NDLI: Sequential regularized MLEM reconstruction method for incomplete sinograms

Content Provider	IEEE Xplore Digital Library
Author	Tuna, U. Pepe, A. Ruotsalainen, U.
Copyright Year	2011
Description	Author affiliation: Department of Signal Processing, Tampere University of Technology, P.O. BOX 533, FIN-33101, BioMediTech, Finland (Tuna, U.; Pepe, A.; Ruotsalainen, U.)
Abstract	In this study, we investigate the capability of the well-known maximum likelihood expectation maximization (MLEM) method in handling the incomplete sinogram reconstruction. MLEM method is known to be able to deal with the missing parts via system matrix modeling. We propose sequentially applied regularized MLEM method for the image reconstruction from the incomplete sinograms. Median filtering (MED), L-filtering (L) and block matching 3D (BM3D) filtering were employed as spatial domain regularizers, separately. The MLEM method was applied sequentially by decreasing gradually the amount of the regularization when the image update between the consecutive iterations was negligible. The sequential application of the MLEM method is used to compensate for the blurring caused by the regularization filters. In order to test the proposed approach thoroughly, we constructed wide test dataset consisting of three sparse sinograms (by using 25%, 10% and 5.5% of the sinogram data), the ECAT HRRT type sinogram (81.2% of the sinogram data), ClearPET type sinogram (88.3% of the sinogram data), AX-PET type sinogram (56% of the sinogram data) and a hypothetical checkerboard type sinogram (50% of the sinogram data) of the Shepp-Logan phantom. The constructed incomplete sinograms were contaminated with the Poisson noise. The incomplete sinograms were reconstructed by using filtered backprojection, MLEM without filtering, MLEM with MED, MLEM with L and MLEM with BM3D filtering. We evaluated the approach visually and with the quantitative normalized mean square error (NMSE). The results showed that MLEM without filtering cannot correct the artifacts related to the missing sinogram bins for some of the generated incomplete sinograms. On the other hand, MLEM method with spatial regularization filter was able to provide successfully reconstructed images for all cases. Quantitative NMSE values were in parallel with the visual impression of the reconstructed images.
Starting Page	2553
Ending Page	2557
File Size	2367805
Page Count	5
File Format	PDF
ISBN	9781467301183
ISSN	10823654
e-ISBN	9781467301206
DOI	10.1109/NSSMIC.2011.6152689
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-23
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Information filters Biological system modeling Noise sparse sinogram Block matching and 3D filtering L filter median filtering missing sinogram bins quantitative evaluation region of interest regularized statistical reconstruction method
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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