PET image reconstruction incorporating 3D mean-median sinogram filtering

Positron Emission Tomography (PET) projection data or sinogram contained poor statistics and randomness that produced noisy PET images. In order to improve the PET image, we proposed an implementation of pre-reconstruction sinogram filtering based on 3D mean-median filter. The proposed filter is des...

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Main Authors: Mokri, Siti Salasiah, Saripan, M. Iqbal, Abd. Rahni, Ashrani Aizzuddin, Nordin, Abdul Jalil, Hashim, Suhairul, Marhaban, Mohammad Hamiruce
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers 2016
Online Access:http://psasir.upm.edu.my/id/eprint/16431/
http://psasir.upm.edu.my/id/eprint/16431/1/PET%20image%20reconstruction%20incorporating%203D%20mean-median%20sinogram%20filtering.pdf
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author Mokri, Siti Salasiah
Saripan, M. Iqbal
Abd. Rahni, Ashrani Aizzuddin
Nordin, Abdul Jalil
Hashim, Suhairul
Marhaban, Mohammad Hamiruce
author_facet Mokri, Siti Salasiah
Saripan, M. Iqbal
Abd. Rahni, Ashrani Aizzuddin
Nordin, Abdul Jalil
Hashim, Suhairul
Marhaban, Mohammad Hamiruce
author_sort Mokri, Siti Salasiah
building UPM Institutional Repository
collection Online Access
description Positron Emission Tomography (PET) projection data or sinogram contained poor statistics and randomness that produced noisy PET images. In order to improve the PET image, we proposed an implementation of pre-reconstruction sinogram filtering based on 3D mean-median filter. The proposed filter is designed based on three aims; to minimise angular blurring artifacts, to smooth flat region and to preserve the edges in the reconstructed PET image. The performance of the pre-reconstruction sinogram filter prior to three established reconstruction methods namely filtered-backprojection (FBP), Maximum likelihood expectation maximization-Ordered Subset (OSEM) and OSEM with median root prior (OSEM-MRP) is investigated using simulated NCAT phantom PET sinogram as generated by the PET Analytical Simulator (ASIM). The improvement on the quality of the reconstructed images with and without sinogram filtering is assessed according to visual as well as quantitative evaluation based on global signal to noise ratio (SNR), local SNR, contrast to noise ratio (CNR) and edge preservation capability. Further analysis on the achieved improvement is also carried out specific to iterative OSEM and OSEM-MRP reconstruction methods with and without pre-reconstruction filtering in terms of contrast recovery curve (CRC) versus noise trade off, normalised mean square error versus iteration, local CNR versus iteration and lesion detectability. Overall, satisfactory results are obtained from both visual and quantitative evaluations.
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spelling upm-164312016-06-08T01:53:40Z http://psasir.upm.edu.my/id/eprint/16431/ PET image reconstruction incorporating 3D mean-median sinogram filtering Mokri, Siti Salasiah Saripan, M. Iqbal Abd. Rahni, Ashrani Aizzuddin Nordin, Abdul Jalil Hashim, Suhairul Marhaban, Mohammad Hamiruce Positron Emission Tomography (PET) projection data or sinogram contained poor statistics and randomness that produced noisy PET images. In order to improve the PET image, we proposed an implementation of pre-reconstruction sinogram filtering based on 3D mean-median filter. The proposed filter is designed based on three aims; to minimise angular blurring artifacts, to smooth flat region and to preserve the edges in the reconstructed PET image. The performance of the pre-reconstruction sinogram filter prior to three established reconstruction methods namely filtered-backprojection (FBP), Maximum likelihood expectation maximization-Ordered Subset (OSEM) and OSEM with median root prior (OSEM-MRP) is investigated using simulated NCAT phantom PET sinogram as generated by the PET Analytical Simulator (ASIM). The improvement on the quality of the reconstructed images with and without sinogram filtering is assessed according to visual as well as quantitative evaluation based on global signal to noise ratio (SNR), local SNR, contrast to noise ratio (CNR) and edge preservation capability. Further analysis on the achieved improvement is also carried out specific to iterative OSEM and OSEM-MRP reconstruction methods with and without pre-reconstruction filtering in terms of contrast recovery curve (CRC) versus noise trade off, normalised mean square error versus iteration, local CNR versus iteration and lesion detectability. Overall, satisfactory results are obtained from both visual and quantitative evaluations. Institute of Electrical and Electronics Engineers 2016 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/16431/1/PET%20image%20reconstruction%20incorporating%203D%20mean-median%20sinogram%20filtering.pdf Mokri, Siti Salasiah and Saripan, M. Iqbal and Abd. Rahni, Ashrani Aizzuddin and Nordin, Abdul Jalil and Hashim, Suhairul and Marhaban, Mohammad Hamiruce (2016) PET image reconstruction incorporating 3D mean-median sinogram filtering. IEEE Transactions on Nuclear Science, 63 (1). pp. 157-169. ISSN 0018-9499 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7407515&tag=1 10.1109/TNS.2015.2513484
spellingShingle Mokri, Siti Salasiah
Saripan, M. Iqbal
Abd. Rahni, Ashrani Aizzuddin
Nordin, Abdul Jalil
Hashim, Suhairul
Marhaban, Mohammad Hamiruce
PET image reconstruction incorporating 3D mean-median sinogram filtering
title PET image reconstruction incorporating 3D mean-median sinogram filtering
title_full PET image reconstruction incorporating 3D mean-median sinogram filtering
title_fullStr PET image reconstruction incorporating 3D mean-median sinogram filtering
title_full_unstemmed PET image reconstruction incorporating 3D mean-median sinogram filtering
title_short PET image reconstruction incorporating 3D mean-median sinogram filtering
title_sort pet image reconstruction incorporating 3d mean-median sinogram filtering
url http://psasir.upm.edu.my/id/eprint/16431/
http://psasir.upm.edu.my/id/eprint/16431/
http://psasir.upm.edu.my/id/eprint/16431/
http://psasir.upm.edu.my/id/eprint/16431/1/PET%20image%20reconstruction%20incorporating%203D%20mean-median%20sinogram%20filtering.pdf