Brain Growth Charts for Quantitative Analysis of Pediatric Clinical Brain MRI Scans with Limited Imaging Pathology

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.

Graphical abstract

Figure 1:

(A) Flowchart shows overview of the data curation and processing pipeline. The initial request for sessions whose signed radiology reports contained no gross pathology was submitted to the honest broker. The honest broker returned a set of anonymized MRI scans, which were then filtered to identify only high-spatial-resolution T1-weighted scans from 3.0-T scanners. Next, the high-resolution scans were manually reviewed by independent raters to remove visually low-quality images. This finalized set of curated clinical brain MRI scans with limited reported imaging pathology was processed using two neuroimaging processing pipelines (FreeSurfer or Infant FreeSurfer 6.0.0 and SynthSeg+) to produce two sets of imaging phenotypes for high-quality clinically acquired scans. MPR = magnetization-prepared rapid acquisition gradient echo. (B) Bar graphs show the distribution of age at scan (left) and the distribution of scans obtained across all scanners (right) labeled by sex. ID = identification number.

Figure 2:

Scatterplots (top row) and violin plots show the distribution of phenotype centiles calculated using generalized additive models for location, scale, and shape, or GAMLSS, for the global FreeSurfer (FS) imaging phenotypes. There was no evidence of a statistical difference in the centile distributions for each phenotype based on the clinical indication for the scan (developmental disorder [DD], clinical eye or vision finding [EV], headache [H], suspected seizure [SS], and other [O]). For all five scan reason categories, there was no evidence of differences between phenotype distributions detected (gray matter volume [GMV]: P = .502, F statistic = 0.837; white matter volume [WMV]: P = .877, F statistic = 0.301; subcortical gray matter volume [sGMV]: P = .674, F statistic = 0.585; ventricular cerebrospinal fluid volume [CSF]: P = .555, F statistic = 0.755; and total cerebrum volume [TCV]: P = .679, F statistic = 0.577).

Figure 3:

Centiles of global phenotypes by MRI scanner before and after harmonization with ComBat across scanners. Boxplots of centiles from each scanner are represented individually, and the scanners are presented left to right in decreasing order of the number of scans. The left column shows the centiles of FreeSurfer imaging phenotypes before ComBat harmonization (pre-ComBat), while the right column shows the centiles of FreeSurfer after ComBat harmonization (post-ComBat). The effect of scanner was tested with analysis of variance, showing no significant difference after harmonization (gray matter volume [GMV]: P = .146, F statistic = 1.53; white matter volume [WMV]: P = .603, F statistic = 0.8; subcortical gray matter volume [sGMV]: P = .743, F statistic = 0.641; ventricular cerebrospinal fluid volume [CSF]: P = .926, F statistic = 0.389; and total cerebrum volume [TCV]: P = .356, F statistic = 1.11). In each boxplot, the horizontal line in the box represents the median, the two hinges represent the 25th and 75th centiles, and the whiskers extend out from the hinges to the furtherst values from the median up to 1.5 times the IQR.

Figure 4:

Growth trajectories of the five global imaging phenotypes compared across data sets and image processing pipelines. The clinical brain MRI scans with limited imaging pathology were processed with FreeSurfer and SynthSeg+ and compared with the models generated from the Lifespan Brain Chart Consortium data with the same age range as the clinical data set. (A, B) Scatterplots show the clinical brain MRI global imaging phenotypes, with the 50th centile line estimated with generalized additive models for location, scale, and shape, or GAMLSS, for the clinical brain growth charts of that phenotype (solid line) and the 50th centile line estimated from the research brain growth charts (dashed line) for the FreeSurfer 6.0.0 and SynthSeg+ processing pipelines. (C) Scatterplots show direct comparison of the clinical imaging phenotypes derived from FreeSurfer and SynthSeg+. The Pearson correlation coefficient, intraclass correlation coefficient (ICC), and 95% CIs for each comparison can be seen in Table 3. CSF = ventricular cerebrospinal fluid volume, GMV = gray matter volume, sGMV = subcortical gray matter volume, TCV = total cerebrum volume, WMV = white matter volume.

Figure 5:

Comparison of regional brain development modeled on scans with limited imaging pathology (SLIPs) and Lifespan Brain Chart Consortium (LBCC) scans. Desikan-Killiany atlases for (A) SLIPs and (B) LBCC scans show the age at which the volume of each cortical region peaked with values averaged across left and right hemispheres. The correspondence of the pair of age at peak volume maps was statistically significant according to spatially informed null models (see Materials and Methods section; spin-test P < .001). (C) Scatterplot shows the age at peak regional volume in clinical and research controls (Pearson r = 0.726 [95% CI: 0.496, 0.861]; intraclass correlation coefficient [ICC] = 0.841). The two lines in C are for the line y = x and the Deming regression (slope = 0.938 [95% CI: 0.533, 1.34]; intercept = 0.220 [95% CI: −1.17, 1.60]). In the scatterplot, the size of each point is proportional to the average size of the brain region it represents. bankssts = banks of superior temporal sulcus.

Figure 6:

(A) Scatterplots show growth trajectories of the five global imaging phenotypes as produced by SynthSeg+ overlaid with out-of-sample data processed by SynthSeg+. Each growth trajectory chart shows how the out-of-sample clinical data fit on the growth charts generated using the primary clinical data. The curves on each chart are the 5th, 10th, 25th, 50th, 75th, 90th, and 95th centiles, respectively. (B) Violin plots show the comparison between the centiles of the primary data and the out-of-sample data as estimated using generalized additive models for location, scale, and shape trained on the primary data. The Kolmogorov-Smirnov test comparing the distributions of centiles from the primary data and the out-of-sample data showed no statistically significant differences between the distributions of centiles for all phenotypes after Bonferroni correction (five classes of phenotypes; P = .430, .338, .854, .118, and .515 for gray matter volume [GMV], white matter volume [WMV], subcortical gray matter volume [sGMV], ventricular cerebrospinal fluid volume [CSF], and total cerebrum volume [TCV], respectively). ns = not significant.