Sci‐Fri AM: MRI and Diagnostic Imaging ‐ 05: Comparison of Input Function Measurements from DCE and MOLLI
Bibliographic record
Abstract
Dynamic contrast‐enhanced (DCE)‐MRI is a technique for obtaining tissue hemodynamic information (e.g. tumours). Despite widespread clinical application of DCE‐MRI, the technique suffers from a lack of standardization and accuracy, especially with respect to the concentration‐versus‐time of gadolinium (Gd) in feeding arteries (the input function, IF). MR phase has a linear quantitative relationship with Gd concentration ([Gd]), making it ideal for measuring the first‐pass of the IF, but is not considered accurate in the steady‐state washout. Modified Look‐Locker Inversion Recovery (MOLLI) is a fast and accurate method to measure T1 and has been validated to quantify typical [Gd] ranges experienced in the washout of the IF. Two different methods to measure the IF for DCE‐MRI were compared: 1) conventional phase‐versus‐time (“Phase‐only”) and 2) phase‐versus‐time combined with pre‐ and post‐DCE MOLLI T1 measurements (“Phase+MOLLI”). The IF obtained from Phase+MOLLI was calculated from MOLLI T1 values and known relaxivity, then added to the Phase‐only acquisition with the washout IF subtracted. A significant difference was observed between IF values for [Gd] between the Phase‐only and Phase+MOLLI acquisitions (P = 0.03). To ensure the IFs from MOLLI T1s were accurate, it was compared to [Gd] obtained from “gold‐standard” inversion recovery (IR). MOLLI showed excellent agreement with IR when imaged in static phantoms (r2 = 0.997, P = 0.001). The Phase+MOLLI IF was more accurate than the Phase‐only IF in measuring the washout. The Phase+MOLLI acquisition may therefore provide a DCE‐MRI reference standard that could lead to better clinical 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.082 | 0.031 |
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".