Relaxometry model of strong dipolar perturbers for balanced‐SSFP: Application to quantification of SPIO loaded cells
Bibliographic record
Abstract
Magnetic resonance microscopy using magnetically labeled cells is an emerging discipline offering the potential for non-destructive studies targeting numerous cellular events in medical research. The present work develops a technique to quantify superparamagnetic iron-oxide (SPIO) loaded cells using fully balanced steady state free precession (b-SSFP) imaging. An analytic model based on phase cancellation was derived for a single particle and extended to predict mono-exponential decay versus echo time in the presence of multiple randomly distributed particles. Numerical models verified phase incoherence as the dominant contrast mechanism and evaluated the model using a full range of tissue decay rates, repetition times, and flip angles. Numerical simulations indicated a relaxation rate enhancement (DeltaR(2b)=0.412 gamma . LMD) proportional to LMD, the local magnetic dose (the additional sample magnetization due to the SPIO particles), a quantity related to the concentration of contrast agent. A phantom model of SPIO loaded cells showed excellent agreement with simulations, demonstrated comparable sensitivity to gradient echo DeltaR(*) (2) enhancements, and 14 times the sensitivity of spin echo DeltaR(2) measurements. We believe this model can be used to facilitate the generation of quantitative maps of targeted cell populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".