科研之友
微信
新浪微博
Facebook
分享链接
摘要
Background: High resolution is of key importance for positron emission tomography (PET) devices. Modern PET scanners use a large number of crystals in detector blocks to improve their resolution. The extra crystals lead to a significant increase in workload of crystal identification in PET detector calibration. Hence the robustness of crystal identification algorithms becomes a challenge. Crystal identification is typically based on flood histograms obtained by irradiating the detector blocks with an annihilation photonflood source. More crystals of smaller size degrade the quality of flood histogram and generate more complex spatial distortions. Existing crystal identification methods are not robust enough to handle these cases.Purpose: Our study aims to improve the robustness and success rate of crystal identification on PET detectors of different-level quality and reduce the manual interaction in PET detectors calibration.Methods: In this study, we developed a hierarchical fusion approach to overcome the limitations of the existing methods for accurate PET detector calibration. We first implemented three complementary state-of-the-art crystal identification methods and then assessed the consistency of the three identification results based on the quality of detected crystal position and row/column label information. The results of higher consistency are considered more reliable. The hierarchical fusion approach integrated first the identification results of the highest consistency and then those of gradually lower consistencies.Results: The performance of the crystal identification method was evaluated with PET detectors of three different levels (normal, heavily distorted, and low signal-to-noise ratio (SNR)). Although the three crystal identification methods alone achieved over 95% of success rate on normal quality detectors, they only achieved around 80% of success rate on detectors of heavy distortion and low SNR. Our hierarchical fusion approach consistently improved the success rate to 99.8%, 92.2% and 94.3% for detectors of normal, heavily distorted, and low SNR.Conclusion: Compared to three individual PET detector identification methods, the fusion-based approach consistently and significantly improved the success rate of PET detector identification, especially for heavily distorted and low SNR detectors.
-
单位北京; 中国科学院研究生院; 中国科学院上海高等研究院; 空
