Performance of steady‐state free precession for imaging carotid artery disease
Bibliographic record
Abstract
PURPOSE: To evaluate steady-state free precession (SSFP) for diagnosing carotid artery disease. MATERIALS AND METHODS: Following bilateral x-ray angiography, seven patients with suspected carotid artery disease were imaged with SSFP, black blood fast spin echo (BB FSE), and time-of-flight MR angiography (TOF MRA). The techniques were compared for characterizing the vessel lumen. Flow phantom experiments were also performed, using speeds of 0 to 40 cm/second, to further evaluate the merits of each MR technique. RESULTS: In the patient studies, of the 14 arteries available, a correct grading of stenosis was possible with SSFP in 9 of 14, FSE in 12 of 14, and TOF in 13 of 14, assuming x-ray angiography as the gold standard. The SSFP technique was the least reliable and had severe artifacts in 5 of 14 arteries, making these images nondiagnostic. The flow phantom demonstrated that although the SSFP technique performs well under slow or no flow, it breaks down at higher flow levels. CONCLUSION: The continuous SSFP sequence used here was not reliable for imaging carotid artery disease owing to artifact in many cases. Nevertheless, the high speed of this SSFP technique does allow it to serve as a rapid scouting method prior to a more detailed evaluation with other MRI methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".