First order correction for T<sub>2</sub>*‐relaxation in determining contrast agent concentration from spoiled gradient echo pulse sequence signal intensity
Bibliographic record
Abstract
PURPOSE: To investigate the accuracy of a method neglecting T(2)*-relaxation, for the conversion of spoiled gradient echo pulse sequence signal intensity to contrast agent (CA) concentration, in dynamic contrast enhanced MRI studies. In addition a new closed form conversion expression is proposed that accounts for a first order approximation of T(2)*-relaxation. MATERIALS AND METHODS: The accuracy of both conversion methods is compared theoretically by means of simulations for four pulse sequences from literature. Both methods are tested in vivo against the numerical conversion method for measuring the arterial input function in mice. RESULTS: Simulations show that the T(2)*-neglecting method underestimates typical tissue CA concentrations (0 mM to 2 mM) up to 6%, while the errors for arterial concentrations (0 mM to 10 mM) range up to 43%. The results from our first order method are numerically indistinguishable from the simulation input values in tumor tissue, while for arterial concentrations the error is reduced up to a factor 10. In vivo, peak Gd-DOTA concentration is underestimated up to 14% with the T(2)*-neglecting method and up to 0.9% with our first order method. CONCLUSION: Our conversion method reduces the underestimation of CA concentration severely in a broad physiological concentration range and is easy to perform in any clinical setting.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".