We are developing a high-resolution, high-efficiency positron emission tomography (PET) detector module with depth of interaction (DOI) capability based on a lutetium oxyorthosilicate (LSO) scintillator array coupled at both ends to position-sensitive avalanche photodiodes (PSAPDs). In this paper we present the DOI resolution, energy resolution and timing resolution results for complete detector modules. The detector module consists of a 7 x 7 matrix of LSO scintillator crystals (1 x 1 x 20 mm3 in dimension) coupled to 8 x 8 mm2 PSAPDs at both ends. Flood histograms were acquired and used to generate crystal look-up tables. The DOI resolution was measured for individual crystals within the array by using the ratio of the signal amplitudes from the two PSAPDs on an event-by-event basis. A measure of the total scintillation light produced was obtained by summing the signal amplitudes from the two PSAPDs. This summed signal was used to measure the energy resolution. The DOI resolution was measured to be 3-4 mm FWHM irrespective of the position of the crystal within the array, or the interaction location along the length of the crystal. The total light signal and energy resolution was almost independent of the depth of interaction. The measured energy resolution averaged 14% FWHM. The coincidence timing resolution measured using a pair of identical detector modules was 4.5 ns FWHM. These results are consistent with the design goals and the performance required of a compact, high-resolution and high-efficiency PET detector module for small animal and breast imaging applications.
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http://dx.doi.org/10.1088/0031-9155/49/18/007 | DOI Listing |
Sensors (Basel)
January 2025
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China.
With the digital transformation of the manufacturing industry, data monitoring and collecting in the manufacturing process become essential. Pointer meter reading recognition (PMRR) is a key element in data monitoring throughout the manufacturing process. However, existing PMRR methods have low accuracy and insufficient robustness due to issues such as blur, uneven illumination, tilt, and complex backgrounds in meter images.
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January 2025
Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland.
A serious limitation to the deployment of IoT solutions in rural areas may be the lack of available telecommunications infrastructure enabling the continuous collection of measurement data. A nomadic computing system, using a UAV carrying an on-board gateway, can handle this; it leads, however, to a number of technical challenges. One is the intermittent collection of data from ground sensors governed by weather conditions for the UAV measurement missions.
View Article and Find Full Text PDFEur Radiol Exp
January 2025
Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy.
Sci Rep
January 2025
Universite Claude Bernard Lyon 1, INL, UMR5270, CNRS, INSA Lyon, Ecole Centrale de Lyon, CPE Lyon, 69622, Villeurbanne, France.
Synchrotron microbeam radiotherapy (MRT), which has entered the clinical transfer phase, requires the development of appropriate quality assurance (QA) tools due to very high dose rates and spatial hyperfractionation. A microstrip plastic scintillating detector system with associated modules was proposed in the context of real-time MRT QA. A prototype of such a system with 105 scintillating microstrips was developed and tested under MRT conditions.
View Article and Find Full Text PDFJ Imaging
December 2024
School of Innovation, Design and Technology (IDT), Mälardalen University, 72123 Västerås, Sweden.
As the demand for autonomous driving (AD) systems has increased, the enhancement of their safety has become critically important. A fundamental capability of AD systems is object detection and trajectory forecasting of vehicles and pedestrians around the ego-vehicle, which is essential for preventing potential collisions. This study introduces the Deep learning-based Acceleration-aware Trajectory forecasting (DAT) model, a deep learning-based approach for object detection and trajectory forecasting, utilizing raw sensor measurements.
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