Large deformation registration of contrast-enhanced images with volume-preserving constraint
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We propose a registration method for the alignment of contrast-enhanced CT liver images. It consists of a fluid-based registration algorithm designed to incorporate a volume-preserving constraint. More specifically our objective is to recover an accurate non-rigid transformation in a perfusion study in presence of contrast-enhanced structures which preserves the incompressibility of liver tissues. This transformation is obtained by integrating a smooth divergence-free vector field derived from the gradient of a statistical similarity measure. This gradient is regularized with a fast recursive low-pass filter and is projected onto the space of divergence-free vector fields using a multigrid solver. Both 2D and 3D versions of the algorithm have been implemented. Simulations and experiments show that our approach improves the registration capture range, enforces the imcompressibility constraint with a good level of accuracy, and is computationally efficient. On perfusion studies, this method prevents the shrinkage of contrast-enhanced regions typically observed with standard fluid methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 it