DocumentCode :
792484
Title :
Spatially Adaptive Temporal Smoothing for Reconstruction of Dynamic Image Sequences
Author :
Brankov, Jovan G. ; Wernick, Miles N. ; King, Michael A. ; Yang, Yongyi ; Narayanan, Manoj V.
Author_Institution :
Dept. of Electr. & Comput. Eng., Illinois Inst. of Technol., Chicago, IL
Volume :
53
Issue :
5
fYear :
2006
Firstpage :
2769
Lastpage :
2777
Abstract :
In this paper, we propose a method for spatio-temporal reconstruction of dynamic image sequences. In a method we proposed previously, temporal smoothing in a Karhunen-Loegraveve (KL) or principal components (PC) transform domain was used prior to reconstruction to reduce the effect of noise. Unlike the Bayesian priors that are usually used in image reconstruction, temporal KL smoothing is a data-driven approach that takes advantage of the fact that the desired part of the data is characterized by strong interframe correlations, whereas the noise is uncorrelated. A potential disadvantage of KL-based methods is that they typically use a pooled estimate of the signal covariance matrix, thus assuming that all pixels obey similar time functions. In this paper, we investigate the possibility of making the temporal smoothing adapt spatially to local characteristics in the projection data. This can improve the noise performance of the temporal smoothing, while lessening the possibility of signal distortion. Computer simulation results are used to evaluate the technique for dynamic imaging applications in brain and tumor imaging
Keywords :
Karhunen-Loeve transforms; correlation methods; covariance matrices; image reconstruction; image sequences; medical image processing; positron emission tomography; Karhunen-Loeve transform; PET; brain imaging; data-driven approach; dynamic image sequence; four-dimensional reconstruction; image reconstruction; interframe correlation; noise performance; positron emission tomography; principal components transform; signal covariance matrix; signal distortion; spatially adaptive temporal smoothing; spatio-temporal reconstruction; time functions; tumor imaging; Application software; Bayesian methods; Computer simulation; Covariance matrix; Distortion; Image reconstruction; Image sequences; Neoplasms; Noise reduction; Smoothing methods; Dynamic positron emission tomography (PET); four-dimensional (4-D) reconstruction; image sequence; principal component analysis; smoothing;
fLanguage :
English
Journal_Title :
Nuclear Science, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9499
Type :
jour
DOI :
10.1109/TNS.2006.882738
Filename :
1710267
Link To Document :
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