Comparison of Rhenium and Iodine as Contrast Agents in X-Ray Imaging
Bibliographic record
Abstract
Purpose. The majority of X-ray contrast agents (XCA) are made with iodine, but iodine-based XCA (I-XCA) exhibit low contrast in high kVp X-rays due to iodine’s low atomic number (Z = 53) and K-edge (33.1 keV). While rhenium is a transition metal with a high atomic number (Z = 75) and K-edge (71.7 keV), the utilization of rhenium-based XCA (Re-XCA) in X-ray imaging techniques has not been studied in depth. Our study had two objectives: (1) to compare both the image quality and the absorbed dose of I- and Re-XCA and (2) to prepare and image a rhenium-doped scaffold. Procedures. I- and Re-XCA were prepared and imaged from 50 to 120 kVp by Micro-computed tomography (µCT) and digital radiography and from 120 to 220 kVp by planar X-ray imaging. The scans were repeated using 0.1 to 1.6 mm thick copper filters to harden the X-ray beam. A rhenium-doped scaffold was prepared via electrospinning, used to coat catheters, and imaged at 90 kVp by µCT. Results. I-XCA have a greater contrast-to-noise ratio (CNR) at 50 and 80 kVp, but Re-XCA have a greater CNR at >120 kVp. The difference in CNR is increased as the thickness of the copper filters is increased. For instance, the percent CNR improvement of rhenium over iodine is 14.2% with a 0.6 mm thick copper filter, but it is 59.1% with a 1.6 mm thick copper filter, as shown at 120 kVp by µCT. Upon coating them with a rhenium-doped scaffold, the catheters became radiopaque. Conclusions. Using Monte Carlo simulations, we showed that it is possible to reduce the absorbed dose of high kVp X-rays while allowing the acquisition of high-quality images. Furthermore, radiopaque catheters have the potential of enhancing the contrast during catheterizations and helping physicians to place catheters inside patients more rapidly and precisely.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".