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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>≈</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> ) 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".