Michael E Kim, Chenyu Gao, Leon Y Cai, Qi Yang, Nancy R Newlin, Karthik Ramadass, Angela Jefferson, Derek Archer, Niranjana Shashikumar, Kimberly R Pechman, Katherine A Gifford, Timothy J Hohman, Lori L Beason-Held, Susan M Resnick, Stefan Winzeck, Kurt G Schilling, Panpan Zhang, Daniel Moyer, Bennett A Landman
{"title":"利用扩散张量成像对 ComBat 的假设进行经验评估。","authors":"Michael E Kim, Chenyu Gao, Leon Y Cai, Qi Yang, Nancy R Newlin, Karthik Ramadass, Angela Jefferson, Derek Archer, Niranjana Shashikumar, Kimberly R Pechman, Katherine A Gifford, Timothy J Hohman, Lori L Beason-Held, Susan M Resnick, Stefan Winzeck, Kurt G Schilling, Panpan Zhang, Daniel Moyer, Bennett A Landman","doi":"10.1117/1.JMI.11.2.024011","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI.</p><p><strong>Approach: </strong>As a baseline, we match <math><mrow><mi>N</mi><mo>=</mo><mn>358</mn></mrow></math> participants from two sites to create a \"silver standard\" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) <math><mrow><msub><mi>β</mi><mi>AGE</mi></msub></mrow></math>, the linear regression coefficient of the relationship between FA and age; (ii) <math><mrow><msubsup><mrow><mover><mrow><mi>γ</mi></mrow><mrow><mo>^</mo></mrow></mover></mrow><mrow><mi>s</mi><mi>f</mi></mrow><mrow><mo>*</mo></mrow></msubsup></mrow></math>, the ComBat-estimated site-shift; and (iii) <math><mrow><msubsup><mrow><mover><mrow><mi>δ</mi></mrow><mrow><mo>^</mo></mrow></mover></mrow><mrow><mi>s</mi><mi>f</mi></mrow><mrow><mo>*</mo></mrow></msubsup></mrow></math>, the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions.</p><p><strong>Results: </strong>ComBat remains well behaved for <math><mrow><msub><mrow><mi>β</mi></mrow><mrow><mi>AGE</mi></mrow></msub></mrow></math> when <math><mrow><mi>N</mi><mo>></mo><mn>162</mn></mrow></math> and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable.</p><p><strong>Conclusion: </strong>Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 2","pages":"024011"},"PeriodicalIF":1.9000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11034156/pdf/","citationCount":"0","resultStr":"{\"title\":\"Empirical assessment of the assumptions of ComBat with diffusion tensor imaging.\",\"authors\":\"Michael E Kim, Chenyu Gao, Leon Y Cai, Qi Yang, Nancy R Newlin, Karthik Ramadass, Angela Jefferson, Derek Archer, Niranjana Shashikumar, Kimberly R Pechman, Katherine A Gifford, Timothy J Hohman, Lori L Beason-Held, Susan M Resnick, Stefan Winzeck, Kurt G Schilling, Panpan Zhang, Daniel Moyer, Bennett A Landman\",\"doi\":\"10.1117/1.JMI.11.2.024011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI.</p><p><strong>Approach: </strong>As a baseline, we match <math><mrow><mi>N</mi><mo>=</mo><mn>358</mn></mrow></math> participants from two sites to create a \\\"silver standard\\\" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) <math><mrow><msub><mi>β</mi><mi>AGE</mi></msub></mrow></math>, the linear regression coefficient of the relationship between FA and age; (ii) <math><mrow><msubsup><mrow><mover><mrow><mi>γ</mi></mrow><mrow><mo>^</mo></mrow></mover></mrow><mrow><mi>s</mi><mi>f</mi></mrow><mrow><mo>*</mo></mrow></msubsup></mrow></math>, the ComBat-estimated site-shift; and (iii) <math><mrow><msubsup><mrow><mover><mrow><mi>δ</mi></mrow><mrow><mo>^</mo></mrow></mover></mrow><mrow><mi>s</mi><mi>f</mi></mrow><mrow><mo>*</mo></mrow></msubsup></mrow></math>, the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions.</p><p><strong>Results: </strong>ComBat remains well behaved for <math><mrow><msub><mrow><mi>β</mi></mrow><mrow><mi>AGE</mi></mrow></msub></mrow></math> when <math><mrow><mi>N</mi><mo>></mo><mn>162</mn></mrow></math> and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable.</p><p><strong>Conclusion: </strong>Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.</p>\",\"PeriodicalId\":47707,\"journal\":{\"name\":\"Journal of Medical Imaging\",\"volume\":\"11 2\",\"pages\":\"024011\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11034156/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1117/1.JMI.11.2.024011\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/4/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1117/1.JMI.11.2.024011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/4/17 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Empirical assessment of the assumptions of ComBat with diffusion tensor imaging.
Purpose: Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI.
Approach: As a baseline, we match participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) , the linear regression coefficient of the relationship between FA and age; (ii) , the ComBat-estimated site-shift; and (iii) , the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions.
Results: ComBat remains well behaved for when and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable.
Conclusion: Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.
期刊介绍:
JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.