Reliability and Spatial Consistency of MR Diffusion Tensor Imaging Measures Along the Cerebral Perivascular Space
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) has emerged as a promising noninvasive method for evaluating water motion that may reflect glymphatic system function. However, the reliability of DTI-ALPS measurements across different region-of-interest (ROI) selection methods remains underinvestigated. This study aimed to assess the interrater reliability among three neuroradiologists in native space and compare DTI-ALPS indices derived from ROIs placed in subjects' native space versus standardized Montreal Neurological Institute (MNI) space. METHODS: DTI-ALPS indices from 16 healthy subjects were calculated from both left and right hemispheres using two ROI placement approaches: (1) native space ROIs manually placed by three neuroradiologists, and (2) standardized ROIs in MNI space based on the fractional anisotropy template. Interrater reliability was assessed using intraclass correlation coefficients (ICCs). The proportion of ROI overlaps among the three neuroradiologists was also evaluated. Differences between native and MNI space measurements were evaluated using related-samples Friedman's analysis with post hoc pairwise comparisons. RESULTS: Interrater reliability for native space ROI placement was moderate for left-sided DTI-ALPS indices (ICC = 0.599) and good for right-sided DTI-ALPS indices (ICC = 0.807). Spatial overlap analysis revealed poor Dice similarity coefficients across all ROI types (range: 0.047-0.312), with right association ROIs showing higher spatial consistency. Significant differences were found between native and MNI space measurements for left-sided DTI-ALPS indices (p = 0.002) but not for right-sided DTI-ALPS indices (p = 0.913). CONCLUSION: These findings highlight the importance of standardized ROI selection approaches for clinical applications of DTI-ALPS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".