A balanced filterless K-edge energy window multilayer detector for dual energy computed tomography
Bibliographic record
Abstract
Ross (or balanced) filter-based systems have been studied extensively in the past, however they have only recently been studied for medical applications such as computed tomography and contrast-enhanced mammography. Balanced filters are filters composed of different materials which have thicknesses designed to match the attenuation for all radiation energies except those within a certain energy window (between the K-edges of the filter materials). Images obtained using different filters to attenuate the incident x-rays can be subtracted to obtain an image which contains information solely within the energy window. The disadvantage of this image acquisition method is the requirement of a separate exposure for each filter. This can lead to motion artifacts in the resulting image for example due to cardiac, respiratory, or patient movement. In this paper we investigate a filterless, multilayer detector design using the general concept of balanced filters. In the proposed detector, energy discrimination is achieved using stacked layers of different conversion materials. Similar to how the thicknesses of balanced filters are chosen, the thicknesses of the conversion layers are designed to match the attenuation of x-rays except between the K-edges of the conversion materials. Motion artifacts are suppressed in the final image due to the simultaneous acquisition of images on all layers during a single exposure. The proposed multilayer design can be used for a number of applications depending on the energy range of interest. To study the proposed design, we consider dual energy computed tomography (CT) using a gadolinium-based contrast agent.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".