Metallic copper as a fiducial marker for both CT and PET
Bibliographic record
Abstract
There is great interest in augmenting computed tomography (CT) with information gained from other imaging modalities. Positron emission tomography (PET) provides valuable data related to patient physiology to aid in the delineation of tumor volumes. Combining the information provided by these imaging modalities requires accurate spatial registration of the two data sets. Fiducial based mapping provides straightforward registration based on corresponding landmark points or fiducials in the two image sets. When external fiducials are employed, consistent intermodality marker placement and centroid identification are essential to achieving an accurate and reliable registration. Similarity of marker design between modalities greatly aides in achieving this goal. Solid copper may serve as a fiducial marker in both CT and PET. Small spheres or wires of copper are readily visible in CT while neutron activation of these same markers produces positron emitting Copper-64 for detection by PET. The use of identical shaped markers in both imaging modalities greatly simplifies the task of intermodality centroid matching. Copper has excellent machining properties and, prior to activation, is easy and safe to handle. The feasibility of Cu as a marker for both CT and PET is demonstrated using imaging phantoms.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".