A New Pulse Pileup Rejection Method Based on Position Shift Identification

Gu, Zhichen; Prout, David; Taschereau, Richard; Bai, Bing; Chatziioannou, Arion F.

doi:10.1109/tns.2015.2495169

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…However, at high event rates this efficiency can compromise the operation of such multiplexed detectors. For example, the BGO-based PETBox4 scanner (Gu et al 2013), which relies on multiplexing 64 PMT channels to three signals can encounter severe pileup at high event rates that requires elaborate algorithms (Gu et al 2016) to both detect and recover the data. In addition, with the independent information from individual pixels the possibility exists to identify multiple scattering of gamma rays within each crystal layer.…”

Section: Discussionmentioning

confidence: 99%

A digital phoswich detector using time-over-threshold for depth of interaction in PET

Prout

Shustef

et al. 2020

Phys. Med. Biol.

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We present the performance of a digital phoswich positron emission tomography (PET) detector, composed by layers of pixilated scintillator arrays, read out by solid state light detectors and an application specific integrated circuit (ASIC). We investigated the use of integrated charge from the scintillation pulses along with time-over-threshold (ToT) to determine the layer of interaction (DOI) in the scintillator. Simulations were performed to assess the effectiveness of the ToT measurements for separating the scintillator events and identifying cross-layer-crystal-scatter (CLCS) events. These simulations indicate that ToT and charge integration from such a detector provide sufficient information to determine the layer of interaction. To demonstrate this in practice, we used a pair of prototype LYSO/BGO detectors. One detector consisted of a 19 × 19 array of 7 mm long LYSO crystals (1.36 mm pitch) coupled to a 16 × 16 array of 8 mm long BGO crystals (1.63 mm pitch). The other detector was similar except the LYSO crystal pitch was 1.63 mm. These detectors were coupled to an 8 × 8 multi-pixel photon counter mounted on a PETsys TOFPET2 ASIC. This high performance ASIC provided digital readout of the integrated charge and ToT from these detectors. We present a method to separate the events from the two scintillator layers using the ToT, and also investigate the performance of this detector. All the crystals within the proposed detector were clearly resolved, and the peak to valley ratio was 11.8 ± 4.0 and 10.1 ± 2.9 for the LYSO and BGO flood images. The measured energy resolution was 9.9% ± 1.3% and 28.5% ± 5.0% respectively for the LYSO and BGO crystals in the phoswich layers. The timing resolution between the LYSO–LYSO, LYSO–BGO and BGO–BGO coincidences was 468 ps, 1.33 ns and 2.14 ns respectively. Results show ToT can be used to identify the crystal layer where events occurred and also identify and reject the majority of CLCS events between layers.

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Section: Discussionmentioning

confidence: 99%

A digital phoswich detector using time-over-threshold for depth of interaction in PET

Prout

Shustef

et al. 2020

Phys. Med. Biol.

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“…The increased granularity of the detector readout scheme in the G8 leads to an extended system dynamic range and better count rate performance. In the G4, the NECR peak was reached at lower total activity than for comparable instruments, because of its compact system geometry, the long decay time of BGO, and the multiplexed electronics (12,15). For each g-interaction in a detector module, the BGO signal is integrated for 800 ns, during which no new event detection is possible.…”

Section: Discussionmentioning

confidence: 99%

“…The signals from all 8 detector modules (4 signals per module) are digitized by 32 free-running 125-MHz analog-to-digital converters, at 14 bits per sample (PicoDigitizer). These digital samples are then processed in a Xilinx Virtex-6 field programmable gate array (Xilinx) in real time for event triggering, validation (15), and coincidence sorting. Energy discrimination is performed offline in software, during event histogramming.…”

Section: System Descriptionmentioning

confidence: 99%

Performance Evaluation of G8, a High-Sensitivity Benchtop Preclinical PET/CT Tomograph

