The ClearPET. Neuro scanner: A dedicated LSO/LuYAP phoswich small animal PET scanner

Ziemons, K.; Achten, Rik; Auffray, E.; Bauer, Andreas; Brandenburg, G.; Bruyndonckx, Peter; Choi, Yong; Daemen, J.; Dehnhardt, M.; Devroede, O.; Fleischer, Matthias; Fuss, L.; Heinrichs, U.; Hollendung, A.; Holschbach, M.; Khodaverdi, Maryam; Kirchner, Peter T.; Kleines, H.; Krieguer, M.; Larue, H.; Lecoq, P.; Leyendecker, M.; Moeller, Ronald D.; Morel, C.; Mosset, J.-B.; Parl, C.; Pietrzyk, U.; Schmitz, J.; Streun, M.; Tavernier, S.; Veggian, M.; Zimmermann, Egon; Coenen, Heinrich Hubert; Halling, H.; Sievering, R.; Zilles, Karl

doi:10.1109/nssmic.2004.1462747

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…The radiation source in this experiment was 68 Ge [12]. The energy window for the pulses is between 350-650 keV as in [14]. The physical properties of the these crystal illustrated that, the difference between time decay constants of the two types of pulses is 20 nanoseconds [15].…”

Section: Methodsmentioning

confidence: 99%

Pulse shape discrimination of LSO and LuYAP scintillators based on Discrete Fractional Fourier Transform

2020

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Parallax error decreases the accuracy of the Positron Emission Tomography (PET) scanner. One of suitable solutions to reduce this error is to gain the depth of interaction information in a PET which uses the phoswich detectors. The pulse shape of the scintillator material can identify the corresponding layer of interaction within the phoswich detector by using Pulse Shape Discrimination (PSD) methods. In this work, we propose the PSD based on a Discrete Fractional Fourier Transform (DFRFT) to extract the features of the scintillation pulses. Then, we use the Support Vector Machine (SVM) to classify these features. A data set consists of 100 000 scintillation pulses for LSO and LuYAP crystals are discriminated using the proposed method. Different fraction factors of the DFRFT are studied to select the optimum one. Also, the SVM is applied using linear, radial basis function, or quadratic kernel. The highest efficiency of the proposed PSD is 96.3% when the applied fraction factor is 0.8 and a quadratic SVM kernel is used.

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Section: Methodsmentioning

confidence: 99%

Pulse shape discrimination of LSO and LuYAP scintillators based on Discrete Fractional Fourier Transform

2020

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“…The scintillation photons of a 68 Ge source are detected using the PMT which was placed in front of the 68 Ge source [22]. The energy window was between 350-650 keV as in [23]. The energy threshold was adjusted to record complete scintillation spectra of the detector.…”

Section: Methodsmentioning

confidence: 99%

Pulse shape discrimination based on Fractional Discrete Cosine Transform

2019

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One of the most serious errors in PET systems is the parallax error, which occurs due to spatial resolution limitation in PET. This error can be solved by using multilayer scintillation detector (phoswish detector) where each layer has a different decay constant. Each phoswish detector is optically coupled to a single PMT. Hence, by applying pulse shape discrimination (PSD) methods to identify the scintillated layer, the parallax error can be reduced. This paper proposes a PSD method relied on a Fractional Discrete Cosine Transform (FrDCT) and a Support Vector Machine (SVM) classifier. The proposed method can be used to differentiate between two arbitrary types of scintillation pulses where each type has a different decay constant. For demonstration of its performance, the proposed FrDCT-based method is applied to a data set of scintillation pulses of LSO and LuYAP crystals. Furthermore, FrDCT with different factors as well as different kernels of SVM; such as Linear, Quadratic, and Radial Basis Function (RBF) kernels, are used to study their effects on the discrimination efficiency. The highest achieved efficiency is equal to 95.9% for the 0.9 factor of FrDCT using a quadratic kernel SVM. On the other hand, the linear kernel consumes the lowest execution time at the expense of slightly reducing the discrimination efficiency. K: Detectors for Beta autoradiography; Digital signal processing (DSP); Image reconstruction in medical imaging; Scintillators, scintillation and light emission processes (solid, gas and liquid scintillators) 1Corresponding author.

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“…One such example is the clearPET Neuro scanner dedicated for nonhuman primates imaging [64], which uses a phoswich detector block combining two 10 mm crystal layers of lutetium-based (LSO:Ce and LuYAP:Ce) scintillators (see Table 1) segmented into 64 (8x8) detection elements, with a cross section of 2x2 mm 2 coupled to multi-channel photomultiplier tubes. The opening diameter of the ring is variable between 13 and 30 cm for an axial field-of-view of 11 cm, whereas the transaxial field-of-view is adjustable per software.…”

Section: Innovations In Brain Pet Instrumentationmentioning

confidence: 99%

The New Challenges of Brain PET Imaging Technology

Zaidi¹,

Montandon

2006

CMIR

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During the last two decades, functional brain imaging using positron emission tomography (PET) has advanced elegantly, and steadily gained importance in the clinical and research arenas. Significant progress has been made by different scanner manufacturers and research groups in the design of dedicated high-resolution three-dimensional (3-D) PET units; however, emerging clinical and research applications of functional brain imaging promise even greater levels of accuracy and precision and therefore, impose more constraints with respect to the intrinsic performance of the PET tomograph. Continuous efforts to integrate recent research findings for the design of different geometries and various detector/readout technologies of PET scanners have become the goal of both the academic community and nuclear medicine industry. As PET has become integrated into clinical practice, several design trends have developed; with systems now available with a spectrum of features, from those designed for "low cost" clinical applications to others designed specifically for very high-resolution research applications. There is also a continual upward revision and refinement in both hardware and software components for all of these systems.Software-and hardware-based correlation between anatomical (x-ray CT, MRI) and physiological (PET) information is a promising research field and now offers unique capabilities for the medical imaging community and biomedical researchers. One of the main advantages of dual-modality PET/CT imaging is that PET data are intrinsically aligned to anatomical information from the x-ray CT without the use of external markers or internal landmarks, thus providing a reliable estimate of the attenuation map to be used for attenuation and scatter correction purposes. On the other hand, combining PET with MRI technology is scientifically more challenging owing to the strong magnetic fields. Nevertheless, significant progress has been made resulting in the design of a prototype small animal PET scanner coupled to three multichannel photomultipliers via optical fibers, so that the PET detector can be operated within a conventional MR system. Thus, many different design paths have been and continue to be pursued in both academic and corporate settings, that offer different trade-offs in terms of their performance. It still is uncertain which designs will be incorporated into future clinical and research systems, but it is certain that technological advances will continue and will enable new quantitative capabilities in brain PET imaging. This paper briefly summarizes state of the art developments in dedicated brain PET instrumentation. Future prospects will also be discussed.

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The ClearPET. Neuro scanner: A dedicated LSO/LuYAP phoswich small animal PET scanner

Cited by 7 publications

References 10 publications

Pulse shape discrimination of LSO and LuYAP scintillators based on Discrete Fractional Fourier Transform

Pulse shape discrimination of LSO and LuYAP scintillators based on Discrete Fractional Fourier Transform

Pulse shape discrimination based on Fractional Discrete Cosine Transform

The New Challenges of Brain PET Imaging Technology

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