Deep reconstruction model for dynamic PET images

Jianan Cui, Xin Liu, Yile Wang and Huafeng Liu

PLOS ONE, 2017, vol. 12, issue 9, 1-21

Abstract: Accurate and robust tomographic reconstruction from dynamic positron emission tomography (PET) acquired data is a difficult problem. Conventional methods, such as the maximum likelihood expectation maximization (MLEM) algorithm for reconstructing the activity distribution-based on individual frames, may lead to inaccurate results due to the checkerboard effect and limitation of photon counts. In this paper, we propose a stacked sparse auto-encoder based reconstruction framework for dynamic PET imaging. The dynamic reconstruction problem is formulated in a deep learning representation, where the encoding layers extract the prototype features, such as edges, so that, in the decoding layers, the reconstructed results are obtained through a combination of those features. The qualitative and quantitative results of the procedure, including the data based on a Monte Carlo simulation and real patient data demonstrates the effectiveness of our method.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0184667

DOI: 10.1371/journal.pone.0184667

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