Simultaneous iodine and barium imaging with photon-counting CT
Bibliographic record
Abstract
Abstract Objective. The objective of this study is to explore the capabilities of photon-counting computed tomography (PCCT) in simultaneously imaging and differentiating materials with close atomic numbers, specifically barium (Z = 56) and iodine (Z = 53), which is challenging for conventional computed tomography (CT). Approach. Experiments were conducted using a bench-top PCCT system equipped with a cadmium zinc telluride detector. Various phantom setups and contrast agent concentrations (1%–5%) were employed, along with a biological sample. Energy thresholds were tuned to the K-edge absorption energies of barium (37.4 keV) and iodine (33.2 keV) to capture multi-energy CT images. K-edge decomposition was performed using K-edge subtraction and principal component analysis (PCA) techniques to differentiate and quantify the contrast agents. Main results. The PCCT system successfully differentiated and accurately quantified barium and iodine in both phantom combinations and a biological sample, achieving high correlations ( R 2 ≈ 1 ) between true and reconstructed concentrations. PCA outperformed K-edge subtraction, particularly in the presence of calcium, by providing superior differentiation between barium and iodine. Significance. This study demonstrates the potential of PCCT for reliable, detailed imaging in both clinical and research settings, particularly for contrast agents with similar atomic numbers. The results suggest that PCCT could offer significant improvements in imaging quality over conventional CT, especially in applications requiring precise material differentiation.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".