Utilizing T1- and T2-Specific Contrast Agents as “Two Colors” MRI Correlation
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is widely used as a medical imaging technique due to its non-invasive nature, high spatial contrast, and virtually unlimited depth of penetration. Different modalities can be used for contrast in MRI, including T1 (spin–lattice) and T2 or T2 * (spin–spin) proton relaxation times, and specific contrast agents (CAs) have been developed that locally enhance the contrasts in MRI images. We present a method combining T1- and T2-specific CAs in a single imaging technique, referred to as correlation MRI. This technique allows different CAs to be used simultaneously to visualize contrast between multiple types of tissue in the same image when applied as targeted CAs. An obstacle for the quantitative use of correlation MRI is that T1 and T2 relaxivity changes generated by CAs are not independent of each other. Here, we measured relaxivities in mixtures with various concentrations of Cas, including Magnevist (Gd3+-based, primarily a T1 CA) and Feridex (Fe2+- and Fe3+-based, primarily a T2 CA), and compared them to theoretically predicted values. It was found that, at clinically relevant concentrations, relaxivities of the mixtures deviate from linearly added values. We finally propose a three-dimensional calibration curve to quantitatively determine the concentration in mixtures of CAs, based on the measured relaxivities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".