Comparison of Lesion Detection and Quantification in MAP Reconstruction with Gaussian and Non-Gaussian Priors
Statistical image reconstruction methods based on maximum a posteriori (MAP) principle have been developed for emission tomography. The prior distribution of the unknown image plays an important role in MAP reconstruction. The most commonly used prior are Gaussian priors, whose logarithm has a quadr...
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| Format: | Article |
| Language: | English |
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Wiley
2006-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/IJBI/2006/87567 |
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| _version_ | 1832551427255304192 |
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| author | Jinyi Qi |
| author_facet | Jinyi Qi |
| author_sort | Jinyi Qi |
| collection | DOAJ |
| description | Statistical image reconstruction methods based on
maximum a posteriori (MAP) principle have been developed for
emission tomography. The prior distribution of the unknown image
plays an important role in MAP reconstruction. The most commonly
used prior are Gaussian priors, whose logarithm has a quadratic
form. Gaussian priors are relatively easy to analyze. It has been
shown that the effect of a Gaussian prior can be approximated by
linear filtering a maximum likelihood (ML) reconstruction. As a
result, sharp edges in reconstructed images are not preserved. To
preserve sharp transitions, non-Gaussian priors have been
proposed. However, their effect on clinical tasks is less obvious.
In this paper, we compare MAP reconstruction with Gaussian and
non-Gaussian priors for lesion detection and region of interest
quantification using computer simulation. We evaluate three
representative priors: Gaussian prior, Huber prior, and
Geman-McClure prior. We simulate imaging a prostate tumor using
positron emission tomography (PET). The detectability of a known
tumor in either a fixed background or a random background is
measured using a channelized Hotelling observer. The bias-variance
tradeoff curves are calculated for quantification of the total
tumor activity. The results show that for the detection and
quantification tasks, the Gaussian prior is as effective as
non-Gaussian priors. |
| format | Article |
| id | doaj-art-cd9ab60fb9c84f448e8c042c5f0b01eb |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2006-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-cd9ab60fb9c84f448e8c042c5f0b01eb2025-02-03T06:01:35ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962006-01-01200610.1155/IJBI/2006/8756787567Comparison of Lesion Detection and Quantification in MAP Reconstruction with Gaussian and Non-Gaussian PriorsJinyi Qi0Department of Biomedical Engineering, University of California, Davis, CA 95616, USAStatistical image reconstruction methods based on maximum a posteriori (MAP) principle have been developed for emission tomography. The prior distribution of the unknown image plays an important role in MAP reconstruction. The most commonly used prior are Gaussian priors, whose logarithm has a quadratic form. Gaussian priors are relatively easy to analyze. It has been shown that the effect of a Gaussian prior can be approximated by linear filtering a maximum likelihood (ML) reconstruction. As a result, sharp edges in reconstructed images are not preserved. To preserve sharp transitions, non-Gaussian priors have been proposed. However, their effect on clinical tasks is less obvious. In this paper, we compare MAP reconstruction with Gaussian and non-Gaussian priors for lesion detection and region of interest quantification using computer simulation. We evaluate three representative priors: Gaussian prior, Huber prior, and Geman-McClure prior. We simulate imaging a prostate tumor using positron emission tomography (PET). The detectability of a known tumor in either a fixed background or a random background is measured using a channelized Hotelling observer. The bias-variance tradeoff curves are calculated for quantification of the total tumor activity. The results show that for the detection and quantification tasks, the Gaussian prior is as effective as non-Gaussian priors.http://dx.doi.org/10.1155/IJBI/2006/87567 |
| spellingShingle | Jinyi Qi Comparison of Lesion Detection and Quantification in MAP Reconstruction with Gaussian and Non-Gaussian Priors International Journal of Biomedical Imaging |
| title | Comparison of Lesion Detection and Quantification in MAP Reconstruction with Gaussian and Non-Gaussian Priors |
| title_full | Comparison of Lesion Detection and Quantification in MAP Reconstruction with Gaussian and Non-Gaussian Priors |
| title_fullStr | Comparison of Lesion Detection and Quantification in MAP Reconstruction with Gaussian and Non-Gaussian Priors |
| title_full_unstemmed | Comparison of Lesion Detection and Quantification in MAP Reconstruction with Gaussian and Non-Gaussian Priors |
| title_short | Comparison of Lesion Detection and Quantification in MAP Reconstruction with Gaussian and Non-Gaussian Priors |
| title_sort | comparison of lesion detection and quantification in map reconstruction with gaussian and non gaussian priors |
| url | http://dx.doi.org/10.1155/IJBI/2006/87567 |
| work_keys_str_mv | AT jinyiqi comparisonoflesiondetectionandquantificationinmapreconstructionwithgaussianandnongaussianpriors |