K-space imaging algorithms applied to UWB SAR

2002 ◽  
Author(s):  
S. Cloude ◽  
A. Milne ◽  
P.D. Smith ◽  
C. Thornhill ◽  
G. Crisp
Keyword(s):  
Imaging Algorithms

PEMBELAJARAN SIMULASI PENCITRAAN CT DENGAN MENGGUNAKAN PRINSIP REKONSTRUKSI CITRA DALAM SOFTWARE MATLAB

2015 ◽  
Vol 8 (3) ◽  
pp. 161
Author(s):  
Samuel Gideon
Keyword(s):  
Image Reconstruction ◽  
Ct Imaging ◽  
The Body ◽  
Reconstruction Technique ◽  
Imaging Features ◽  
Back Projection ◽  
Cross Sectional ◽  
Low Contrast ◽  
Image Reconstruction Technique ◽  
Imaging Algorithms

This research was conducted as a learning alternatives for study of CT (computed tomograpghy) imaging using image reconstruction technique which are inversion matrix, back projection and filtered back projection. CT imaging can produce images of objects that do not overlap. Objects more easily distinguishable although given the relatively low contrast. The image is generated on CT imaging is the result of reconstruction of the original object. Matlab allows us to create and write imaging algorithms easily, easy to undersand and gives applied and exciting other imaging features. In this study, an example cross-sectional image recon-struction performed on the body of prostate tumors using. With these methods, medical prac-titioner (such as oncology clinician, radiographer and medical physicist) allows to simulate the reconstruction of CT images which almost resembles the actual CT visualization techniques.Keywords : computed tomography (CT), image reconstruction, Matlab

Subwavelength Defect Characterization Using Guided Wave Scattering Matrix

2013 ◽  
Vol 330 ◽  
pp. 504-509
Author(s):  
Yang Zheng ◽  
Jin Jie Zhou ◽  
Hui Zheng
Keyword(s):  
Wave Scattering ◽  
Scattering Matrix ◽  
Guided Wave ◽  
Defect Characterization ◽  
Challenging Problem ◽  
Recognition Method ◽  
Quantitative Characterization ◽  
S Matrix ◽  
Imaging Algorithms

Although many imaging algorithms such as ellipse and hyperbola algorithm can roughly locate defects in large plate-like structures with sparse guided wave arrays, quantitative characterization of them is still a challenging problem, especially for those small defects known as subwavelength defects. Scattering signals of defects contain abundant information so that can be used to evaluate defects. A defects recognition method using the S-matrix (scattering matrix) was presented. S-matrices of hole and crack with S0 mode incident were experimentally measured. The results show that defects can be recognized from the morphology of 2D S-matrix chart. This method has great potential to achieve more specific parameters of small defects with sparse guided wave arrays.

Potential and Limitations of Various Ultrasonic Medical Imaging Algorithms

1992 ◽  
pp. 29-33
Author(s):  
H. Morbitzer ◽  
D. Huo ◽  
K. J. Langenberg ◽  
R. M. Schmitt
Keyword(s):  
Medical Imaging ◽  
Imaging Algorithms

scholarly journals A Multiplicative Regularizer Augmented with Spatial Priors for Microwave Imaging

2020 ◽  
Author(s):  
Nozhan Bayat ◽  
Puyan Mojabi
Keyword(s):  
Experimental Data ◽  
Total Variation ◽  
Prior Information ◽  
Microwave Imaging ◽  
Minimal Change ◽  
Data Sets ◽  
Quantitative Accuracy ◽  
L2 Norm ◽  
Imaging Algorithms ◽  
Multiplicative Regularization

The standard weighted L2 norm total variation multiplicative regularization (MR) term originally developed for microwave imaging algorithms is modified to take into account<br>structural prior information, also known as spatial priors (SP), about the object being imaged. This modification adds one extra term to the integrand of the standard MR, thus, being referred to as an augmented MR (AMR). The main advantage of the proposed approach is that it requires a minimal change to the existing microwave imaging algorithms that are already equipped with the MR. Using two experimental data sets, it is shown that the proposed AMR (i) can handle partial SP, and (ii) can, to some extent, enhance the quantitative accuracy achievable from<br>microwave imaging.

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