Chang Y Ho, Scott Persohn, Meghana Sankar, Paul R Territo
{"title":"利用 T1 弛豫测量法绘制白质髓鞘生长图","authors":"Chang Y Ho, Scott Persohn, Meghana Sankar, Paul R Territo","doi":"10.3174/ajnr.A8306","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and purpose: </strong>Myelin maturation occurs in late fetal life to early adulthood, with the most rapid changes observed in the first few years of infancy. To quantify the degree of myelination, a specific MR imaging sequence is required to measure the changes in tissue proton relaxivity (R1). R1 positively correlates with the degree of myelination maturation at a given age. Similar to head circumference charts, these data can be used to develop normal growth charts for specific white matter tracts to detect pathologies involving abnormal myelination.</p><p><strong>Materials and methods: </strong>This is a cross-sectional study using normal clinical pediatric brain MR images with the MP2RAGE sequence to generate T1 maps. The T1 maps were segmented to 75 brain regions from a brain atlas (white matter and gyri). Statistical modeling for all subjects across regions and the age range was computed, and estimates of population-level percentile ranking were computed to describe the effective myelination rate as a function of age. Test-retest analysis was performed to assess reproducibility. Logistic trendline and regression were performed for selected white matter regions and plotted for growth charts.</p><p><strong>Results: </strong>After exclusion for abnormal MR imaging or diseases affecting myelination from the electronic medical record, 103 subject MR images were included, ranging from birth to 17 years of age. Test-retest analysis resulted in a high correlation for white matter (<i>r</i> = 0.88) and gyri (<i>r</i> = 0.95). All white matter regions from the atlas had significant <i>P</i> values for logistic regression with <i>R</i> <sup>2</sup> values ranging from 0.41 to 0.99.</p><p><strong>Conclusions: </strong>These data can serve as a myelination growth chart to permit patient comparisons with normal levels with respect to age and brain regions, thus improving detection of developmental disorders affecting myelin.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11392380/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development of Myelin Growth Charts of the White Matter Using T1 Relaxometry.\",\"authors\":\"Chang Y Ho, Scott Persohn, Meghana Sankar, Paul R Territo\",\"doi\":\"10.3174/ajnr.A8306\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background and purpose: </strong>Myelin maturation occurs in late fetal life to early adulthood, with the most rapid changes observed in the first few years of infancy. To quantify the degree of myelination, a specific MR imaging sequence is required to measure the changes in tissue proton relaxivity (R1). R1 positively correlates with the degree of myelination maturation at a given age. Similar to head circumference charts, these data can be used to develop normal growth charts for specific white matter tracts to detect pathologies involving abnormal myelination.</p><p><strong>Materials and methods: </strong>This is a cross-sectional study using normal clinical pediatric brain MR images with the MP2RAGE sequence to generate T1 maps. The T1 maps were segmented to 75 brain regions from a brain atlas (white matter and gyri). Statistical modeling for all subjects across regions and the age range was computed, and estimates of population-level percentile ranking were computed to describe the effective myelination rate as a function of age. Test-retest analysis was performed to assess reproducibility. Logistic trendline and regression were performed for selected white matter regions and plotted for growth charts.</p><p><strong>Results: </strong>After exclusion for abnormal MR imaging or diseases affecting myelination from the electronic medical record, 103 subject MR images were included, ranging from birth to 17 years of age. Test-retest analysis resulted in a high correlation for white matter (<i>r</i> = 0.88) and gyri (<i>r</i> = 0.95). All white matter regions from the atlas had significant <i>P</i> values for logistic regression with <i>R</i> <sup>2</sup> values ranging from 0.41 to 0.99.</p><p><strong>Conclusions: </strong>These data can serve as a myelination growth chart to permit patient comparisons with normal levels with respect to age and brain regions, thus improving detection of developmental disorders affecting myelin.</p>\",\"PeriodicalId\":93863,\"journal\":{\"name\":\"AJNR. 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American journal of neuroradiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3174/ajnr.A8306","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of Myelin Growth Charts of the White Matter Using T1 Relaxometry.
Background and purpose: Myelin maturation occurs in late fetal life to early adulthood, with the most rapid changes observed in the first few years of infancy. To quantify the degree of myelination, a specific MR imaging sequence is required to measure the changes in tissue proton relaxivity (R1). R1 positively correlates with the degree of myelination maturation at a given age. Similar to head circumference charts, these data can be used to develop normal growth charts for specific white matter tracts to detect pathologies involving abnormal myelination.
Materials and methods: This is a cross-sectional study using normal clinical pediatric brain MR images with the MP2RAGE sequence to generate T1 maps. The T1 maps were segmented to 75 brain regions from a brain atlas (white matter and gyri). Statistical modeling for all subjects across regions and the age range was computed, and estimates of population-level percentile ranking were computed to describe the effective myelination rate as a function of age. Test-retest analysis was performed to assess reproducibility. Logistic trendline and regression were performed for selected white matter regions and plotted for growth charts.
Results: After exclusion for abnormal MR imaging or diseases affecting myelination from the electronic medical record, 103 subject MR images were included, ranging from birth to 17 years of age. Test-retest analysis resulted in a high correlation for white matter (r = 0.88) and gyri (r = 0.95). All white matter regions from the atlas had significant P values for logistic regression with R2 values ranging from 0.41 to 0.99.
Conclusions: These data can serve as a myelination growth chart to permit patient comparisons with normal levels with respect to age and brain regions, thus improving detection of developmental disorders affecting myelin.