Development and evaluation of MRI based bayesian image reconstruction methods for PET

Chao Hsing Wang; Jyh-Cheng Chen; Chien Min Kao; Ren-Shyan Liu

doi:10.1109/NSSMIC.2003.1352422

Development and evaluation of MRI based bayesian image reconstruction methods for PET

Chao Hsing Wang^*, Jyh-Cheng Chen, Chien Min Kao, Ren-Shyan Liu

^*Corresponding author for this work

Department of Biomedical Imaging and Radiological Sciences

Research output: Contribution to journal › Conference article › peer-review

Abstract

MRI Based Bayesian Image Reconstruction Methods for positron emission tomography reconstruction with the aim at improving image quality was developed. The line site map from MRI as a priori is made up of either a modified Markov random field or Canny edge detector with the Gaussian smoothing filter. It is used in maximum a posteriori (MAP) algorithm by a weighted line site method. We evaluate and compare the performance of these reconstruction methods. The results show that the Bayesian methods produce reconstructed images with less noise and better spatial resolution than those produced by the maximum likelihood-expectation maximization method.

Original language	English
Article number	M10-220
Pages (from-to)	2599-2603
Number of pages	5
Journal	IEEE Nuclear Science Symposium Conference Record
Volume	4
DOIs	https://doi.org/10.1109/NSSMIC.2003.1352422
State	Published - 19 Oct 2003
Event	2003 IEEE Nuclear Science Symposium Conference Record - Nuclear Science Symposium, Medical Imaging Conference - Portland, OR, United States Duration: 19 Oct 2003 → 25 Oct 2003

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10.1109/NSSMIC.2003.1352422

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@article{ff80ddb3b8134121af493c37fda6c586,

title = "Development and evaluation of MRI based bayesian image reconstruction methods for PET",

abstract = "MRI Based Bayesian Image Reconstruction Methods for positron emission tomography reconstruction with the aim at improving image quality was developed. The line site map from MRI as a priori is made up of either a modified Markov random field or Canny edge detector with the Gaussian smoothing filter. It is used in maximum a posteriori (MAP) algorithm by a weighted line site method. We evaluate and compare the performance of these reconstruction methods. The results show that the Bayesian methods produce reconstructed images with less noise and better spatial resolution than those produced by the maximum likelihood-expectation maximization method.",

author = "Wang, \{Chao Hsing\} and Jyh-Cheng Chen and Kao, \{Chien Min\} and Ren-Shyan Liu",

note = "Funding Information: This work was supported in part by VTY Joint Research Program, Tsou's Foundation under Grant VTY90-P2-14. The first author was also supported in part by NRPGM under Grant NSC91-3112-P-075-001-Y. The authors wish to thank C.M. Kao for providing invaluable information. We thank H.H.S. Lu, and I.T. Hsiao for their invaluable discussions. The authors also appreciate the help of T.B. Chen, K.L. Chou, K.Y. Tu, R.M. Hwang, and H.S. Chueh in preparing the experiments. We thank Eric Jao for his help in editing. ; 2003 IEEE Nuclear Science Symposium Conference Record - Nuclear Science Symposium, Medical Imaging Conference ; Conference date: 19-10-2003 Through 25-10-2003",

year = "2003",

month = oct,

day = "19",

doi = "10.1109/NSSMIC.2003.1352422",

language = "English",

volume = "4",

pages = "2599--2603",

journal = "IEEE Nuclear Science Symposium Conference Record",

issn = "1095-7863",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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TY - JOUR

T1 - Development and evaluation of MRI based bayesian image reconstruction methods for PET

AU - Wang, Chao Hsing

AU - Chen, Jyh-Cheng

AU - Kao, Chien Min

AU - Liu, Ren-Shyan

N1 - Funding Information: This work was supported in part by VTY Joint Research Program, Tsou's Foundation under Grant VTY90-P2-14. The first author was also supported in part by NRPGM under Grant NSC91-3112-P-075-001-Y. The authors wish to thank C.M. Kao for providing invaluable information. We thank H.H.S. Lu, and I.T. Hsiao for their invaluable discussions. The authors also appreciate the help of T.B. Chen, K.L. Chou, K.Y. Tu, R.M. Hwang, and H.S. Chueh in preparing the experiments. We thank Eric Jao for his help in editing.

PY - 2003/10/19

Y1 - 2003/10/19

N2 - MRI Based Bayesian Image Reconstruction Methods for positron emission tomography reconstruction with the aim at improving image quality was developed. The line site map from MRI as a priori is made up of either a modified Markov random field or Canny edge detector with the Gaussian smoothing filter. It is used in maximum a posteriori (MAP) algorithm by a weighted line site method. We evaluate and compare the performance of these reconstruction methods. The results show that the Bayesian methods produce reconstructed images with less noise and better spatial resolution than those produced by the maximum likelihood-expectation maximization method.

AB - MRI Based Bayesian Image Reconstruction Methods for positron emission tomography reconstruction with the aim at improving image quality was developed. The line site map from MRI as a priori is made up of either a modified Markov random field or Canny edge detector with the Gaussian smoothing filter. It is used in maximum a posteriori (MAP) algorithm by a weighted line site method. We evaluate and compare the performance of these reconstruction methods. The results show that the Bayesian methods produce reconstructed images with less noise and better spatial resolution than those produced by the maximum likelihood-expectation maximization method.

UR - https://www.scopus.com/pages/publications/11844296722

U2 - 10.1109/NSSMIC.2003.1352422

DO - 10.1109/NSSMIC.2003.1352422

M3 - Conference article

AN - SCOPUS:11844296722

SN - 1095-7863

VL - 4

SP - 2599

EP - 2603

JO - IEEE Nuclear Science Symposium Conference Record

JF - IEEE Nuclear Science Symposium Conference Record

M1 - M10-220

T2 - 2003 IEEE Nuclear Science Symposium Conference Record - Nuclear Science Symposium, Medical Imaging Conference

Y2 - 19 October 2003 through 25 October 2003

ER -

Development and evaluation of MRI based bayesian image reconstruction methods for PET

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