Alastair J Gemmell, Colin M Brown, Surajit Ray, Alexander Small
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Sixty-seven Textural Analysis features were extracted and tested for robustness and volume dependency.</p><p><strong>Results: </strong>Features showing high volume dependency or high Coefficient of Variation (indicating poor repeatability) were removed from the list of features that may be suitable for use in clinical studies. After feature reduction, there were 39 features for <sup>99 m</sup>Tc and 33 features for <sup>177</sup>Lu remaining.</p><p><strong>Conclusion: </strong>The use of a uniform phantom to test volume dependency and a Revolver phantom to identify repeatable Textural Analysis features is possible for quantitative SPECT using <sup>99 m</sup>Tc or <sup>177</sup>Lu. Selection of such features is likely to be centre-dependent due to differences in camera performance as well as acquisition and reconstruction protocols.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"12 1","pages":"40"},"PeriodicalIF":3.0000,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12006590/pdf/","citationCount":"0","resultStr":"{\"title\":\"Robustness of textural analysis features in quantitative <sup>99 m</sup>Tc and <sup>177</sup>Lu SPECT-CT phantom acquisitions.\",\"authors\":\"Alastair J Gemmell, Colin M Brown, Surajit Ray, Alexander Small\",\"doi\":\"10.1186/s40658-025-00749-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Textural Analysis features in molecular imaging require to be robust under repeat measurement and to be independent of volume for optimum use in clinical studies. 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引用次数: 0
摘要
背景:分子成像的结构分析特征要求在重复测量下具有鲁棒性,并且独立于体积,以便在临床研究中最佳使用。最近的EANM和SNMMI放射组学指南提供了关于使用幻影识别稳健特征的潜在建议(Hatt in EJNMMI, 2022)。本研究将所建议的幻影应用于两种放射性核素99mtc和177Lu的SPECT定量。方法:使用统一的模体来测试体积依赖性,并使用定制的“左轮手枪”模体,基于Hatt (EJNMMI, 2022)中描述的PET模体,但对SPECT进行了局部调整。每个幻膜分别填充99mtc和177Lu。提取了67个纹理分析特征,并对其鲁棒性和体积依赖性进行了测试。结果:显示高容量依赖性或高变异系数(表明重复性差)的特征从可能适合用于临床研究的特征列表中删除。特征减除后,99mtc有39个特征,177Lu有33个特征。结论:使用均匀模体来测试体积依赖性,使用左轮手枪模体来识别可重复的纹理分析特征,可以使用99 mTc或177Lu进行定量SPECT。由于相机性能以及采集和重建协议的差异,这些特征的选择可能是中心依赖的。
Robustness of textural analysis features in quantitative 99 mTc and 177Lu SPECT-CT phantom acquisitions.
Background: Textural Analysis features in molecular imaging require to be robust under repeat measurement and to be independent of volume for optimum use in clinical studies. Recent EANM and SNMMI guidelines for radiomics provide advice on the potential use of phantoms to identify robust features (Hatt in EJNMMI, 2022). This study applies the suggested phantoms to use in SPECT quantification for two radionuclides, 99 mTc and 177Lu.
Methods: Acquisitions were made with a uniform phantom to test volume dependency and with a customised 'Revolver' phantom, based on the PET phantom described in Hatt (EJNMMI, 2022) but with local adaptations for SPECT. Each phantom was filled separately with 99 mTc and 177Lu. Sixty-seven Textural Analysis features were extracted and tested for robustness and volume dependency.
Results: Features showing high volume dependency or high Coefficient of Variation (indicating poor repeatability) were removed from the list of features that may be suitable for use in clinical studies. After feature reduction, there were 39 features for 99 mTc and 33 features for 177Lu remaining.
Conclusion: The use of a uniform phantom to test volume dependency and a Revolver phantom to identify repeatable Textural Analysis features is possible for quantitative SPECT using 99 mTc or 177Lu. Selection of such features is likely to be centre-dependent due to differences in camera performance as well as acquisition and reconstruction protocols.
期刊介绍:
EJNMMI Physics is an international platform for scientists, users and adopters of nuclear medicine with a particular interest in physics matters. As a companion journal to the European Journal of Nuclear Medicine and Molecular Imaging, this journal has a multi-disciplinary approach and welcomes original materials and studies with a focus on applied physics and mathematics as well as imaging systems engineering and prototyping in nuclear medicine. This includes physics-driven approaches or algorithms supported by physics that foster early clinical adoption of nuclear medicine imaging and therapy.