SU‐G‐IeP1‐03: Comparing Arterial Input Function Measurements in DCE‐MRI Using MOLLI and Phase
Bibliographic record
Abstract
Purpose: To compare two different methods for measuring the arterial input function (AIF) in dynamic contrast‐enhanced (DCE)‐MRI. Methods: Five DCE‐MRI experiments were performed on an aqueous DCE‐MRI phantom (Shelley Medical) in a Siemens 3T Trio MRI scanner with 32 channel head coil and clinical DCE‐MRI protocol. MRI signal phase (φ) data were saved for offline processing. T1 relaxation measurements, using a Modified Look‐ Locker Inversion Recovery (MOLLI) pulse sequence, were performed preand post‐DCE. The input flow rate was set to 200 mL/min to mimic blood flow in the human body. The AIF of Gd injection was determined with two different methods. 1. “Phase‐only”: Pre‐injection baseline phase (φ0) was subtracted from phase‐vs‐time [φ(t)]. This quantity, φ(t) – φ0, was then used to compute the AIF. 2. “Phase+MOLLI”: The AIF value during the post‐injection steady‐state washout, denoted AIF(w), was calculated from MOLLI T1s and known relaxivity. Phase during washout was denoted φ(w). The quantity φ(t) – φ(w) was used to compute AIF – AIF(w). The final Phase+MOLLI AIF was then calculated by adding the MOLLI AIF(w). A “gold‐standard” AIF(w) was also obtained by sampling liquid in the input tube port of the phantom post‐DCE, then later measuring T1 with standard inversion recovery (IR). AIF(w) values from Phase‐only, MOLLI, and IR were compared using a two‐tailed paired t‐test. Results: AIF(w) from Phase‐only and MOLLI were significantly different (p = 0.04). AIF(w) from MOLLI and IR were the same (p = 0.89). The Phase‐only curve therefore incorrectly estimated AIF(w). Conclusion: There is currently no standard method for determining the AIF in DCE‐MRI. This work has shown that a Phase‐only AIF returns incorrect values for the washout portion of the curve. The Phase+MOLLI method could provide a more accurate and reproducible AIF in clinical DCE‐MRI, which could lead to better diagnoses.
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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 | 0.003 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".