Abstract
When using cardiac nuclear medicine images for diagnosis, the filtered back-projection (FBP) algorithm can reconstruct positron emission tomography (PET) images under low count rates. However, background strike artifacts in PET images are affected by diagnostic judgment. Hence, this study developed a robust method of removing background strike artifacts from FBP images without reducing image quality. A Jaszczak anthropomorphic torso phantom and a laboratory rabbit were used for performance tests of the proposed method. Parallel computing was applied to optimize the mask size of morphological structure operator (MSO) by minimizing the background standard deviation (Std). The optimal MSO mask size for the evaluated Jaszczak phantom was 3×3. The FBP images processed by MSO had significantly reduced strike artifacts measured by background Std (P=1E-5). After MSO processing, the time activity curve (TAC) of FBP images was stable and resembled the original FBP images (P=0.5). The proposed approach is highly stable and reduces noise by 13.08±2.32 in FBP images after MSO processing with 3×3 mask.
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