Non-invasive Spinal Cord Angiography for Imaging Vascular Spinal Cord Malformations
Bibliographic record
Abstract
On conventional (non-contrast-enhanced) MR imaging signs of increased signal intensity in the central cord and enlarged subarachnoid flow voids are suggestive for the presence of a vascular spinal cord malformation. However, they provide no predictive information on the exact location of the malformation and thus additional imaging is warranted. At present, catheter angiography is still the standard of reference to image the arteries and veins of the spinal cord 1 and the preferred technique for diagnosing, localizing, and classifying vascular spinal lesions . Although it provides superior spatial resolution and image quality, catheter angiography has, however, several major drawbacks, as it is invasive, involves exposure to ionizing radiation, and has a small risk for major complications, including spinal cord infarction . In addition, it can often be time consuming and may require multiple catheterizations to locate the vascular spinal cord malformation. For these reasons, new imaging methods were searched and developed in order to non-invasively visualize the aberrant spinal cord vasculature. Recent advances in MR and CT angiography have strongly improved the vessel-to-background contrast by using fast acquisition in combination with contrast agent bolus injection and are now able to depict and differentiate normal from abnormal spinal cord vasculature. In this paper the relevant vascular radiological anatomy of the spinal cord is first briefly outlined. Subsequently, previously used and new spinal cord MR angiography techniques as well as CT angiography techniques will be discussed. Then the MR and CT angiography techniques will be compared for spinal cord angiography. To conclude an outlook is provided on possible future developments and applications of non-invasive spinal cord angiography.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".