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用于评估图像质量的物理参数与视觉分析之间的关系:一项针对脑SPECT的多中心、多厂商体模研究

Relationship between physical parameters and visual analysis for assessment of image quality: a multi-center and multi-vendor phantom study in brain SPECT.

作者信息

Sugiura Akie, Shibutani Takayuki, Onoguchi Masahisa, Nagaki Akio, Tsuboi Kotatsu, Ito Toshimune, Ichikawa Hajime

机构信息

Department of Quantum Medical Technology, Graduate School of Medical Sciences, Kanazawa University.

Kanazawa, Ishikawa, Japan.

出版信息

Asia Ocean J Nucl Med Biol. 2025;13(1):42-52. doi: 10.22038/aojnmb.2024.75204.1524.

DOI:10.22038/aojnmb.2024.75204.1524
PMID:39744054
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11682472/
Abstract

OBJECTIVES

Brain perfusion single-photon emission computed tomography (SPECT) image quality varies depending on SPECT systems. This study aimed to evaluate the relationship between physical parameters and visual analysis for assessment of the brain SPECT image quality. We conducted our phantom study under various conditions in a multi-center and multi-vendor study.

METHODS

SPECT images of the brain phantom were acquired from eight devices in five institutions. The phantom was filled with 28 kBq/ml of Tc solution at the start of scanning. We obtained various data with different acquisition times under clinical reconstruction and acquisition conditions at each institution. Four physical parameters (percent contrast, contrast noise ratio (CNR), asymmetry index (AI), and sharpness index (SI)) were measured with the phantom. Seven observers blindly evaluated all image series and scored them on a scale of 1-3 using four checkpoints: contrast, image noise, symmetry, and sharpness. The average score for all observers was calculated.

RESULTS

CNR increased with increasing visual analysis scores for contrast and image noise, both of which were significantly different between the group with scores "<2" and the group with scores "≥2 and <3". AI decreased as the visual analysis score for symmetry increased, and the AI of both groups with scores "≥2 and <3" and "3" were significantly lower than that of the group with scores "<2". Conversely, no relationship with visual analysis was found for percent contrast and SI.

CONCLUSION

We clarified the relationship between physical parameters and visual analysis of a brain phantom in a multi-center and multi-vendor study. CNR and AI showed agreement with visual analysis.

摘要

目的

脑灌注单光子发射计算机断层扫描(SPECT)图像质量因SPECT系统而异。本研究旨在评估物理参数与视觉分析之间的关系,以评估脑SPECT图像质量。我们在多中心、多供应商的研究中,在各种条件下进行了体模研究。

方法

从五个机构的八台设备获取脑体模的SPECT图像。扫描开始时,体模中填充28 kBq/ml的锝溶液。我们在每个机构的临床重建和采集条件下,以不同的采集时间获得了各种数据。用体模测量了四个物理参数(对比度百分比、对比噪声比(CNR)、不对称指数(AI)和锐度指数(SI))。七名观察者对所有图像系列进行盲法评估,并使用四个检查点(对比度、图像噪声、对称性和锐度)在1-3分的范围内对其进行评分。计算所有观察者的平均得分。

结果

CNR随着对比度和图像噪声视觉分析得分的增加而增加,得分“<2”的组与得分“≥2且<3”的组之间在这两个方面均有显著差异。AI随着对称性视觉分析得分的增加而降低,得分“≥2且<3”和“3”的两组的AI均显著低于得分“<2”的组。相反,未发现对比度百分比和SI与视觉分析之间存在关系。

结论

我们在多中心、多供应商的研究中阐明了脑体模物理参数与视觉分析之间的关系。CNR和AI与视觉分析结果一致。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae4/11682472/5272c4edae8c/AOJNMB-13-42-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae4/11682472/e454180ad0ae/AOJNMB-13-42-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae4/11682472/c8829c79954d/AOJNMB-13-42-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae4/11682472/ee2c723ca4eb/AOJNMB-13-42-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae4/11682472/72be0839b4c3/AOJNMB-13-42-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae4/11682472/d405058185d1/AOJNMB-13-42-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae4/11682472/5272c4edae8c/AOJNMB-13-42-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae4/11682472/e454180ad0ae/AOJNMB-13-42-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae4/11682472/c8829c79954d/AOJNMB-13-42-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae4/11682472/ee2c723ca4eb/AOJNMB-13-42-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae4/11682472/72be0839b4c3/AOJNMB-13-42-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae4/11682472/d405058185d1/AOJNMB-13-42-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae4/11682472/5272c4edae8c/AOJNMB-13-42-g006.jpg

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本文引用的文献

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Attenuation correction using deep learning for brain perfusion SPECT images.基于深度学习的脑灌注 SPECT 图像衰减校正。
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