title: Postreconstruction filtering of 3D PET images by using weighted higher-order singular value decomposition.
abstract: BACKGROUND
  Positron emission tomography (PET) always suffers from high levels of noise due to the constraints of the injected dose and acquisition time, especially in the studies of dynamic PET imaging. To improve the quality of PET image, several approaches have been introduced to suppress noise. However, traditional filters often blur the image edges, or erase small detail, or rely on multiple parameters. In order to solve such problems, nonlocal denoising methods have been adapted to denoise PET images.
  
  
  METHODS
  In this paper, we propose to use the weighted higher-order singular value decomposition for PET image denoising. We first modeled the noise in the PET image as Poisson distribution. Then, we transformed the noise to an additive Gaussian noise by use of the anscombe root transformation. Finally, we denoised the transformed image using the proposed higher-order singular value decomposition (HOSVD)-based algorithms. The denoised results were compared with results from some general filters by performing physical phantom and mice studies.
  
  
  RESULTS
  Compared to other commonly used filters, HOSVD-based denoising algorithms can preserve boundaries and quantitative accuracy better. The spatial resolution and the low activity features in PET image also can be preserved by use of HOSVD-based methods. Comparing with the standard HOSVD-based algorithm, the proposed weighted HOSVD algorithm can suppress the stair-step artifact, and the time-consumption is about half of that needed by the Wiener-augmented HOSVD algorithm.
  
  
  CONCLUSIONS
  The proposed weighted HOSVD denoising algorithm can suppress noise while better preserving of boundary and quantity in PET images.
authors: Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education and School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, 710126, China.
authors: Hongbo
authors: H
authors: Liu
authors: Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, China.
authors: Kun
authors: K
authors: Wang
authors: Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education and School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, 710126, China. tian@ieee.org.|Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, China. tian@ieee.org.
authors: Jie
authors: J
authors: Tian
mesh_terms: D000465:Algorithms; D000818:Animals; D007089:Image Enhancement; D021621:Imaging, Three-Dimensional / Q000379:methods*; D051379:Mice; D049268:Positron-Emission Tomography; D059629:Signal-To-Noise Ratio
keywords: Anscombe variance-stabilizing transform (AVST); Denoising; Higher-order singular value decomposition (HOSVD); Positron emission tomography (PET); Similarity; Weighted