Assessment of Quality of Fetal Heart Views by 3D/4D Ultrasonography Using Spatio-Temporal Image Correlation in the Second and Third Trimesters of Pregnancy

2014 ◽  
Vol 32 (6) ◽  
pp. 1015-1021 ◽  
Author(s):  
Luciane Alves Rocha ◽  
Liliam Cristine Rolo ◽  
Fernanda Silveira Bello Barros ◽  
Luciano Marcondes Machado Nardozza ◽  
Antonio Fernandes Moron ◽  
...  
2003 ◽  
Vol 22 (4) ◽  
pp. 380-387 ◽  
Author(s):  
G. R. DeVore ◽  
P. Falkensammer ◽  
M. S. Sklansky ◽  
L. D. Platt

2014 ◽  
Vol 25 (1) ◽  
pp. 59-72
Author(s):  
JIMMY ESPINOZA

Spatio-temporal image correlation (STIC) is a feature of four-dimensional ultrasonography (4D US) that allows the acquisition of volume datasets akin to blocks of pathological specimens, where all the anatomical information is contained in the block and the information displayed depends on the level at which the block is cut. STIC has the additional advantages that these planes can be assessed in a virtual beating heart, and that rendering techniques can be used to gain additional insight into the structure and function of the fetal heart.


2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.


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