Construction of Optimal Velocity Encoding for Cerebral Blood Flow Volume Measurement with Phase-Contrast MRA
Bibliographic record
Abstract
Accurate velocity encoding is crucial for quantification of arterial inflow and venous outflow in intracranial diseases. The purpose of this study was to optimize the velocity encoding of phase-contrast (PC) MRA and quantify cerebral blood flow in normal volunteers. Ten healthy volunteers were examined on a GE 1.5 T MR system with 2D PCMRA sequence. The parameters of the sequence were as follows: TR 40 ms, TE 6.6 ms, flip angle 20deg, slice thickness 4 mm, matrix 256times256, field of view 140 mm. In each cardiac cycle, 40 images were obtained. Velocity encoding was set from 30 to 90 cm/sec at 10 cm/sec interval for total of 7 scans per volunteer. The scan level was chosen at C2 perpendicular to the vessels of interest. Data were analyzed using CV glow software on a GE advantage windows workstation (4.0). Arterial inflow, venous outflow, peak velocity, and mean velocity were obtained for bilateral internal carotid artery (ICA), vertebral artery (VA), and jugular vein (JV). Significant differences were observed in arterial inflow of bilateral ICA, peak velocity and mean velocity of right ICA when velocity encoding (Venc) was 30 cm/sec. compared with other Venc (P0.05). For accurate cerebral blood flow measurement using PCMRA technique and consideration of biophysiological variation, the reasonable velocity encoding was 60 cm/sec for ICA, VA and JV. The mean artery inflow of ICA and VA was 655plusmn118 ml/min and mean venous outflow of JV (C2-C3 level) was 506plusmn186 ml/min. The ratio of outflow to inflow for cerebral blood was 0.826. Phase-contrast MRA can be used to assess the relationship between major cerebral vessel inflow to outflow
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".