Poster — Thur Eve — 37: Incorporation of K‐Edge Materials into Dual‐Energy X‐Ray Imaging Theory
Bibliographic record
Abstract
Dual‐energy x‐ray imaging is capable of enhancing the conspicuity of materials by removing extraneous contrast. Classic dual‐energy theory, however, does not encompass materials which have a K edge in the diagnostic energy range, such as iodine contrast agent. We attempt to extend dual‐energy theory to incorporate all materials. In the first approach, a third basis material is introduced to account for the discontinuity. The transmission of a human torso consisting of muscle, adipose and iodine can be represented by equivalent thicknesses of three basis materials chosen to be Lucite, aluminum and iodine. Material look‐alike unit vectors in the Lucite, aluminum and iodine space give the direction for projecting patient data to remove contrast between any two pairs of materials. This approach fails, however, if a contrast agent different from that used as a basis material is present, such as barium. A more general extension of dual‐energy theory would accommodate different K edges. In the second approach, a K‐edge material is represented by two basis materials and a Heaviside function shifted to the K edge and scaled to give the jump ratio. The scale factor is a slowly varying function of the energy shift: the jump ratio is 5.58 for iodine (Z=53) and 5.23 for barium (Z=56). This makes it possible to describe any material by three parameters. The next step will be to link the two disjoint approaches to obtain a general extension of dual‐energy theory, of which the current theory is a projection into the two‐basis plane.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".