Differentiating CSF flow artifacts from pathology: an educational review
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) of the neuroaxis is prone to a variety of artifacts. Familiarity with these artifacts and their respective mitigation techniques is essential for accurate neuroradiological interpretation. In this educational review, we focus on artifacts caused by the physiological flow of cerebrospinal fluid (CSF), which are encountered commonly and, depending on the context, may be beneficial or detrimental in diagnostic decision-making. The pictorial examples provided will illustrate key cases with their practical implications. CRITICAL RELEVANCE STATEMENT: This paper highlights common CSF flow artifacts, including phase encoding artifacts, time-of-flight signal loss, entry slice phenomenon, and intravoxel dephasing, emphasizing their impact on diagnosis interpretation and mitigation strategies. KEY POINTS: CSF artifacts stem from flow dynamics, phase differences, or magnetic field interactions. Artifacts obscure or mimic pathologies, degrade image quality, or occasionally aid in diagnostic decision-making. Mitigation strategies are simple and intuitive, including modification of phase directions, employing alternate imaging sequences, and altering MRI parameters.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".