Protocol for magnetic resonance imaging acquisition, quality assurance, and quality check for the Accelerator program for Discovery in Brain disorders using Stem cells

Abstract

Objective: The Accelerator program for Discovery in Brain disorders using Stem cells (ADBS) is a longitudinal study on five cohorts of patients with major psychiatric disorders from genetically high-risk families, their unaffected first-degree relatives, and healthy subjects. We describe the ADBS protocols for acquisition, quality assurance (QA), and quality check (QC) for multimodal magnetic resonance brain imaging studies.

Methods: We describe the acquisition and QC protocols for structural, functional, and diffusion images. For QA, we acquire proton density and functional images on phantoms, along with repeated scans on human volunteer. We describe the analysis of phantom data and test-retest reliability of volumetric and diffusion measures.

Results: Analysis of acquired phantom data shows linearity of proton density signal with increasing proton fraction, and an overall stability of various spatial and temporal QA measures. Examination of dice coefficient and statistical analyses of coefficient of variation in test-retest data on the human volunteer showed consistency of volumetric and diffusivity measures at whole-brain, regional, and voxel-level.

Conclusion: The described acquisition and QA-QC procedures can yield consistent and reliable quantitative measures. It is expected that this longitudinal neuroimaging dataset will, upon its release, serve the scientific community well and pave the way for interesting discoveries.

Keywords: ADBS; longitudinal study; magnetic resonance imaging; quality assurance; quality check.

FIGURE 1

Analysis of proton density images acquired on the HPD phantom; (a) fiducials F01, F02, and F03 are first detected. These locations are then used for labeling the other circles using heuristics (see Supporting Information Material). The boundaries of the circles are eroded twice to ensure that only the signal within the circle is used for any calculation; (b) mean signal from each circle is plotted against the proton fraction in these spheres. Since the signal is supposed to linearly increase with increasing proton fraction, we fit a linear regression model and calculate the R ² value. A poorer fit of this linear model would indicate a problem in the acquisition

FIGURE 2

Variation of EPI quality measures on agar gel phantom (see Table S5 for a breakup of number of scans per month and Table S6 for scan schedule); the average value within each measurement is shown by a dashed orange line while mean ± twice the standard deviation of the measurement is shown by a dashed purple line; outlier values are measurements beyond this range and are marked with orange colored circles; note that the lower mean ± twice the standard deviation limit is not shown for PSG (volume) and drift as there were no values beyond this range. PIU, percent integral uniformity; PSC, percent signal change; PSG, percent signal ghosting; SFNR, signal‐to‐fluctuation‐noise‐ratio; SNR, signal‐to‐noise ratio

FIGURE 3

Percentage R ² value from proton density images acquired on HPD phantom between April 2018 and December 2019 (see Table S6 for a breakup of number of scans per month and Table S7 for scan schedule); the average percentage R ² is shown by a dashed orange line while mean ± twice the standard deviation of the percentage R ² is shown by a dashed purple line; outlier values beyond this range and are marked with orange‐colored circles; note that the upper mean ± twice the standard deviation limit is not shown. HPD, high precision devices

FIGURE 4

Absolute percentage difference in coefficient of variation (CV) of GM volumes between acquisition Series 1 (S1) and Series 2 (S2), for regions of interest from Hammers Atlas for test retest reliability data; (a) regions from the left hemisphere, and (b) regions from the right hemisphere; see Table S11 for full names of the brain regions. GM, gray matter

FIGURE 5

Region‐wise counts of the number of voxels which had significantly different coefficient of variation of GM between acquisition Series 1 and Series 2 at $\alpha <0.001$ (uncorrected); see Table S11 for full names of the brain regions. GM, gray matter

FIGURE 6

Pairwise Dice coefficient for gray matter (green color), white matter (purple line), and cerebrospinal fluid (orange color); between July and December 2019, a volunteer underwent three scans per month (total 18 scans); Dice coefficient between the segmentation of the three tissue classes was calculated for all pairs of images (total 153 pairs) to examine the consistency of segmentation (see Table S9 for details of reliability schedule)

FIGURE 7

Absolute percentage difference in coefficient of variation (CV) of (a) axial diffusivity (AD), (b) mean diffusivity (MD), (c) radial diffusivity (RD), and (d) fractional anisotropy (FA) between acquisition Series 1 (S1) and Series 2 (S2), for regions of interest from JHU white matter labels Atlas for test‐retest reliability data; see Table S12 for full names of the brain regions; unlabeled ticks are right hemisphere regions

FIGURE 8

Region‐wise counts of the number of voxels which had significantly different coefficient of variation of (a) axial diffusivity (AD), (b) mean diffusivity (MD), (c) radial diffusivity (RD), and (d) fractional anisotropy (FA) between acquisition Series 1 and Series 2 at $\alpha <0.001$ (uncorrected); see Table S12 for full names of the brain regions

FIGURE 9

Summary of the quality assurance and quality check pipeline being followed in the Accelerator program for Discovery in Brain disorders using Stem cells project