Steady‐state first‐pass perfusion (SSFPP): A new approach to 3D first‐pass myocardial perfusion imaging
Bibliographic record
Abstract
PURPOSE: To describe and characterize a new approach to first-pass myocardial perfusion utilizing balanced steady-state free precession acquisition without the use of saturation recovery or other magnetization preparation. THEORY: The balanced steady-state free precession sequence is inherently sensitive to contrast agent enhancement of the myocardium. This sensitivity can be used to advantage in first-pass myocardial perfusion imaging by eliminating the need for magnetization preparation. METHODS: Bloch equation simulations, phantom experiments, and in vivo 2D imaging studies were run comparing the proposed technique with three other methods: saturation recovery spoiled gradient echo, saturation recovery steady-state free precession, and steady-state spoiled gradient echo without magnetization preparation. Additionally, an acquisition-reconstruction strategy for 3D perfusion imaging is proposed and initial experience with this approach is demonstrated in healthy subjects and one patient. RESULTS: Phantom experiments verified simulation results showing the sensitivity of the balanced steady-state free precession sequence to contrast agent enhancement in solid tissue is similar to that of magnetization-prepared acquisitions. Images acquired in normal volunteers showed the proposed technique provided superior signal and signal-to-noise ratio compared with all other sequences at baseline as well as postcontrast. CONCLUSIONS: A new approach to first-pass myocardial perfusion is presented that obviates the need for magnetization preparation and provides high signal-to-noise ratio.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".