Motion correction in dynamic contrast-enhanced magnetic resonance images using pharmacokinetic modeling
Bibliographic record
Abstract
Pharmacokinetic modeling is a mathematical modeling technique that examines the dynamics of concentration curves to reveal information about tissue microvasculature. Typically, image registration is performed as a pre- processing step to remove motion in dynamic contrast-enhanced (DCE) image sequences and ensure accurate pharmacokinetic analysis. In this work, we introduce a registration method for correcting motion in a sequence of DCE images. The proposed method involves the use of Tofts pharmacokinetic model to generate a sequence of reference images. These images simplify the challenging task of registering DCE images by pairing each frame in the motion- corrupted sequence with a reference image that resembles the overall contrast enhancement of the template. Abdominal DCE-MR images were used for validation. Reduction of motion in the registered sequence was observed both visually and quantitatively. Both global and local measures of registration accuracy obtained from the registered sequence and its associated signal intensity curves were smaller than their pre-registration counterparts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".