Maria Agnese Pirozzi, Antonietta Canna, Federica Di Nardo, Mario Sansone, Francesca Trojsi, Mario Cirillo, Fabrizio Esposito
{"title":"大脑皮层和主要皮层下结构的定量磁感应强度成像指标的可靠性。","authors":"Maria Agnese Pirozzi, Antonietta Canna, Federica Di Nardo, Mario Sansone, Francesca Trojsi, Mario Cirillo, Fabrizio Esposito","doi":"10.1111/jon.13234","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background and purpose</h3>\n \n <p>Susceptibility estimates derived from quantitative susceptibility mapping (QSM) images for the cerebral cortex and major subcortical structures are variably reported in brain magnetic resonance imaging (MRI) studies, as average of all (<span></span><math>\n <semantics>\n <msub>\n <mi>μ</mi>\n <mi>all</mi>\n </msub>\n <annotation>${{{{\\mu}}}_{{\\mathrm{all}}}}$</annotation>\n </semantics></math>), absolute (<span></span><math>\n <semantics>\n <msub>\n <mi>μ</mi>\n <mi>abs</mi>\n </msub>\n <annotation>${{{{\\mu}}}_{{\\mathrm{abs}}}}$</annotation>\n </semantics></math>), or positive- (<span></span><math>\n <semantics>\n <msub>\n <mi>μ</mi>\n <mi>p</mi>\n </msub>\n <annotation>${{{{\\mu}}}_{\\mathrm{p}}}$</annotation>\n </semantics></math>) and negative-only (<span></span><math>\n <semantics>\n <msub>\n <mi>μ</mi>\n <mi>n</mi>\n </msub>\n <annotation>${{{{\\mu}}}_{\\mathrm{n}}}$</annotation>\n </semantics></math>) susceptibility values using a region of interest (ROI) approach.</p>\n \n <p>This pilot study presents a reliability analysis of currently used ROI-QSM metrics and an alternative ROI-based approach to obtain voxel-weighted ROI-QSM metrics (<span></span><math>\n <semantics>\n <msub>\n <mi>μ</mi>\n <mi>wp</mi>\n </msub>\n <annotation>${{{{\\mu}}}_{{\\mathrm{wp}}}}$</annotation>\n </semantics></math> and <span></span><math>\n <semantics>\n <msub>\n <mi>μ</mi>\n <mi>wn</mi>\n </msub>\n <annotation>${{{{\\mu}}}_{{\\mathrm{wn}}}}$</annotation>\n </semantics></math>).</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Ten healthy subjects underwent repeated (test-retest) 3-dimensional multi-echo gradient-echo (3DMEGE) 3 Tesla MRI measurements. Complex-valued 3DMEGE images were acquired and reconstructed with slice thicknesses of 1 and 2 mm (3DMEGE1, 3DMEGE2) along with 3DT1-weighted isometric (voxel 1 mm<sup>3</sup>) images for independent registration and ROI segmentation.</p>\n \n <p>Agreement, consistency, and reproducibility of ROI-QSM metrics were assessed through Bland-Altman analysis, intraclass correlation coefficient, and interscan and intersubject coefficient of variation (CoV).</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>All ROI-QSM metrics exhibited good to excellent consistency and test-retest agreement with no proportional bias. Interscan CoV was higher for <span></span><math>\n <semantics>\n <msub>\n <mi>μ</mi>\n <mi>all</mi>\n </msub>\n <annotation>${{{{\\mu}}}_{{\\mathrm{all}}}}$</annotation>\n </semantics></math> in comparison to the other metrics where it was below 15%, in both 3DMEGE1 and 3DMEGE2 datasets. Intersubject CoV for <span></span><math>\n <semantics>\n <msub>\n <mi>μ</mi>\n <mi>all</mi>\n </msub>\n <annotation>${{{{\\mu}}}_{{\\mathrm{all}}}}$</annotation>\n </semantics></math> and <span></span><math>\n <semantics>\n <msub>\n <mi>μ</mi>\n <mi>abs</mi>\n </msub>\n <annotation>${{{{\\mu}}}_{{\\mathrm{abs}}}}$</annotation>\n </semantics></math> exceeded 50% in all ROIs.