Time-course study of a gold nanoparticle contrast agent for cardiac-gated micro-CT imaging in mice
Bibliographic record
Abstract
Although micro-computed tomography (micro-CT) images have high contrast for bone or air, between soft tissues the contrast is typically low. To overcome this inherent issue, attenuating exogenous contrast agents are used to provide contrast enhancement in the vasculature and abdominal organs. The aim of this study is to measure the contrast enhancement time course for a gold nanoparticle blood-pool contrast agent and use it to perform cardiac-gated 4D micro-CT scans of the heart. Six healthy female C57BL/6 mice were anesthetized and imaged after receiving an injected dose of MVivo gold nanoparticle blood-pool contrast agent. Following the injection, we performed micro-CT scans at 0, 0.25, 0.5, 0.75, 1, 2, 4, 8, 24, 48 and 72 h. The mean CT number was measured for 7 different organs. No contrast enhancement was noticed in the bladder, kidneys or muscle during the time-course study. However, it clearly appears that the contrast enhancement is high in both right ventricle and vena cava. To perform cardiac-gated imaging, either the gold nanoparticle agent (n = 3) or an iodine-based (n = 3) contrast agent was introduced and images representing 9 phases of the cardiac cycle were obtained in 6 additional mice. A few typical cardiac parameters were measured or calculated, with similar accuracy between the gold and iodinated agents, but better visualization of structures with the gold agent. The MVivo Au contrast agent can be used for investigations of cardiac or vascular disease with a single bolus injection, with an optimal cardiac imaging window identified during the first hour after injection, demonstrating similar image quality to iodinated contrast agents and excellent measurement accuracy. Furthermore, the long-lasting contrast enhancement of up to 8 h can be very useful for scanning protocols that require longer acquisition times.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".