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Petersen E, LaBella A, Li Y, Wang Z, Goldan AH. Resolving inter-crystal scatter in a light-sharing depth-encoding PET detector. Phys Med Biol 2024; 69:035024. [PMID: 38169459 DOI: 10.1088/1361-6560/ad19f1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 01/02/2024] [Indexed: 01/05/2024]
Abstract
Objective.Inter-crystal scattering (ICS) in light-sharing positron emission tomography (PET) detectors leads to ambiguity in positioning the initial interaction, which significantly degrades the contrast, quantitative accuracy, and spatial resolution of the resulting image. Here, we attempt to resolve the positioning ambiguity of ICS in a light-sharing depth-encoding detector by exploiting the confined, deterministic light-sharing enabled by the segmented light guide unique to Prism-PET.Approach.We first considered a test case of ICS between two adjacent crystals using an analytical and a neural network approach. The analytical approach used a Bayesian estimation framework constructed from a scatter absorption model-the prior-and a detector response model-the likelihood. A simple neural network was generated for the same scenario, to provide mutual validation for the findings. Finally, we generalized the solution to three-dimensional event positioning that handles all events in the photopeak using a convolutional neural network with unique architecture that separately predicts the identity and depth-of-interaction (DOI) of the crystal containing the first interaction.Main results.The analytical Bayesian method generated an estimation error of 20.5 keV in energy and 3.1 mm in DOI. Further analysis showed that the detector response model was sufficiently robust to achieve adequate performance via maximum likelihood estimation (MLE), without prior information. We then found convergent results using a simple neural network. In the generalized solution using a convolutional neural network, we found crystal identification accuracy of 83% and DOI estimation error of 3.0 mm across all events. Applying this positioning algorithm to simulated data, we demonstrated significant improvements in image quality over the baseline, centroid-based positioning approach, attaining 38.9% improvement in intrinsic spatial resolution and enhanced clarity in hot spots of diameters 0.8 to 2.5 mm.Significance.The accuracy of our findings exceeds those of previous reports in the literature. The Prism-PET light guide, mediating confined and deterministic light-sharing, plays a key role in ICS recovery, as its mathematical embodiment-the detector response model-was the essential driver of accuracy in our results.
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Affiliation(s)
- Eric Petersen
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States of America
- Department of Radiology, Weill Cornell Medical College, Cornell University, New York, NY, United States of America
| | - Andy LaBella
- Department of Radiology, Stony Brook University, Stony Brook, NY, United States of America
| | - Yixin Li
- Department of Radiology, Weill Cornell Medical College, Cornell University, New York, NY, United States of America
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, United States of America
| | - Zipai Wang
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States of America
- Department of Radiology, Weill Cornell Medical College, Cornell University, New York, NY, United States of America
| | - Amir H Goldan
- Department of Radiology, Weill Cornell Medical College, Cornell University, New York, NY, United States of America
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Gu Y, Wang M, Gong Y, Li X, Wang Z, Wang Y, Jiang S, Zhang D, Li C. Unveiling breast cancer risk profiles: a survival clustering analysis empowered by an online web application. Future Oncol 2023; 19:2651-2667. [PMID: 38095059 DOI: 10.2217/fon-2023-0736] [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] [Indexed: 12/23/2023] Open
Abstract
Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Materials & methods: Analysis is based on the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which contains 1726 subjects and 22 variables. Cox regression was used to identify survival risk factors for K-means clustering. Logrank tests and C-statistics were compared across different cluster numbers and Kaplan-Meier plots were presented. Results & conclusion: Our study fills an existing void by introducing a unique combination of unsupervised learning techniques and survival information on the clinician side, demonstrating the potential of survival clustering as a valuable tool in uncovering hidden structures based on distinct risk profiles.