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Among the evaluated ROI-QSM metrics, <span></span><math>\n <semantics>\n <msub>\n <mi>μ</mi>\n <mi>all</mi>\n </msub>\n <annotation>${{{{\\mu}}}_{{\\mathrm{all}}}}$</annotation>\n </semantics></math> and <span></span><math>\n <semantics>\n <msub>\n <mi>μ</mi>\n <mi>abs</mi>\n </msub>\n <annotation>${{{{\\mu}}}_{{\\mathrm{abs}}}}$</annotation>\n </semantics></math> estimates were less reliable, whereas separating positive and negative values (using <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>μ</mi>\n <mi>p</mi>\n </msub>\n <mo>,</mo>\n <mspace></mspace>\n <msub>\n <mi>μ</mi>\n <mi>n</mi>\n </msub>\n <mo>,</mo>\n <mspace></mspace>\n <msub>\n <mi>μ</mi>\n <mi>wp</mi>\n </msub>\n <mo>,</mo>\n <mspace></mspace>\n <msub>\n <mi>μ</mi>\n <mi>wn</mi>\n </msub>\n </mrow>\n <annotation>${{{{\\mu}}}_{\\mathrm{p}}},\\ {{{{\\mu}}}_{\\mathrm{n}}},\\ {{{{\\mu}}}_{{\\mathrm{wp}}}},\\ {{{{\\mu}}}_{{\\mathrm{wn}}}}$</annotation>\n </semantics></math>) improved the reproducibility within, and the comparability between, subjects, even when reducing the slice thickness. These preliminary findings may offer valuable insights toward standardizing ROI-QSM metrics across different patient cohorts and imaging settings in future clinical MRI studies.</p>\n </section>\n </div>","PeriodicalId":16399,"journal":{"name":"Journal of Neuroimaging","volume":"34 6","pages":"720-731"},"PeriodicalIF":2.3000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jon.13234","citationCount":"0","resultStr":"{\"title\":\"Reliability of quantitative magnetic susceptibility imaging metrics for cerebral cortex and major subcortical structures\",\"authors\":\"Maria Agnese Pirozzi, Antonietta Canna, Federica Di Nardo, Mario Sansone, Francesca Trojsi, Mario Cirillo, Fabrizio Esposito\",\"doi\":\"10.1111/jon.13234\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background and purpose</h3>\\n \\n <p>Susceptibility estimates derived from quantitative susceptibility mapping (QSM) images for the cerebral cortex and major subcortical structures are variably reported in brain magnetic resonance imaging (MRI) studies, as average of all (<span></span><math>\\n <semantics>\\n <msub>\\n <mi>μ</mi>\\n <mi>all</mi>\\n </msub>\\n <annotation>${{{{\\\\mu}}}_{{\\\\mathrm{all}}}}$</annotation>\\n </semantics></math>), absolute (<span></span><math>\\n <semantics>\\n <msub>\\n <mi>μ</mi>\\n <mi>abs</mi>\\n </msub>\\n <annotation>${{{{\\\\mu}}}_{{\\\\mathrm{abs}}}}$</annotation>\\n </semantics></math>), or positive- (<span></span><math>\\n <semantics>\\n <msub>\\n <mi>μ</mi>\\n <mi>p</mi>\\n </msub>\\n <annotation>${{{{\\\\mu}}}_{\\\\mathrm{p}}}$</annotation>\\n </semantics></math>) and negative-only (<span></span><math>\\n <semantics>\\n <msub>\\n <mi>μ</mi>\\n <mi>n</mi>\\n </msub>\\n <annotation>${{{{\\\\mu}}}_{\\\\mathrm{n}}}$</annotation>\\n </semantics></math>) susceptibility values using a region of interest (ROI) approach.</p>\\n \\n <p>This pilot study presents a reliability analysis of currently used ROI-QSM metrics and an alternative ROI-based approach to obtain voxel-weighted ROI-QSM metrics (<span></span><math>\\n <semantics>\\n <msub>\\n <mi>μ</mi>\\n <mi>wp</mi>\\n </msub>\\n <annotation>${{{{\\\\mu}}}_{{\\\\mathrm{wp}}}}$</annotation>\\n </semantics></math> and <span></span><math>\\n <semantics>\\n <msub>\\n <mi>μ</mi>\\n <mi>wn</mi>\\n </msub>\\n <annotation>${{{{\\\\mu}}}_{{\\\\mathrm{wn}}}}$</annotation>\\n </semantics></math>).