Penalized Likelihood PET Image Reconstruction Using Patch-Based Edge-Preserving Regularization

Iterative image reconstruction for positron emission tomography (PET) can improve image quality by using spatial regularization that penalizes image intensity difference between neighboring pixels. The most commonly used quadratic penalty often oversmoothes edges and fine features in reconstructed i...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2012-12, Vol.31 (12), p.2194-2204
Main Authors: Wang, Guobao, Qi, Jinyi
Format: Article
Language:English
Subjects:
Algorithms
Animals
Computer Simulation
Convergence
Haplorhini
Image edge detection
Image Processing, Computer-Assisted - methods
Image reconstruction
Imaging, Three-Dimensional
Models, Biological
Neoplasms, Experimental - pathology
Noise measurement
Optimization
patch regularization
penalized maximum likelihood
Phantoms, Imaging
Positron emission tomography
Positron-Emission Tomography - methods
Robustness
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Summary:Iterative image reconstruction for positron emission tomography (PET) can improve image quality by using spatial regularization that penalizes image intensity difference between neighboring pixels. The most commonly used quadratic penalty often oversmoothes edges and fine features in reconstructed images. Nonquadratic penalties can preserve edges but often introduce piece-wise constant blocky artifacts and the results are also sensitive to the hyper-parameter that controls the shape of the penalty function. This paper presents a patch-based regularization for iterative image reconstruction that uses neighborhood patches instead of individual pixels in computing the nonquadratic penalty. The new regularization is more robust than the conventional pixel-based regularization in differentiating sharp edges from random fluctuations due to noise. An optimization transfer algorithm is developed for the penalized maximum likelihood estimation. Each iteration of the algorithm can be implemented in three simple steps: an EM-like image update, an image smoothing and a pixel-by-pixel image fusion. Computer simulations show that the proposed patch-based regularization can achieve higher contrast recovery for small objects without increasing background variation compared with the quadratic regularization. The reconstruction is also more robust to the hyper-parameter than conventional pixel-based nonquadratic regularizations. The proposed regularization method has been applied to real 3-D PET data.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2012.2211378