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 (R1) 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 R1 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 R1 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 B0 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 B0 was modulated for varying relaxation durations prior to imaging. R1 maps and R1 dispersion (ΔR1/ΔB0) were calculated at 1.5 T on a pixel‐by‐pixel basis. In addition, in vivo R1 maps of mice were acquired at 3 T. At fields less than 1 T, a large R1 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 R1 dispersion at field strengths less than 1 T and limited R1 dispersion at field strengths greater than 1 T. These findings emphasize the inherent weak R1 magnetic field dependence of healthy tissues at clinical field strengths. This characteristic of tissues can be exploited by a combination of FFC‐MRI and T1 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".