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Ten healthy subjects underwent repeated (test-retest) 3-dimensional multi-echo gradient-echo (3DMEGE) 3 Tesla MRI measurements. Complex-valued 3DMEGE images were acquired and reconstructed with slice thicknesses of 1 and 2 mm (3DMEGE1, 3DMEGE2) along with 3DT1-weighted isometric (voxel 1 mm<sup>3</sup>) images for independent registration and ROI segmentation.</p>\\n \\n <p>Agreement, consistency, and reproducibility of ROI-QSM metrics were assessed through Bland-Altman analysis, intraclass correlation coefficient, and interscan and intersubject coefficient of variation (CoV).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>All ROI-QSM metrics exhibited good to excellent consistency and test-retest agreement with no proportional bias. Interscan CoV was higher for <span></span><math>\\n <semantics>\\n <msub>\\n <mi>μ</mi>\\n <mi>all</mi>\\n </msub>\\n <annotation>${{{{\\\\mu}}}_{{\\\\mathrm{all}}}}$</annotation>\\n </semantics></math> in comparison to the other metrics where it was below 15%, in both 3DMEGE1 and 3DMEGE2 datasets. Intersubject CoV for <span></span><math>\\n <semantics>\\n <msub>\\n <mi>μ</mi>\\n <mi>all</mi>\\n </msub>\\n <annotation>${{{{\\\\mu}}}_{{\\\\mathrm{all}}}}$</annotation>\\n </semantics></math> and <span></span><math>\\n <semantics>\\n <msub>\\n <mi>μ</mi>\\n <mi>abs</mi>\\n </msub>\\n <annotation>${{{{\\\\mu}}}_{{\\\\mathrm{abs}}}}$</annotation>\\n </semantics></math> exceeded 50% in all ROIs.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Among the evaluated ROI-QSM metrics, <span></span><math>\\n <semantics>\\n <msub>\\n <mi>μ</mi>\\n <mi>all</mi>\\n </msub>\\n <annotation>${{{{\\\\mu}}}_{{\\\\mathrm{all}}}}$</annotation>\\n </semantics></math> and <span></span><math>\\n <semantics>\\n <msub>\\n <mi>μ</mi>\\n <mi>abs</mi>\\n </msub>\\n <annotation>${{{{\\\\mu}}}_{{\\\\mathrm{abs}}}}$</annotation>\\n </semantics></math> estimates were less reliable, whereas separating positive and negative values (using <span></span><math>\\n <semantics>\\n <mrow>\\n <msub>\\n <mi>μ</mi>\\n <mi>p</mi>\\n </msub>\\n <mo>,</mo>\\n <mspace></mspace>\\n <msub>\\n <mi>μ</mi>\\n <mi>n</mi>\\n </msub>\\n <mo>,</mo>\\n <mspace></mspace>\\n <msub>\\n <mi>μ</mi>\\n <mi>wp</mi>\\n </msub>\\n <mo>,</mo>\\n <mspace></mspace>\\n <msub>\\n <mi>μ</mi>\\n <mi>wn</mi>\\n </msub>\\n </mrow>\\n <annotation>${{{{\\\\mu}}}_{\\\\mathrm{p}}},\\\\ {{{{\\\\mu}}}_{\\\\mathrm{n}}},\\\\ {{{{\\\\mu}}}_{{\\\\mathrm{wp}}}},\\\\ {{{{\\\\mu}}}_{{\\\\mathrm{wn}}}}$</annotation>\\n </semantics></math>) improved the reproducibility within, and the comparability between, subjects, even when reducing the slice thickness. These preliminary findings may offer valuable insights toward standardizing ROI-QSM metrics across different patient cohorts and imaging settings in future clinical MRI studies.