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Affiliation(s)
- Yuan Gu
- Department of Statistics, The George Washington University, Washington, DC 20052, USA
| | - Mingyue Wang
- Department of Mathematics, Syracuse University, Syracuse, NY 13244, USA
| | - Yishu Gong
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, NY 02115, USA
| | - Xin Li
- Department of Statistics, The George Washington University, Washington, DC 20052, USA
| | - Ziyang Wang
- Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK
| | - Yuli Wang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Song Jiang
- Department of Biochemistry, Huzhou Institute of Biological Products Co., Ltd., 313017, China
| | - Dan Zhang
- Department of Information Science and Engineering, Shandong University, Shan Dong, China
| | - Chen Li
- Department of Biology, Chemistry and Pharmacy, Free University of Berlin, Berlin, 14195, Germany
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Liu Z, Mungai S, Niu M, Kuang Z, Ren N, Wang X, Sang Z, Yang Y. Edge effect reduction of high-resolution PET detectors using LYSO and GAGG phoswich crystals. Phys Med Biol 2023; 68. [PMID: 36808920 DOI: 10.1088/1361-6560/acbde1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/21/2023] [Indexed: 02/23/2023]
Abstract
Objective. Small-animal positron emission tomography (PET) is a powerful preclinical imaging tool in animal model studies. The spatial resolution and sensitivity of current PET scanners developed for small-animal imaging need to be improved to increase the quantitative accuracy of preclinical animal studies. This study aimed to improve the identification capability of edge scintillator crystals of a PET detector which will enable to apply a crystal array with the same cross-section area as the active area of a photodetector for improving the detection area and thus reducing or eliminating the inter-detector gaps.Approach. PET detectors using crystal arrays with mixed lutetium yttrium orthosilicate (LYSO) and gadolinium aluminum gallium garnet (GAGG) crystals were developed and evaluated. The crystal arrays consisted of 31 × 31 array of 0.49 × 0.49 × 20 mm3crystals; they were read out by two silicon photomultiplier arrays with pixel sizes of 2 × 2 mm2that were placed at both ends of the crystal arrays. The second or first outermost layer of the LYSO crystals was replaced by GAGG crystals in the two crystal arrays. The two crystal types were identified using a pulse-shape discrimination technique to provide better edge crystal identification.Main results. Using the pulse shape discrimination technique, almost all (except for a few edge) crystals were resolved in the two detectors; high sensitivity was achieved by using the scintillator array and the photodetector with the same areas and achieved high resolution by using crystals with sizes equal to 0.49 × 0.49 × 20 mm3. Energy resolutions of 19.3 ± 1.8% and 18.9 ± 1.5%, depth-of-interaction resolutions of 2.02 ± 0.17 mm and 2.04 ± 0.18 mm, and timing resolutions of 1.6 ± 0.2 ns and 1.5 ± 0.2 ns were achieved by the two detectors, respectively.Significance. In summary, novel three-dimensional high-resolution PET detectors consisting of a mixture of LYSO and GAGG crystals were developed. The detectors significantly improve the detection area with the same photodetectors and thus improve the detection efficiency.
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Affiliation(s)
- Zheng Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Samuel Mungai
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Ming Niu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Zhonghua Kuang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Ning Ren
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Xiaohui Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Ziru Sang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Yongfeng Yang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
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Cong L, Kuang Z, Sang Z, Wang X, Niu M, Yang Y. Comparison of arithmetic mean and energy-weighted mean flood histogram generation methods for dual-ended readout PET detectors. Med Phys 2022; 49:4455-4465. [PMID: 35567406 DOI: 10.1002/mp.15710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 04/26/2022] [Accepted: 05/03/2022] [Indexed: 11/05/2022] Open
Abstract
PURPOSE Dual-ended readout pixelated scintillator array detectors can provide a suitable crystal resolvability and satisfactory depth of interaction (DOI), energy, and timing resolutions. Usually, the flood histogram measured by one-sided readout is depth dependent, and the flood histogram quality degrades as the distance between the interaction site and photodetector increases. Information measured by two photodetectors must be combined to obtain an improved flood histogram yielding a better PET scanner spatial resolution. METHODS Two flood histogram generation algorithms for dual-ended readout of pixelated scintillator array detectors were compared by theoretical calculations and experimental measurements. The first algorithm is the arithmetic mean (AM) algorithm, which assigns the same weight to the flood histograms measured by photodetectors 1 and 2. The second algorithm is the energy-weighted mean (EWM) algorithm, which assigns each flood histogram a certain weight proportional to the energy measured by the photodetector. Theoretical equations were derived to determine the quality of the flood histograms obtained with these two algorithms. Experimental measurements were performed with an 18 × 18 lutetium-yttrium oxyorthosilicate (LYSO) array with a crystal size of 0.62 × 0.62 × 20 mm3 read out by two multi-anode photomultiplier tubes at both ends. Flood histograms of the whole array and five specific depths were compared between the above two algorithms. RESULTS The theoretical results indicated that the flood histograms obtained with the EWM method matched those obtained with the AM method at the middle detector depth and were better at other detector depths when the distance (S) between the locations of the same crystal in the flood histograms measured by photodetectors 1 and 2 reached 0. The advantage of the EWM method decreased with increasing S value since the crystal position in the flood histogram obtained with the EWM method varies with the depth when S does not equal 0. The advantage of the EWM method decreased with increasing S value. The experimental results generally agreed with the theoretical predictions. Compared to the AM method, the EWM method provided a similar flood histogram at a depth of 10 mm but generated a better flood histogram at depths of 2 and 18 mm. Although an inverse correlation between Q (a quality factor representing the advantage of the EWM method) and S was observed, the variation in Q given the same S value was high. The average Q value at the same S still agreed with the theoretical predictions. CONCLUSIONS Theoretical equations were derived, and experimental measurements were performed to compare two flood histogram generation algorithms for dual-ended readout PET detectors. The results indicated that the EWM method based on inverse variance weighting theory could provide better flood histograms than those provided by the AM method. