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基于硅光电倍增管的大型伽马相机像素设计优化

Optimization of the Pixel Design for Large Gamma Cameras Based on Silicon Photomultipliers.

作者信息

Wunderlich Carolin, Paoletti Riccardo, Guberman Daniel

机构信息

Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Pisa, 56126 Pisa, Italy.

Dipartimento di Scienze Fisiche, della Terra e dell'Ambiente, Università di Siena, 53100 Siena, Italy.

出版信息

Sensors (Basel). 2024 Sep 19;24(18):6052. doi: 10.3390/s24186052.

DOI:10.3390/s24186052
PMID:39338796
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11435885/
Abstract

Most single-photon emission computed tomography (SPECT) scanners employ a gamma camera with a large scintillator crystal and 50-100 large photomultiplier tubes (PMTs). In the past, we proposed that the weight, size and cost of a scanner could be reduced by replacing the PMTs with large-area silicon photomultiplier (SiPM) pixels in which commercial SiPMs are summed to reduce the number of readout channels. We studied the feasibility of that solution with a small homemade camera, but the question on how it could be implemented in a large camera remained open. In this work, we try to answer this question by performing Geant4 simulations of a full-body SPECT camera. We studied how the pixel size, shape and noise could affect its energy and spatial resolution. Our results suggest that it would be possible to obtain an intrinsic spatial resolution of a few mm FWHM and an energy resolution at 140 keV close to 10%, even if using pixels more than 20 times larger than standard commercial SiPMs of 6 × 6 mm2. We have also found that if SiPMs are distributed following a honeycomb structure, the spatial resolution is significantly better than if using square pixels distributed in a square grid.

摘要

大多数单光子发射计算机断层扫描(SPECT)扫描仪采用带有大尺寸闪烁晶体和50 - 100个大型光电倍增管(PMT)的伽马相机。过去,我们曾提出,通过用大面积硅光电倍增管(SiPM)像素取代PMT,可以降低扫描仪的重量、尺寸和成本,其中将商用SiPM进行求和以减少读出通道的数量。我们用一台小型自制相机研究了该解决方案的可行性,但关于如何在大型相机中实现这一方案的问题仍未解决。在这项工作中,我们通过对一台全身SPECT相机进行Geant4模拟来尝试回答这个问题。我们研究了像素尺寸、形状和噪声如何影响其能量分辨率和空间分辨率。我们的结果表明,即使使用比标准商用6×6 mm² SiPM大20倍以上的像素,也有可能获得几毫米半高宽(FWHM)的固有空间分辨率以及接近10%的140 keV能量分辨率。我们还发现,如果SiPM按照蜂窝结构分布,其空间分辨率明显优于以正方形网格分布的方形像素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/8a6d1fcc803d/sensors-24-06052-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/957e710a8b86/sensors-24-06052-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/0cbc970f7a2c/sensors-24-06052-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/159294ec86d1/sensors-24-06052-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/08c8847358c4/sensors-24-06052-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/8b609ade853a/sensors-24-06052-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/084622f7432e/sensors-24-06052-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/c043910175f0/sensors-24-06052-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/a0db1b936cb4/sensors-24-06052-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/11185745bcb8/sensors-24-06052-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/c4173861ef02/sensors-24-06052-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/8a6d1fcc803d/sensors-24-06052-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/957e710a8b86/sensors-24-06052-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/0cbc970f7a2c/sensors-24-06052-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/159294ec86d1/sensors-24-06052-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/08c8847358c4/sensors-24-06052-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/8b609ade853a/sensors-24-06052-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/084622f7432e/sensors-24-06052-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/c043910175f0/sensors-24-06052-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/a0db1b936cb4/sensors-24-06052-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/11185745bcb8/sensors-24-06052-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/c4173861ef02/sensors-24-06052-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/508c/11435885/8a6d1fcc803d/sensors-24-06052-g011.jpg

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本文引用的文献

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Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects.机器学习和深度学习在 SPECT 和 PET 成像中的应用:概述、挑战和未来展望。
Pharmacol Res. 2023 Nov;197:106984. doi: 10.1016/j.phrs.2023.106984. Epub 2023 Nov 7.
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NEMA NU 1-2018 performance characterization and Monte Carlo model validation of the Cubresa Spark SiPM-based preclinical SPECT scanner.基于古巴雷斯(Cubresa)火花硅光电倍增管(SiPM)的临床前单光子发射计算机断层扫描(SPECT)扫描仪的NEMA NU 1-2018性能表征与蒙特卡罗模型验证
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Large-Area SiPM Pixels (LASiPs): A cost-effective solution towards compact large SPECT cameras.
大面积硅光电倍增管像素(LASiPs):一种用于紧凑型大 SPECT 相机的经济有效解决方案。
Phys Med. 2021 Feb;82:171-184. doi: 10.1016/j.ejmp.2021.01.066. Epub 2021 Feb 25.
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