Sci‐Fri AM: MRI and Diagnostic Imaging ‐ 03: The influence of sampling percentage in deformable registration on kinetic model analysis results in DCE‐MRI of the breast
Bibliographic record
Abstract
Purpose: Dynamic contrast enhanced (DCE) MRI is applied extensively for diagnosis and treatment monitoring of breast cancer. However, patient motion can introduce artificial variation in the signal enhancement curves. Non‐rigid registration can improve the curves but computation time can be long. Reducing the percentage of the image sampled (PS) can reduce time at the theoretical cost of registration accuracy. This work investigates the influence of PS on kinetic model analysis results and goodness‐of‐fit. Methods: DCE images were acquired using a 3T Siemens Biograph mMR. Deformable registration was performed on one patient dataset with 3Dslicer using PS values of 5, 20, and 100%. For three regions of interest within the tumor, tissue contrast agent concentration values versus time were generated and analyzed using the TOFTS pharmacokinetic model. Model parameters, their 95% confidence intervals and the coefficients of variation (CV), which served as a measure of goodness of fit, were recorded. Results: Computation time was approximately 16, 8, and 4 minutes/image for 100, 20, and 5 PS. The CV decreased following registration and there was a trend of decreasing CV with increasing PS. However, no differences in parameter values obtained with 100% PS and parameters values obtained with lower PS were observed. Substantial differences were found between parameter values obtained with versus without registration Conclusions: Increasing PS led to improved goodness‐of‐fit for the kinetic model analysis, at the expense of substantially increased computation time. However, this improved fit did not appear to influence parameter values for this patient.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".