Real‐time Fourier velocity encoding: An in vivo evaluation
Bibliographic record
Abstract
PURPOSE: To compare in vivo real-time Fourier velocity encoding (FVE), spectral-Doppler ultrasound, and phase-contrast (PC) magnetic-resonance (MR) imaging. MATERIALS AND METHODS: In vivo velocity spectra were measured in the suprarenal and infrarenal aorta and the hepatic segment of the inferior vena cava of eight normal volunteers using FVE, and compared to similar measurements using Doppler ultrasound and gated PC MR imaging. In vivo waveforms were compared qualitatively according to flow pattern appearance (number, shape, and position of velocity peaks) and quantitatively according to peak velocity. RESULTS: Good agreement was obtained between peak velocities measured in vitro using FVE and PC MR imaging (R(2) = 0.99, P = 2.10(-6), slope = 0.97 +/- 0.05). Qualitatively, the FVE and ultrasound measurements agreed closely in the majority of in vivo cases (excellent or good in 21/24 cases) while the PC MR method resolved fewer velocity peaks due to the inherent temporal averaging of cardiac-gated studies (excellent or good agreement with FVE in 13/24 cases). Quantitatively, the FVE measurement of peak velocity correlated strongly with both ultrasound (R(2) = 0.71, P = 2.10(-7), slope = 0.81 +/- 0.08) and PC MR (R(2) = 0.85, P = 2.10(-10), slope = 1.04 +/- 0.08). CONCLUSION: Real-time MR assessment of blood-flow velocity correlated well with spectral Doppler ultrasound. Such new methods may allow hemodynamic information to be acquired in vessels inaccessible to ultrasound or in patients for whom respiratory compensation is not possible.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".