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Improved Weighted Least Squares Algorithm for PET Image Reconstruction Using Iterative Coordinate Descent Algorithms

In this paper, we present an image reconstruction method for positron emission tomography (PET) based on weighted least squares objective function. Unlike a usual WLS algorithm, the proposed method, which we call it ICD-WLS, minimizes the WLS objective function using iterative coordinate descent (ICD) method in a sequence of small “hidden” data spaces. Although ICD-WLS also takes a strategy to update parameter sequentially just like common ICD method, however, the use of these small “hidden” data spaces makes the ICD-WLS method to converge more faster and to produce reconstructed images with greater contrast and detail than the usual WLS methods. The experimental results show that the proposed method is more effective even if the projection data include statistic noise.