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  • Meeting abstract
  • Open Access

Multi-modality image reconstruction for dual-head small-animal PET

  • 1 and
  • 1
EJNMMI Physics20152 (Suppl 1) :A35

https://doi.org/10.1186/2197-7364-2-S1-A35

  • Published:

Keywords

  • Expectation Maximization Algorithm
  • Convex Optimization Problem
  • High Detection Sensitivity
  • Data Fidelity
  • Total Variation Norm

The hybrid positron emission tomography/computed tomography (PET/CT) or positron emission tomography/magnetic resonance imaging (PET/MRI) has become routine practice in clinics. The applications of multi-modality imaging can also benefit research advances. Consequently, dedicated small-imaging system like dual-head small-animal PET (DHAPET) that possesses the advantages of high detection sensitivity and high resolution can exploit the structural information from CT or MRI. It should be noted that the special detector arrangement in DHAPET leads to severe data truncation, thereby degrading the image quality. We proposed to take advantage of anatomical priors and total variation (TV) minimization methods to reconstruct PET activity distribution form incomplete measurement data. The objective is to solve the penalized least-squares function consisted of data fidelity term, TV norm and medium root priors. In this work, we employed the splitting-based fast iterative shrinkage/thresholding algorithm to split smooth and non-smooth functions in the convex optimization problems. Our simulations studies validated that the images reconstructed by use of the proposed method can outperform those obtained by use of conventional expectation maximization algorithms or that without considering the anatomical prior information. Additionally, the convergence rate is also accelerated.

Authors’ Affiliations

(1)
National Taiwan University, Taipei, Taiwan

Copyright

© Huang and Chou; licensee Springer. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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