Effects of registration error on parametric response map analysis: a simulation study using liver CT-perfusion images
Bibliographic record
Abstract
Purpose: To investigate the effects of registration error (RE) on parametric response map (PRM) analysis of pre and post-radiotherapy (RT) functional images. Methods: Arterial blood flow maps (ABF) were generated from the CT-perfusion scans of 5 patients with hepatocellular carcinoma. ABF values within each patient map were modified to produce seven new ABF maps simulating 7 distinct post-RT functional change scenarios. Ground truth PRMs were generated for each patient by comparing the simulated and original ABF maps. Each simulated ABF map was then deformed by different magnitudes of realistic respiratory motion in order to simulate RE. PRMs were generated for each of the deformed maps and then compared to the ground truth PRMs to produce estimates of RE-induced misclassification. Main findings: The percentage of voxels misclassified as decreasing, no change, and increasing, increased with RE For all patients, increasing RE was observed to increase the number of high post-RT ABF voxels associated with low pre-RT ABF voxels and vice versa. 3 mm of average tumour RE resulted in 18-45% tumour voxel misclassification rates. Conclusions: RE induced misclassification posed challenges for PRM analysis in the liver where registration accuracy tends to be lower. Quantitative understanding of the sensitivity of the PRM method to registration error is required if PRMs are to be used to guide radiation therapy dose painting techniques.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".