Attenuation of Lower-Thoracic, Lumbar, and Sacral Spinal Cord Motion: Implications for Imaging Human Spinal Cord Structure and Function
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Recent literature indicates that cervical and upper-thoracic spinal cord motion adversely affect both structural and functional MR imaging (fMRI; particularly diffusion tensor imaging [DTI] and spinal fMRI), ultimately reducing the reliability of these methods for both research and clinical applications. In the present study, we investigated motion of the lower-thoracic, lumbar, and sacral cord segments to evaluate the incidence of similar motion-related confounds in these regions. MATERIALS AND METHODS: Recently developed methods, used previously for measuring cervical and upper-thoracic spinal cord motion, were employed in the present study to examine anteroposterior (A/P) and left-right (L/R) spinal cord motion in caudal regions. Segmented cinematic imaging was applied with a gradient-echo, turbo fast low-angle shot (turbo-FLASH) pulse sequence to acquire midline images of the cord at 24 cardiac phases throughout the lower-thoracic, lumbar, and sacral spinal cord regions. RESULTS: The magnitude of A/P motion was found to be largest in rostral cord regions, whereas in caudal regions (at the level of the T4/T5 vertebrae and below), peak cord motion was uniformly small (routinely < or =0.10 mm). L/R motion, however, was found to be minimal throughout the thoracic, lumbar, and sacral regions. CONCLUSION: Motion-related errors in spinal fMRI and DTI are expected to be significantly reduced throughout caudal regions of the spinal cord, thus yielding higher sensitivity and specificity compared with rostral regions. The paucity of such errors is expected to provide a means of observing the specific impact of motion (in rostral regions) and to enable the acquisition of uncorrupted DTI and fMRI data for studies of structure and function throughout lumbar and sacral regions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".