</p>\\n </section>\\n </div>\",\"PeriodicalId\":16399,\"journal\":{\"name\":\"Journal of Neuroimaging\",\"volume\":\"34 6\",\"pages\":\"720-731\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jon.13234\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Neuroimaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jon.13234\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neuroimaging","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jon.13234","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景和目的：在脑磁共振成像（MRI）研究中，根据定量易感度图（QSM）图像得出的大脑皮层和主要皮层下结构的易感度估计值被不同程度地报告为所有的平均值（μ all ${{{{\mu}}}_{\mathrm{all}}}}$ ）、绝对值（μ abs ${{{{\mu}}}_{\mathrm{abs}}}}$ ），或使用感兴趣区（ROI）方法的正感（μ p ${{{{\mu}}}_{\mathrm{p}}$ ）和负感（μ n ${{{{\mu}}}_{\mathrm{n}}$ ）。本试验研究对目前使用的ROI-QSM指标和另一种基于ROI的方法进行了可靠性分析，以获得体素加权的ROI-QSM指标（μ wp ${{{{\mu}}}_{{mathrm{wp}}}}$ 和 μ wn ${{{{\mu}}}_{\mathrm{wn}}}}$ ）。方法：十名健康受试者接受了重复（测试-再测试）三维多回波梯度回波（3DMEGE）3 特斯拉磁共振成像测量。采集并重建了切片厚度为 1 毫米和 2 毫米的复值 3DMEGE 图像（3DMEGE1、3DMEGE2）以及 3DT1 加权等距（体素 1 mm3）图像，用于独立配准和 ROI 分割。通过Bland-Altman分析、类内相关系数、扫描间和受试者间变异系数（CoV）评估了ROI-QSM指标的一致性、再现性和可重复性：结果：所有 ROI-QSM 指标都表现出良好到卓越的一致性和测试-再测试一致性，没有比例偏差。在3DMEGE1和3DMEGE2数据集中，μ all ${{{{\mu}}}_{\mathrm{all}}}}$的扫描间变异系数高于其他指标，后者低于15%。在所有ROI中，μ all ${{{{\mu}}_{\mathrm{all}}}}$ 和 μ abs ${{{{\mu}}_{\mathrm{abs}}}}$ 的受试者间CoV都超过了50%.结论：在所评估的 ROI-QSM 指标中，μ all ${{{{\mu}}_{{mathrm{all}}}}$ 和 μ abs ${{{{\mu}}_{{mathrm{abs}}}}$ 估计值的可靠性较低，而正负值分离（使用 μ p , μ n , μ wp 、 μ wn ${{{{\mu}}}_{\mathrm{p}}},\ {{{{\mu}}}_{\mathrm{n}}},\ {{{{\mu}}}_{\mathrm{wp}}}},\ {{{{\mu}}}_{\mathrm{wn}}}}$ ）提高了受试者内部的可重复性和受试者之间的可比性，即使在减少切片厚度的情况下也是如此。这些初步研究结果可能会为未来临床磁共振成像研究中不同患者群和成像环境下的ROI-QSM指标标准化提供有价值的见解。
Reliability of quantitative magnetic susceptibility imaging metrics for cerebral cortex and major subcortical structures
Background and purpose
Susceptibility estimates derived from quantitative susceptibility mapping (QSM) images for the cerebral cortex and major subcortical structures are variably reported in brain magnetic resonance imaging (MRI) studies, as average of all (), absolute (), or positive- () and negative-only () susceptibility values using a region of interest (ROI) approach.
This pilot study presents a reliability analysis of currently used ROI-QSM metrics and an alternative ROI-based approach to obtain voxel-weighted ROI-QSM metrics ( and ).
Methods
Ten healthy subjects underwent repeated (test-retest) 3-dimensional multi-echo gradient-echo (3DMEGE) 3 Tesla MRI measurements. Complex-valued 3DMEGE images were acquired and reconstructed with slice thicknesses of 1 and 2 mm (3DMEGE1, 3DMEGE2) along with 3DT1-weighted isometric (voxel 1 mm3) images for independent registration and ROI segmentation.
Agreement, consistency, and reproducibility of ROI-QSM metrics were assessed through Bland-Altman analysis, intraclass correlation coefficient, and interscan and intersubject coefficient of variation (CoV).
Results
All ROI-QSM metrics exhibited good to excellent consistency and test-retest agreement with no proportional bias. Interscan CoV was higher for in comparison to the other metrics where it was below 15%, in both 3DMEGE1 and 3DMEGE2 datasets. Intersubject CoV for and exceeded 50% in all ROIs.
Conclusions
Among the evaluated ROI-QSM metrics, and estimates were less reliable, whereas separating positive and negative values (using ) improved the reproducibility within, and the comparability between, subjects, even when reducing the slice thickness. These preliminary findings may offer valuable insights toward standardizing ROI-QSM metrics across different patient cohorts and imaging settings in future clinical MRI studies.
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