Multimodal Contrast Agent for Combined Computed Tomography and Magnetic Resonance Imaging Applications
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine the feasibility of a multimodal system to effectively induce and maintain contrast enhancement in both computed tomography (CT) and magnetic resonance (MR) for radiation therapy applications. MATERIALS AND METHODS: The physicochemical characteristics of a liposome-encapsulated iohexol and gadoteridol formulation were assessed in terms of agent loading efficiencies, size and morphology, in vitro stability, and release kinetics. The imaging properties of the liposome formulation were assessed based on T1 and T2 relaxivity measurements and in vitro CT and MR imaging in a phantom. A preliminary imaging-based evaluation of the in vivo stability of this multimodal contrast agent was also performed in a lupine model. RESULTS: The average agent loading levels achieved were 26.5+/-3.8 mg/mL for iodine and 6.6+/- 1.5 mg/mL for gadolinium. These concentrations correspond to approximately 10% of that found in the commercially available preparations of each of these agents. However, this liposome-based formulation is expected to have a smaller volume of distribution and prolonged circulation lifetime in vivo. This multimodal system was found to have high agent retention in vitro, which translated into maintained contrast enhancement (up to 3 days) and stability in vivo. CONCLUSIONS: This study demonstrated the feasibility of engineering a multimodal contrast agent with prolonged contrast enhancement in vivo for use in CT and MR. This contrast agent may serve as a valuable tool for cardiovascular imaging as well as image registration and guidance applications in radiation therapy.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".