Keeping It Consistent: Assessing Measurement Repeatability of a Novel Anisotropic Phantom for Higher Order Diffusion Tensor MRI Sequences
Bibliographic record
Abstract
Diffusion MRI (dMRI) provides valuable insight into tissue microstructure for clinical and research applications. Traditional diffusion tensor imaging (DTI) models diffusion as a Gaussian process, which limits its accuracy in regions with complex fibre configurations (such as crossing or bifurcating tracts). Higher-order models, including diffusion kurtosis imaging (DKI) and constrained spherical deconvolution (CSD), address these limitations by capturing non-Gaussian diffusion behaviour or resolving multiple fibre orientations within a voxel. Despite their theoretical advantages, these models are more sensitive to noise and acquisition variability, raising concerns about repeatability. Currently, there is no standardized method to perform quality assurance (QA) on dMRI data, and there are limited studies measuring the repeatability of higher-order tensor metrics. This thesis evaluates the repeatability of higher-order dMRI metrics using a novel anisotropic phantom developed by PreOperative Performance (Toronto, ON). The phantom contains fibre modules with controlled geometries (linear, branching, and crossing bundles) designed to mimic white matter tract architecture. Six diverse regions of interest (ROIs) were assessed across 11 independent imaging sessions using DTI, high angular resolution diffusion imaging (HARDI), and DKI protocols. Repeatability was quantified using coefficient of variation (CoV) and intraclass correlation coefficient (ICC). DTI-derived metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), exhibited excellent repeatability (CoV<10%, ICC>0.9). HARDI acquisitions (60- and 90-direction) yielded slightly improved repeatability over DTI. DKI metrics, including mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), and kurtosis fractional anisotropy (KFA), showed greater variability, particularly in ROIs with fibre crossings. Generalized fractional anisotropy (GFA), derived from CSD, demonstrated increasing ICCs with higher angular resolution: 0.66 (DTI), 0.80 (HARDI-60), and 0.85 (HARDI-90). These findings support the phantom’s utility for repeatability testing and contribute toward developing QA standards for advanced dMRI models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".