Multimodal Contrast Agent for Combined Computed Tomography and Magnetic Resonance Imaging Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| 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 it