Automatic mask generation using independent component analysis in dynamic contrast enhanced-MRI
Bibliographic record
Abstract
Studying image intensity change in each pixel in dynamic contrast enhanced (DCE)-MRI data enables differentiation of different tissue types based on their difference in contrast uptake. Pharmacokinetic modeling of tissues is commonly used to extract physiological parameters (i.e. Ktransand ve) from the intensity-time curves. In a two compartmental model the intensity-time curve of the feeding blood vessels or arterial input function (AIF) as well as the signal from extravascular space (ES) is required. As direct measurement of these quantities is not possible some assumptions are made to approximate their values. Any error in measuring these quantities results in an error in the measured physiological parameters. We propose using Independent component analysis (ICA) to generate an automatic mask for separating the two spaces and extracting their intensity-time curves. An experimental phantom is constructed to mimic the behavior of real tissues and the actual intensity-time curves for the AIF and ES are measured from its DCE-MRI images. Then ICA is applied to the DCE dataset to separate these spaces. The result show high degree of agreement between the actual and ICA results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 0.002 |
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".