Nuclear magnetic relaxation dispersion of murine tissue for development of <i>T</i><sub>1</sub> (<i>R</i><sub>1</sub>) dispersion contrast imaging
Bibliographic record
Abstract
This study quantified the spin–lattice relaxation rate ( R 1 ) dispersion of murine tissues from 0.24 mT to 3 T. A combination of ex vivo and in vivo spin–lattice relaxation rate measurements were acquired for murine tissue. Selected brain, liver, kidney, muscle, and fat tissues were excised and R 1 dispersion profiles were acquired from 0.24 mT to 1.0 T at 37 °C, using a fast field‐cycling MR (FFC‐MR) relaxometer . In vivo R 1 dispersion profiles of mice were acquired from 1.26 T to 1.74 T at 37 °C, using FFC‐MRI on a 1.5 T scanner outfitted with a field‐cycling insert electromagnet to dynamically control B 0 prior to imaging. Images at five field strengths (1.26, 1.39, 1.5, 1.61, 1.74 T) were acquired using a field‐cycling pulse sequence, where B 0 was modulated for varying relaxation durations prior to imaging. R 1 maps and R 1 dispersion (Δ R 1 /Δ B 0 ) were calculated at 1.5 T on a pixel‐by‐pixel basis. In addition, in vivo R 1 maps of mice were acquired at 3 T. At fields less than 1 T, a large R 1 magnetic field dependence was observed for tissues. ROI analysis of the tissues showed little relaxation dispersion for magnetic fields from 1.26 T to 3 T. Our tissue measurements show strong R 1 dispersion at field strengths less than 1 T and limited R 1 dispersion at field strengths greater than 1 T. These findings emphasize the inherent weak R 1 magnetic field dependence of healthy tissues at clinical field strengths. This characteristic of tissues can be exploited by a combination of FFC‐MRI and T 1 contrast agents that exhibit strong relaxivity magnetic field dependences (inherent or by binding to a protein), thereby increasing the agents' specificity and sensitivity. This development can provide potential insights into protein‐based biomarkers using FFC‐MRI to assess early changes in tumour development, which are not easily measureable with conventional MRI.
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How this classification was reachedexpand
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) | 0.001 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".