Flip angle optimization for dynamic contrast-enhanced MRI-studies with spoiled gradient echo pulse sequences
Bibliographic record
Abstract
Spoiled gradient echo pulse (SPGRE) sequences are commonly used in dynamic contrast-enhanced MRI (DCE-MRI) studies to measure the contrast agent concentration in a tissue of interest over time. However, due to improper tuning of the SPGRE parameters, concentration uncertainty can be very high, even at high signal-to-noise ratio in the MR measurement. In this work, an optimization procedure is proposed for selecting the optimal value of the SPGRE-flip angle FA(opt), given the expected concentration range. The optimization condition ensures that every concentration in the assumed range has the lowest possible uncertainty. By decoupling the R(1)- and R*(2)-effects caused by the presence of the contrast agent, a contour plot has been generated from which FA(opt) can be read off for any study design. Investigation of ten recent DCE-MRI studies showed that improper flip angle selection unnecessarily increases the concentration uncertainty, up to 742% and 72% on average for the typical physiological concentration ranges of 0-2 mM in tumour tissue and 0-10 mM in blood, respectively. Simulations show that the reduced noise levels on the concentration curves, observed at the optimal flip angle, effectively increase the precision of the kinetic parameters estimates (up to 82% for K(trans), 82% for ν(e) and 92% for ν(p) in the case of an individually measured arterial input function (AIF), up to 53% for K(trans), 59% for ν(e) and 67% for ν(p) in the case of a standard AIF). In vivo experiments confirm the potential of flip angle optimization to increase the reproducibility of the kinetic parameter estimates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".