Image reconstruction from real scattering data using an iterative scheme with incorporated a priori information

1999 ◽  
Vol 41 (2) ◽  
pp. 33-36 ◽  
Author(s):  
W. Rieger ◽  
M. Haas ◽  
C. Huber ◽  
G. Lehner ◽  
W.M. Rucker
Keyword(s):  
Image Reconstruction ◽  
Iterative Scheme ◽  
A Priori ◽  
Scattering Data ◽  
A Priori Information ◽  
Priori Information

Using Deconvolution to Improve PET Spatial Resolution in OSEM Iterative Reconstruction

2007 ◽  
Vol 46 (02) ◽  
pp. 231-235 ◽  
Author(s):  
I. Castiglioni ◽  
G. Russo ◽  
M. Tana ◽  
F. Dell'Acqua ◽  
M. Gilardi ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Spatial Resolution ◽  
Penalty Function ◽  
Iterative Reconstruction ◽  
A Priori ◽  
Image Deconvolution ◽  
A Priori Information ◽  
Novel Approach ◽  
Priori Information ◽  
Pet Image Reconstruction

Summary Objectives : A novel approach to the PET image reconstruction is presented, based on the inclusion of image deconvolution during conventional OSEM reconstruction. Deconvolution is here used to provide a recovered PET image to be included as “a priori" information to guide OSEM toward an improved solution. Methods : Deconvolution was implemented using the Lucy-Richardson (LR) algorithm: Two different deconvolution schemes were tested, modifying the conventional OSEM iterative formulation: 1) We built a regularizing penalty function on the recovered PET image obtained by deconvolution and included i in the OSEM iteration. 2) After each conventional global OSEM iteration, we deconvolved the resulting PET image and used this “recovered" version as the initialization image for the next OSEM iteration. Tests were performed on both simulated and acquired data. Results : Compared to the conventional OSEM, both these strategies, applied to simulated and acquired data, showed an improvement in image spatial resolution with better behavior in the second case. In this way, small lesions, present on data, could be better discriminated in terms of contrast. Conclusions : Application of this approach to both simulated and acquired data suggests its efficacy in obtaining PET images of enhanced quality.

Image processing and image reconstruction with the use of a-priori information

1990 ◽  
Author(s):  
Chin-Tu Chen ◽  
Valen E. Johnson ◽  
Xiaoping Hu ◽  
Wing H. Wong ◽  
Charles E. Metz
Keyword(s):  
Image Processing ◽  
Image Reconstruction ◽  
A Priori ◽  
A Priori Information ◽  
Priori Information

A Priori Information in Image Reconstruction

2003 ◽  
pp. 477-483 ◽  
Author(s):  
A. Yagola ◽  
B. Artamonov ◽  
V. Belokurov ◽  
E. Koptelova ◽  
M. Sazhin ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
A Priori ◽  
A Priori Information ◽  
Priori Information

BAYESIAN METHODS IN THE EVALUATION OF SCATTERING DATA

2006 ◽  
Vol 15 (02) ◽  
pp. 354-361
Author(s):  
H. J. KRAPPE
Keyword(s):  
Elastic Scattering ◽  
Bayesian Methods ◽  
Optical Model ◽  
A Priori ◽  
Scattering Data ◽  
Model Parameters ◽  
A Priori Information ◽  
Model Independent ◽  
Priori Information

It is shown how a priori information can be introduced in an optimal way to extract optical-model parameters in a model-independent way from incomplete elastic scattering data.

scholarly journals Microwave Radar Imaging of Heterogeneous Breast Tissue Integrating A Priori Information

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jochen Moll ◽  
Thomas N. Kelly ◽  
Dallan Byrne ◽  
Mantalena Sarafianou ◽  
Viktor Krozer ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Relative Permittivity ◽  
Breast Tissue ◽  
A Priori ◽  
A Priori Information ◽  
Image Reconstruction Techniques ◽  
Array Configuration ◽  
Microwave Radar ◽  
Priori Information ◽  
Dense Class

Conventional radar-based image reconstruction techniques fail when they are applied to heterogeneous breast tissue, since the underlying in-breast relative permittivity is unknown or assumed to be constant. This results in a systematic error during the process of image formation. A recent trend in microwave biomedical imaging is to extract the relative permittivity from the object under test to improve the image reconstruction quality and thereby to enhance the diagnostic assessment. In this paper, we present a novel radar-based methodology for microwave breast cancer detection in heterogeneous breast tissue integrating a 3D map of relative permittivity as a priori information. This leads to a novel image reconstruction formulation where the delay-and-sum focusing takes place in time rather than range domain. Results are shown for a heterogeneous dense (class-4) and a scattered fibroglandular (class-2) numerical breast phantom using Bristol’s 31-element array configuration.

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