Sub-detector unit timing calibration for brain PET based on L1-norm constraint

被引:5
|
作者
Yu, Xingjian [1 ]
Isobe, Takashi [2 ]
Watanabe, Mitsuo [2 ]
Liu, Huafeng [1 ]
机构
[1] Zhejiang Univ, State Key Lab Modern Opt Instrumentat, Coll Opt Sci & Engn, Hangzhou, Zhejiang, Peoples R China
[2] Hamamatsu Photon KK, Cent Res Lab, Hamamatsu, Shizuoka 4348601, Japan
来源
基金
中国国家自然科学基金;
关键词
PET; timing calibration; L1-norm; detector unit segmentation; iterative shrinkage-thresholding;
D O I
10.1088/2057-1976/2/3/035008
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Anovel sub-detector unit timing calibration method using iterative shrinkage-thresholding (IST) has been developed for a high-resolution brain positron emission tomography (PET) system, which uses a four-layer depth of interaction detector. The timing calibration process is formulated into a linear problem in the proposed method. To obtain a low-variance and robust estimation, an L1-norm constraint is added in the linear equation. Moreover, the detector unit segmentation in the detectors is used to achieve a sub-detector unit level timing calibration. No special source or additional detector element is required for the timing calibration. It is shown that the measured data set from a normal cylindrical phantom filled with radioisotope solution is sufficient for performing a high-precision timing calibration. This method is suitable for PET systems with a large number of crystal elements. In this paper, both simulation and real scanning data were used to demonstrate the effectiveness and robustness of the sub-detector unit level IST method. The timing resolution of a Na-22 point source placed at the center of the field of view in the brain PET system was evaluated using different timing calibration methods, along with the optimal selection of detector unit segmentation, to validate the IST method. The timing resolution was improved from 3.34 ns (at full width at half maximum (FWHM)) to 2.54 ns (FWHM) through implementing the IST and the detector unit segmentation.
引用
收藏
页数:10
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