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Longhan Cong
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China
| | - Zhonghua Kuang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China
| | - Ziru Sang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China
| | - Xiaohui Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China
| | - Ming Niu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China
| | - Yongfeng Yang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China
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Niu M, Liu Z, Kuang Z, Wang X, Ren N, Sang Z, Wu S, Cong L, Sun T, Hu Z, Yang Y. Ultra-high resolution depth-encoding small animal PET detectors: Using GAGG and LYSO crystal arrays. Med Phys 2022; 49:3006-3020. [PMID: 35301730 DOI: 10.1002/mp.15606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 03/03/2022] [Accepted: 03/03/2022] [Indexed: 11/12/2022] Open
Abstract
PURPOSE Small animal PET scanners are widely used in current biomedical research. The study aimed to develop high efficiency and ultra-high resolution detectors that could be used to develop a small animal PET scanner with high sensitivity and spatial resolution approaching to its physical limit. METHODS 4 crystal arrays were fabricated and measured in this study. Crystal arrays 1 and 2 consisted of 38 × 38 GAGG and LYSO crystals of 0.4 × 0.4 × 20 mm3 size. Crystal array 3 consisted of 16 × 16 GAGG crystals of 0.3 × 0.3 × 20 mm3 size, and crystal array 4 consisted of 24 × 24 LYSO crystals 0.3 × 0.3 × 20 mm3 in size. The crystal arrays were dual-ended readouts using 8 × 8 SiPM arrays of 2 × 2 mm2 pixel area. The SiPM array was read-out using a signal multiplexing circuit to convert the 64 output signals into 4 position-encoding signals. The performances of the 4 detectors in terms of flood histogram, energy resolution, depth of interaction resolution and timing resolution were measured. RESULTS The GAGG detectors provided better flood histograms, ∼30% higher photopeak amplitude, ∼20% higher energy resolution, ∼12% worse DOI resolution and ∼15% worse timing resolution compared with LYSO detectors of the same crystal size. These 4 detectors provided DOI resolutions of <2 mm, energy resolutions of <22% and timing resolutions of <1.6 ns. All crystals of 0.4 × 0.4 × 20 mm3 and 0.3 × 0.3 × 20 mm3 could be clearly resolved if the crystal array was 1 mm smaller in the four sides than that in the SiPM array. CONCLUSIONS High DOI resolution PET detectors were developed using both GAGG and LYSO arrays with crystal sizes of 0.3 and 0.4 mm, respectively, and a length of 20 mm. The detectors can be used in the future to develop small animal PET scanners, especially dedicated mouse imaging PET scanners, which can simultaneously achieve high sensitivity and ultra-high spatial resolution. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Ming Niu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zheng Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhonghua Kuang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Xiaohui Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Ning Ren
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Ziru Sang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - San Wu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Longhan Cong
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Tao Sun
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhanli Hu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yongfeng Yang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
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Development and Evaluation of a Dual-Layer-Offset PET Detector Constructed with Different Reflectors. CRYSTALS 2022. [DOI: 10.3390/cryst12010093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Dual-layer-offset or multi-layer-offset design of a PET detector can improve spatial resolution while maintaining high sensitivity. In this study, three dual-layer-offset LYSO detectors with three different reflectors (ESR, Toray, and BaSO4) were developed. The top layer consisted of a 17 × 17 array of crystals 1 × 1 × 6.5 mm3 in size and the bottom layer consisted of an 18 × 18 array of crystals 1 × 1 × 9.5 mm3 in size. Neither light guides nor optical glue were used between the two layers of crystals. A custom-designed electronics system, composed of a 6 × 6 SiPM array, two FPC cables, and a custom-designed data processing module, was used to read out signals. An optimized interaction-decoding algorithm using the center of gravity to determine the position and threshold of analog signals for timing methods was applied to generate decoding flood histograms. The detector performances, in terms of peak to valley ratio of the flood histograms and energy resolutions, were calculated and compared. The dual-layer-offset PET detector constructed with BaSO4 reflectors performed much better than the other two reflectors in both crystal identification and energy resolution. The average peak-to-valley ratio and the energy resolution were approximately 7 and 11%, respectively. In addition, the crystals in the bottom layer showed better performance at crystal identification than those in the top layer. This study can act as a reference providing guidance in choosing scintillator reflectors for multi-layer dedicated DOI detectors designed for small-animal PET imaging.
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