SU‐G‐IeP1‐06: Estimating Relative Tissue Density From Quantitative MR Images: A Novel Perspective for MRI‐Only Heterogeneity Corrected Dose Calculation
Bibliographic record
Abstract
Purpose: To explore the feasibility of extracting the relative density from quantitative MRI measurements as well as estimate a correlation between the extracted measures and CT Hounsfield units. Methods: MRI has the ability to separate water and fat signals, producing two separate images for each component. By performing appropriate corrections on the separated images, quantitative measurement of water and fat mass density can be estimated. This work aims to test this hypothesis on 1.5T.Peanut oil was used as fat‐representative, while agar as water‐representative. Gadolinium Chloride III and Sodium Chloride were added to the agar solution to adjust the relaxation times and the medium conductivity, respectively. Peanut oil was added to the agar solution with different percentages: 0%, 3%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% and 100%. The phantom was scanned on 1.5T GE Optima 450W with the body coil using a multigradient echo sequences. Water/fat separation were performed while correcting for main field (B0) inhomogeneity and T2* relaxation time. B1+ inhomogeneities were ignored. The phantom was subsequently scanned on a Philips Brilliance CT Big Bore. MR‐corrected fat signal from all vials were normalized to 100% fat signal. CT Hounsfield values were then compared to those obtained from the normalized MR‐corrected fat values as well as to the phantom for validation. Results: Good agreement were found between CT HU and the MR‐extracted fat values (R2 = 0.98). CT HU also showed excellent agreement with the prepared fat fractions (R2=0.99). Vials with 70%, 80%, and 90% fat percentages showed inhomogeneous distributions, however their results were included for completion. Conclusion: Quantitative MRI water/fat imaging can be potentially used to extract the relative tissue density. Further in‐vivo validation are required.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".