Minimum-Norm-Based Source Imaging Algorithms

2015 ◽  
pp. 9-28
Author(s):  
Kensuke Sekihara ◽  
Srikantan S. Nagarajan
Keyword(s):  
Minimum Norm ◽  
Source Imaging ◽  
Imaging Algorithms

Magnetic source imaging based on the Minimum-Norm Least-Squares Inverse

1993 ◽  
Vol 5 (4) ◽  
pp. 365-371 ◽  
Author(s):  
Jia-Zhu Wang ◽  
Samuel J. Williamson ◽  
Lloyd Kaufman
Keyword(s):  
Least Squares ◽  
Minimum Norm ◽  
Source Imaging ◽  
Magnetic Source Imaging ◽  
Magnetic Source

scholarly journals MEG source imaging method using fast L1 minimum-norm and its applications to signals with brain noise and human resting-state source amplitude images

NeuroImage ◽  
2014 ◽  
Vol 84 ◽  
pp. 585-604 ◽  
Author(s):  
Ming-Xiong Huang ◽  
Charles W. Huang ◽  
Ashley Robb ◽  
AnneMarie Angeles ◽  
Sharon L. Nichols ◽  
...  
Keyword(s):  
Resting State ◽  
Imaging Method ◽  
Minimum Norm ◽  
Source Imaging

Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm

1995 ◽  
Vol 95 (4) ◽  
pp. 231-251 ◽  
Author(s):  
Irina F. Gorodnitsky ◽  
John S. George ◽  
Bhaskar D. Rao
Keyword(s):  
Minimum Norm ◽  
Source Imaging

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

Sign in / Sign up

Export Citation Format

Share Document