Theoretical comparison of energy-resolved and temporal-subtraction angiography
Bibliographic record
Abstract
Energy-resolving x-ray detectors may enable producing iodine-specific images of the coronary arteries without the presence of motion artifacts. We refer to this approach as energy-resolved angiography (ERA), which uses basis material decomposition to produce iodine-specific images. We compared the theoretical iodine pixel signal-to- noise ratio (SNR) and the zero-frequency SNR of ERA with that of conventional digital subtraction angiography (DSA), the latter of which produces iodine-specific images by subtracting images acquired before and after iodine injection. For both ERA and DSA, we modeled iodine SNR with and without the response of realistic x-ray detectors. For ERA, we used a validated model of the energy response of a cadmium zinc telluride (CZT) spectroscopic x-ray detector to account for spectral degradation and spatio-energetic cross talk due to charge sharing. For DSA, we modeled the response of a cesium-iodine (CsI)-based detector and validated our model by comparison with published data. Incorporating a realistic energy response for spectroscopic x-ray detectors decreased the pixel SNR and zero-frequency SNR by greater than a factor of two. In the case of DSA, optical blur in the scintillator increased iodine SNR relative to ideal systems, a result attributable to reduced high-frequency noise in the presence of optical blur. Our results suggest that, for the same patient x-ray exposure, the pixel SNR and zero-frequency SNR of ERA will be ~1/6 and ~1/3 of that DSA, respectively.
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 | 0.001 | 0.004 |
| 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) | 0.002 | 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".