Restoration of lost frequency in OpenPET imaging: comparison between the method of convex projections and the maximum likelihood expectation maximization method

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• 作者：Hideaki Tashima (1)
  Takayuki Katsunuma (2)
  Hiroyuki Kudo (3)
  Hideo Murayama (1)
  Takashi Obi (4)
  Mikio Suga (2)
  Taiga Yamaya (1)
  
• 关键词：OpenPET ; Positron emission tomography ; Iterative methods ; Maximum likelihood expectation maximization ; Method of convex projections ; Projections onto convex sets
• 刊名：Radiological Physics and Technology
• 出版年：2014
• 出版时间：July 2014
• 年：2014
• 卷：7
• 期：2
• 页码：329-339
• 全文大小：1,576 KB
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• 作者单位：Hideaki Tashima (1)
  Takayuki Katsunuma (2)
  Hiroyuki Kudo (3)
  Hideo Murayama (1)
  Takashi Obi (4)
  Mikio Suga (2)
  Taiga Yamaya (1)
  
  1. Molecular Imaging Center, National Institute of Radiological Sciences, 4-9-1 Anagawa, Inage-ku, Chiba, 263-8555, Japan
  2. Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, 263-8522, Japan
  3. Division of Information Engineering, Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba, 305-8573, Japan
  4. Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259-G2-2 Nagatsuta-cho, Midori-ku, Yokohama, 226-8502, Japan
  
• ISSN：1865-0341

文摘

We are developing a new PET scanner based on the “OpenPET-geometry, which consists of two detector rings separated by a gap. One item to which attention must be paid is that OpenPET image reconstruction is classified into an incomplete inverse problem, where low-frequency components are truncated. In our previous simulations and experiments, however, the OpenPET imaging was made feasible by application of iterative image reconstruction methods. Therefore, we expect that iterative methods have a restorative effect to compensate for the lost frequency. There are two types of reconstruction methods for improving image quality when data truncation exists: one is the iterative methods such as the maximum-likelihood expectation maximization (ML-EM) and the other is an analytical image reconstruction method followed by the method of convex projections, which has not been employed for the OpenPET. In this study, therefore, we propose a method for applying the latter approach to the OpenPET image reconstruction and compare it with the ML-EM. We found that the proposed analytical method could reduce the occurrence of image artifacts caused by the lost frequency. A similar tendency for this restoration effect was observed in ML-EM image reconstruction where no additional restoration method was applied. Therefore, we concluded that the method of convex projections and the ML-EM had a similar restoration effect to compensate for the lost frequency.