Comparing single-shot EPI and 2D-navigated, multi-shot EPI diffusion tensor imaging acquisitions in the lumbar spinal cord at 3T
Bibliographic record
Abstract
Diffusion tensor imaging (DTI) can provide insights into spinal cord microstructure in health and disease; however, its application has been largely limited to cervical spinal segments using single-shot echo-planar imaging (EPI) diffusion-weighted MRI acquisitions. In this work, we evaluate a multi-shot EPI diffusion-weighted acquisition with reduced field-of-view (FOV) and 2D-navigated motion correction applied in the lumbar spinal cord of healthy volunteers, and compare image quality, geometric distortions, and quantitative DTI indices to those obtained with conventional, single-shot EPI diffusion-weighted MRI in a distinct, age/sex-matched healthy cohort. At 3 Tesla, 25 and 27 healthy participants were imaged using the single-shot and multi-shot EPI readouts with diffusion weighting, respectively, with matching resolution and comparable scan time. Seven participants underwent both diffusion acquisitions and were included in both cohorts. DTI indices were compared between the multi-shot and single-shot cohorts. Image signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) between gray and white matter, geometric distortions, and within-subject bias between the acquisitions were also assessed. The lumbar spinal cord diffusion indices derived from both cohorts were comparable to those in previous studies using single-shot EPI, though within-subject analysis demonstrated a systematic bias between the acquisitions in gray and white matter DTI measures, indicating these acquisitions are not interchangeable within a study. The multi-shot quantitative DTI maps demonstrated a significant reduction in image artifacts (i.e., distortions and blurring) and higher SNR and CNR compared to single-shot images. Overall, the reduced FOV, 2D-navigated, motion-corrected multi-shot acquisition demonstrated improved DTI quality metrics compared to single-shot, supporting its application for the lumbar spinal cord region.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".