Accurate positioning of magnetic microparticles beyond the spatial resolution of clinical MRI scanners using susceptibility artifacts
Bibliographic record
Abstract
Susceptibility-based negative contrast in magnetic resonance imaging (MRI) provides a mean to visualize magnetic microparticles. In the presence of a number of microparticles in the field of view (FOV), the shape of the artifact is affected by the dipole-dipole interaction between the particles. Due to the limited spatial resolution of the clinical MR scanners, the exact positioning of the particles in MR images is not possible. However, the shape of the artifact can shed light on how the particles are distributed within the FOV. In this work, a simulation model and in-vitro experiments were used to study the shape and the amount of the susceptibility artifact for various spacing and angulations between the microparticles. The results showed that for a pair of identical particles with a diameter of D, the signal loss starts to change when particles are separated ~15 × D and they become fully distinguishable when their distance reaches ~ 40 × D.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".