann Altmann ◽  
Daniele Della Latta ◽  
Massimiliano Salvatori ◽  
Angela Di Fulvio
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Image Resolution ◽  
Emission Tomography ◽  
Nuclear Safeguards ◽  
Activity Levels ◽  
Spent Fuel ◽  
Gamma Emission ◽  
Cross Sectional ◽  
Fuel Rods

AbstractCompliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JNT 1510, which included the European Commission, Finland, Hungary and Sweden. The PGET is used for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. While the former is mitigated by detector physical collimation, we implemented a linear forward model to model the detector responses to the fuel rods inside the PGET, to account for the latter. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection (FBP) method based on the inverse Radon transform for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network (CNN) to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.

A Real-Time, GPU-Based Implementation of Aperture Domain Model Image REconstruction

2021 ◽  
pp. 1-1
Author(s):  
Christopher Khan ◽  
Kazuyuki Dei ◽  
Siegfried Schlunk ◽  
Kathryn Ozgun ◽  
Brett Byram
Keyword(s):  
Image Reconstruction ◽  
Real Time ◽  
Domain Model ◽  
Model Image
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