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Brain Growth Charts for Quantitative Analysis of Pediatric Clinical Brain MRI Scans with Limited Imaging Pathology
Jenna M Schabdach1, J Eric Schmitt1, Susan Sotardi1
1From the Lifespan Brain Institute (LiBI) of the Children's Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S., A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.), Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and Health Informatics (J.E.S., S.S., V.P.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G., T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A., T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa; Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology (R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B., R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania, Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of Radiology (J.E.I.) and Department of Otolaryngology-Head and Neck Surgery (S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; and McGovern Institute for Brain Research (S.G.) and Computer Science & Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of Technology, Cambridge, Mass.
Insights
Clinically acquired brain MRI scans, when curated, can reliably supplement research data for neurodevelopmental studies. These brain growth charts closely mirror those from research-quality scans, overcoming data limitations.
Area of Science:
- Neuroimaging and Developmental Neuroscience
- Medical Image Analysis and Data Curation
Background:
- Clinical brain MRI scans are underutilized for neurodevelopment research due to technical variability and lack of controls.
- Retrospective studies using clinical MRI scans are limited compared to costly, prospectively acquired research scans.
- Heterogeneity in clinical MRI data hinders robust neurodevelopmental investigations.
Purpose of the Study:
- To establish a benchmark for neuroanatomic variability in clinically acquired brain MRI scans with limited imaging pathology (SLIPs).
- To assess if growth charts from curated clinical MRI scans differ from research-quality scans.
- To determine if clinical indication for scanning influences brain growth chart outcomes.
Main Methods:
- Secondary analysis of 532 pediatric clinical brain MRI scans (≤22 years) across nine 3.0-T scanners.
- Curation involved manual review of radiology reports and image quality assessment to exclude gross pathology.
- Global and regional brain volumes were measured; clinical growth charts were compared to 8346 research controls using Pearson correlation.
Main Results:
- Curated clinical brain growth charts showed high correlation (median r = 0.979) with research-derived charts for normative developmental trajectories.
- No significant bias was found in clinical brain charts based on the clinical indication for the MRI scans.
- The study included a diverse dataset of pediatric patients scanned between 2005 and 2020.
Conclusions:
- Curated clinical MRI scans with limited imaging pathology provide a reliable benchmark for neuroanatomic variability.
- Clinical brain growth charts are comparable to those from research-quality data, suggesting their utility in research.
- This approach can significantly supplement existing research datasets for neurodevelopmental studies.
Abstract:
Background Clinically acquired brain MRI scans represent a valuable but underused resource for investigating neurodevelopment due to their technical heterogeneity and lack of appropriate controls. These barriers have curtailed retrospective studies of clinical brain MRI scans compared with more costly prospectively acquired research-quality brain MRI scans. Purpose To provide a benchmark for neuroanatomic variability in clinically acquired brain MRI scans with limited imaging pathology (SLIPs) and to evaluate if growth charts from curated clinical MRI scans differed from research-quality MRI scans or were influenced by clinical indication for the scan. Materials and Methods In this secondary analysis of preexisting data, clinical brain MRI SLIPs from an urban pediatric health care system (individuals aged ≤22 years) were scanned across nine 3.0-T MRI scanners. The curation process included manual review of signed radiology reports and automated and manual quality review of images without gross pathology. Global and regional volumetric imaging phenotypes were measured using two image segmentation pipelines, and clinical brain growth charts were quantitatively compared with charts derived from a large set of research controls in the same age range by means of Pearson correlation and age at peak volume. Results The curated clinical data set included 532 patients (277 male; median age, 10 years [IQR, 5-14 years]; age range, 28 days after birth to 22 years) scanned between 2005 and 2020. Clinical brain growth charts were highly correlated with growth charts derived from research data sets (22 studies, 8346 individuals [4947 male]; age range, 152 days after birth to 22 years) in terms of normative developmental trajectories predicted by the models (median r = 0.979). Conclusion The clinical indication of the scans did not significantly bias the output of clinical brain charts. Brain growth charts derived from clinical controls with limited imaging pathology were highly correlated with brain charts from research controls, suggesting the potential of curated clinical MRI scans to supplement research data sets. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Ertl-Wagner and Pai in this issue.
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