A Real-time Scintillation Crystal Identification Method and Its FPGA Implementation

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A popular method for gamma ray detection utilizes scintillation detectors which consist of crystals optically coupled to a photomultiplier tubes (PMTs). Scintillators are widely used in the medical radiation fields such as CT scanners, gamma cameras and positron emission tomography (PET) scanners ‎[1]-‎[5]. The scintillation crystal responds to the absorption of gamma ray by the emission of a light pulse. This light pulse is characterized by the special properties of the crystal such as the decay time constant. Then the PMT generates an electrical pulse relative to the absorbed gamma energy.
The phenomenon known as a parallax-error or depth-of-interaction (DOI) error ‎[5] which reduces the sensitivity and reconstruction quality of PET, most probably happened when the photons enter to the detector with non-perpendicular angle. Phosphor sandwich (phoswich) detectors [1] are considered as one of the methods used to reduce the parallax-error.
The phoswich detector is a stack of two or more different scintillation crystals; i.e. with different decay time constants, optically coupled to a single PMT. Hence the DOI error is reduced when the scintillated crystal is identified. The crystal identification (CI) requires applying one of the pulse shape discrimination (PSD) methods ‎ [6] - ‎ [20].
Different PSD algorithms were developed which can be classified into two categories: time domain and frequency domain. In the time domain, the cross correlation ‎ [10], fuzzy logic ‎ [11] and neural network ‎ [12] ‎ were employed in the PSD and CI methods. On the other hand, the Fast Fourier Transform (FFT) of the pulses improved the CI performance ‎‎[13]. Moreover, the normalized least sample (NLS) method ‎[14], which is dependent strongly on ...

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.... Saleh, and M. A. Ashour, "A Zernike Moment Method for Pulse Shape Discrimination in PMT-Based PET Detectors," Nuclear Science, IEEE Transactions on, pp. 1-9, 2013.
[25] V. Vapnik, “Statistical Learning Theory,” John Wiley & Sons Inc., New York, 1998.
[26] V. Vapnik, “The Nature of Statistical Learning Theory,” Springer Verlag, Berlin, 1995.
[27] G. Amayeh, A. Erol, G. Bebis, M. Nicolescu, "Accurate and Efficient Calculation of High Order Zernike Moments", International Symposium on Visual Computing, (LNCS, Vol.3804), Lake Tahoe, Nevada, pp. 462-469, 2005.
[28] J. Gu, H.Z. Shu, C. Toumoulin, L.M. Luo, "A novel algorithm for fast computation of Zernike moments", Pattern Recognition, vol. 35, no. 12, pp. 2905- 2911, 2002.
[29] S. Hwang, W. Kim, "A novel approach to the fast computation of Zernike moments", Pattern Recognition, vol. 39, no. 11, pp. 2065–76, 2006.

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