Taschereau

et al. 2018

J Nucl Med

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G8 is a bench top integrated PET/CT scanner dedicated to high sensitivity and high resolution imaging of mice. This work characterizes its National Electrical Manufacturers Association (NEMA) NU4-2008 performance where applicable and also provides an assessment of the basic imaging performance of the CT subsystem. The PET subsystem in G8 consists of four flat-panel type detectors arranged in a box like geometry. Each panel consists of two modules of a 26 × 26 pixelated bismuth germanate (BGO) scintillator array with individual crystals measuring 1.75 × 1.75 × 7.2 mm. The crystal arrays are coupled to multichannel photomultiplier tubes via a tapered, pixelated glass lightguide. A cone-beam CT consisting of a micro focus X-ray source and a Complementary Metal Oxide Semiconductor (CMOS) detector provides anatomical information. Sensitivity, spatial resolution, energy resolution, scatter fraction, count-rate performance and the capability of phantom and mouse imaging were evaluated for the PET subsystem. Noise, dose level, contrast and resolution were evaluated for the CT subsystem. With an energy window of 350-650 keV, the peak sensitivity was measured to be 9.0% near the center of the field of view (CFOV). The crystal energy resolution ranged from 15.0% to 69.6% full width at half maximum (FWHM), with a mean of 19.3 ± 3.7%. The average detector intrinsic spatial resolution was 1.30 mm and 1.38 mm FWHM in the transverse and axial directions. The maximum likelihood expectation maximization (ML-EM) reconstructed image of a point source in air, averaged 0.81 ± 0.11 mm FWHM. The peak noise equivalent count rate (NECR) for the mouse-sized phantom was 44 kcps for a total activity of 2.9 MBq (78 µCi) and the scatter fraction was 11%. For the CT subsystem, the value of the modulation transfer function (MTF) at 10% was 2.05 cycles/mm. The overall performance demonstrates that the G8 can produce high quality images for molecular imaging based biomedical research.

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“…Currently, various methods have been proposed for PUC, including pile-up rejection (PUR) [2][3][4][5], pile-up separation (PUS) by recovering pulses [6][7][8][9], and pulse height estimation (PHE) using numerical algorithms [10][11][12]. PUR is effective in solving spectrum distortion, however, leads to counting loss by discarding pile-up signals.…”

Section: Introductionmentioning

confidence: 99%

Optimizing deep learning-based piled-up pulse height correction method for high radiation-field application

Kim,

Ko,

Lee

et al. 2023

J. Inst.

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In high-radiation environments, measured pile-up pulses can lead to unavoidable issues such as total count loss and spectrum distortion. Additionally, the recording of large volumes of data within a short period makes real-time processing difficult. In this study, a deep learning-based pulse height estimation (PHE) method was optimized to perform pile-up signal correction in high-radiation fields. First, we adopted a previous deep-learning-based PHE method that allows for fast correction without being restricted to specific detectors. However, the peak-finding method was slightly modified to improve the count restoration rate. Moreover, the input data length of the deep learning model was optimized for convolutional neural networks (CNN) and deep neural networks (DNN) to achieve the maximum correction performance using minimal input data. A series of single pulses was experimentally obtained from a LaBr3 detector with a short decay time to prepare a dataset for training the deep learning models. The pile-up signals were generated by randomly synthesizing single pulses. Samples around their peaks were sliced using the peak-finding method and used as input data for the deep learning models. As a result of the optimization, the modified peak-finding method improved the count restoration rate compared to the previous method by effectively detecting the peaks of tail pile-up, and peak pile-up pulses. Furthermore, the input data length and region were optimized based on the performance evaluation of each deep learning model. Despite having a simpler architecture than the CNN model, the DNN model demonstrated excellent PHE performance. The results of this study showed the efficient and practical considerations necessary for applying pile-up signal correction in high-radiation fields.

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A New Pulse Pileup Rejection Method Based on Position Shift Identification

Cited by 10 publications

References 27 publications

A digital phoswich detector using time-over-threshold for depth of interaction in PET

A digital phoswich detector using time-over-threshold for depth of interaction in PET

Performance Evaluation of G8, a High-Sensitivity Benchtop Preclinical PET/CT Tomograph

Optimizing deep learning-based piled-up pulse height correction method for high radiation-field application

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