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J Nucl Med. 2008; 49 (Supplement 1):61P
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Instrumentation & Data Analysis: Image Generation

PET - Reconstruction and Compensation

Maximum a posteriori event positioning in high-resolution PET CZT detectors

Guillem Pratx1 and Craig Levin1

1 Stanford University, Stanford, California

242

Objectives: Cadmium-Zinc-Telluride (CZT) may be useful for PET, but its photo-fraction is low and multiple interactions in the detectors are frequent. However, a CZT detector can localize the 3D coordinates of individual photon interactions with high spatial and energy resolution. A challenge is to correctly estimate the first interaction, which best defines the correct line of response. Previously, we had proposed a maximum-likelihood (ML) approach. In this work, we investigate a maximum a posteriori (MAP) positioning method.

Methods: Monte-Carlo integration is used to compute the likelihood of each possible sequence of interaction. An accurate physical model is used to compute the a priori probability of each sequence and obtain a posteriori probabilities. This model uses the exponential photon linear attenuation law and the Klein Nishina formula. A parameter β defines the relative weights of the likelihood and the prior model (β=0 for ML). For fast computation, we are investigating using graphics processing units to parallelize the algorithm.

Results: We generated data using Monte-Carlo for two simulated CZT-based PET systems. The high-specs (HS) system has 1x1x1 mm3 spatial and 3% fwhm energy resolution. The low-specs (LS) system has 1x1x5 mm3 spatial and 12% energy resolution. The method was evaluated on both systems with β varying. The performance is measured as the success rate in recovering either the full sequence of interactions or just the first interaction.

Conclusions: For the HS system, MAP performs worse than ML as β increases. This indicates that low-noise data should be trusted more than the prior model. For the LS system and β=2, MAP outperforms ML by 7% for recovering the correct interaction sequence and 8% for the first interaction. An accurate prior model makes the position estimate more robust to noise in the data.

Research Support: NIH R01CA119056, R33EB003283, R01CA120474





This Article
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Right arrow Email this article to a friend
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Right arrow Articles by Pratx, G.
Right arrow Articles by Levin, C.
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Right arrow Articles by Pratx, G.
Right arrow Articles by Levin, C.