Chemical Shift MR Imaging Methods for the Quantification of Transcatheter Lipiodol Delivery to the Liver: Preclinical Feasibility Studies in a Rodent Model
Bibliographic record
Abstract
PURPOSE: To demonstrate the feasibility of using chemical shift magnetic resonance (MR) imaging fat-water separation methods for quantitative estimation of transcatheter lipiodol delivery to liver tissues. MATERIALS AND METHODS: Studies were performed in accordance with institutional Animal Care and Use Committee guidelines. Proton nuclear MR spectroscopy was first performed to identify lipiodol spectral peaks and relative amplitudes. Next, phantoms were constructed with increasing lipiodol-water volume fractions. A multiecho chemical shift-based fat-water separation method was used to quantify lipiodol concentration within each phantom. Six rats served as controls; 18 rats underwent catheterization with digital subtraction angiography guidance for intraportal infusion of a 15%, 30%, or 50% by volume lipiodol-saline mixture. MR imaging measurements were used to quantify lipiodol delivery to each rat liver. Lipiodol concentration maps were reconstructed by using both single-peak and multipeak chemical shift models. Intraclass and Spearman correlation coefficients were calculated for statistical comparison of MR imaging-based lipiodol concentration and volume measurements to reference standards (known lipiodol phantom compositions and the infused lipiodol dose during rat studies). RESULTS: Both single-peak and multipeak measurements were well correlated to phantom lipiodol concentrations (r(2) > 0.99). Lipiodol volume measurements were progressively and significantly higher when comparing between animals receiving different doses (P < .05 for each comparison). MR imaging-based lipiodol volume measurements strongly correlated with infused dose (intraclass correlation coefficients > 0.93, P < .001) with both single- and multipeak approaches. CONCLUSION: Chemical shift MR imaging fat-water separation methods can be used for quantitative measurements of lipiodol delivery to liver tissues.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".