SU‐E‐I‐128: Evaluation of Magnetic Resonance Phase Data of Projection and 2‐D FLASH Acquisition to Estimate the AIF
Bibliographic record
Abstract
Purpose: Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE‐ MRI) is a technique that non‐invasively collects data related to the vasculature of tissue. Quantitative information is derived from the time course concentration of contrast agent in a supplying vessel, known as the arterial input function (AIF). The AIF should have a high temporal resolution, but it often compromises spatial resolution. The phase of MRI signal varies linearly with the concentration of a contrast agent, is independent of the hematocrit and is expected to have an increased signal to noise ratio (SNR) when compared to magnitude based data. The use of phase data from projection images satisfies the requirement for high temporal resolution and may be used to reconstruct a 2D image if projected along different axes. Methods: Data was collected using a 7.0 T Bruker MRI system. 1‐D Projection and 2‐D FLASH images were collected on a phantom for varying concentrations of Gadolinium. Phase information was determined directly from the free induction decay (FID) and unwrapped where appropriate. Results: Calibration of the phase data (2‐D FLASH acquisition) verified that the phase increased linearly with the concentration of contrast agent with a slope of (1.183 ± 0.015) rad/mM. A small phase drift was present ((0.014± 0.019) rad/100 min), but had neglible impact on the calibration curve. The concentration of contrast agent as determined from the projection data was in good agreement with the 2D analysis with a slope of (1.187 ± 0.027) rad/mM. Conclusions: Projection based imaging will significantly increase the temporal resolution of DCE‐MRI studies. The linear relationship between the phase and concentration of contrast agent was consistent between the 2‐D FLASH and projection acquisitions. The results of this study suggest that projection based imaging may be used to accurately estimate the AIF.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".