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Zeng X, Zhang Z, Li D, Huang X, Wang Z, Wang Y, Zhou W, Wang P, Zhu M, Wei Q, Gong H, Wei L. Evaluation of monolithic crystal detector with dual-ended readout utilizing multiplexing method. Phys Med Biol 2024; 69:085003. [PMID: 38484392 DOI: 10.1088/1361-6560/ad3417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 03/14/2024] [Indexed: 04/04/2024]
Abstract
Objective.Monolithic crystal detectors are increasingly being applied in positron emission tomography (PET) devices owing to their excellent depth-of-interaction (DOI) resolution capabilities and high detection efficiency. In this study, we constructed and evaluated a dual-ended readout monolithic crystal detector based on a multiplexing method.Approach.We employed two 12 × 12 silicon photomultiplier (SiPM) arrays for readout, and the signals from the 12 × 12 array were merged into 12 X and 12 Y channels using channel multiplexing. In 2D reconstruction, three methods based on the centre of gravity (COG) were compared, and the concept of thresholds was introduced. Furthermore, a light convolutional neural network (CNN) was employed for testing. To enhance depth localization resolution, we proposed a method by utilizing the mutual information from both ends of the SiPMs. The source width and collimation effect were simulated using GEANT4, and the intrinsic spatial resolution was separated from the measured values.Main results.At an operational voltage of 29 V for the SiPM, an energy resolution of approximately 12.5 % was achieved. By subtracting a 0.8 % threshold from the total energy in every channel, a 2D spatial resolution of approximately 0.90 mm full width at half maximum (FWHM) can be obtained. Furthermore, a higher level of resolution, approximately 0.80 mm FWHM, was achieved using a CNN, with some alleviation of edge effects. With the proposed DOI method, a significant 1.36 mm FWHM average DOI resolution can be achieved. Additionally, it was found that polishing and black coating on the crystal surface yielded smaller edge effects compared to a rough surface with a black coating.Significance.The introduction of a threshold in COG method and a dual-ended readout scheme can lead to excellent spatial resolution for monolithic crystal detectors, which can help to develop PET systems with both high sensitivity and high spatial resolution.
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Affiliation(s)
- Xiangtao Zeng
- Beijing Engineering Research Centre of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
- CAEA Centre of Excellence on Nuclear Technology Applications for Nuclear Detection and Imaging, Beijing 100049, People's Republic of China
| | - Zhiming Zhang
- Beijing Engineering Research Centre of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
- CAEA Centre of Excellence on Nuclear Technology Applications for Nuclear Detection and Imaging, Beijing 100049, People's Republic of China
| | - Daowu Li
- Beijing Engineering Research Centre of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
- CAEA Centre of Excellence on Nuclear Technology Applications for Nuclear Detection and Imaging, Beijing 100049, People's Republic of China
| | - Xianchao Huang
- Beijing Engineering Research Centre of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
- CAEA Centre of Excellence on Nuclear Technology Applications for Nuclear Detection and Imaging, Beijing 100049, People's Republic of China
| | - Zhuoran Wang
- Beijing Engineering Research Centre of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
- CAEA Centre of Excellence on Nuclear Technology Applications for Nuclear Detection and Imaging, Beijing 100049, People's Republic of China
| | - Yingjie Wang
- Beijing Engineering Research Centre of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
- CAEA Centre of Excellence on Nuclear Technology Applications for Nuclear Detection and Imaging, Beijing 100049, People's Republic of China
| | - Wei Zhou
- Beijing Engineering Research Centre of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
- CAEA Centre of Excellence on Nuclear Technology Applications for Nuclear Detection and Imaging, Beijing 100049, People's Republic of China
| | - Peilin Wang
- Beijing Engineering Research Centre of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
- CAEA Centre of Excellence on Nuclear Technology Applications for Nuclear Detection and Imaging, Beijing 100049, People's Republic of China
| | - Meiling Zhu
- Beijing Engineering Research Centre of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
- CAEA Centre of Excellence on Nuclear Technology Applications for Nuclear Detection and Imaging, Beijing 100049, People's Republic of China
| | - Qing Wei
- Beijing Engineering Research Centre of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
- CAEA Centre of Excellence on Nuclear Technology Applications for Nuclear Detection and Imaging, Beijing 100049, People's Republic of China
| | - Huixing Gong
- Beijing Engineering Research Centre of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
- CAEA Centre of Excellence on Nuclear Technology Applications for Nuclear Detection and Imaging, Beijing 100049, People's Republic of China
| | - Long Wei
- Beijing Engineering Research Centre of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
- CAEA Centre of Excellence on Nuclear Technology Applications for Nuclear Detection and Imaging, Beijing 100049, People's Republic of China
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Schwenck J, Kneilling M, Riksen NP, la Fougère C, Mulder DJ, Slart RJHA, Aarntzen EHJG. A role for artificial intelligence in molecular imaging of infection and inflammation. Eur J Hybrid Imaging 2022; 6:17. [PMID: 36045228 PMCID: PMC9433558 DOI: 10.1186/s41824-022-00138-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/16/2022] [Indexed: 12/03/2022] Open
Abstract
The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers’ expertise. Although molecular imaging, like [18F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.
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Decuyper M, Maebe J, Van Holen R, Vandenberghe S. Artificial intelligence with deep learning in nuclear medicine and radiology. EJNMMI Phys 2021; 8:81. [PMID: 34897550 PMCID: PMC8665861 DOI: 10.1186/s40658-021-00426-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 11/19/2021] [Indexed: 12/19/2022] Open
Abstract
The use of deep learning in medical imaging has increased rapidly over the past few years, finding applications throughout the entire radiology pipeline, from improved scanner performance to automatic disease detection and diagnosis. These advancements have resulted in a wide variety of deep learning approaches being developed, solving unique challenges for various imaging modalities. This paper provides a review on these developments from a technical point of view, categorizing the different methodologies and summarizing their implementation. We provide an introduction to the design of neural networks and their training procedure, after which we take an extended look at their uses in medical imaging. We cover the different sections of the radiology pipeline, highlighting some influential works and discussing the merits and limitations of deep learning approaches compared to other traditional methods. As such, this review is intended to provide a broad yet concise overview for the interested reader, facilitating adoption and interdisciplinary research of deep learning in the field of medical imaging.
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Affiliation(s)
- Milan Decuyper
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Jens Maebe
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Roel Van Holen
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Stefaan Vandenberghe
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
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