Dynamic tracking of magnetic nanoparticles for mapping microvascular networks using a clinical 1.5 T magnetic resonance scanner
Bibliographic record
Abstract
Being able to visualize blood vessels with an inner diameter of less than 150 μm is the present limit of modern medical imaging modalities and it becomes an important issue to advance state-of-the-art medical imaging, diagnostics, surgery, and targeted interventions. In cancer therapy, such capability would provide the information required for new delivery methods such as magnetic resonance navigation to navigate therapeutic agents along a planned trajectory deeper in the vasculature and hence closer to the region to be treated for enhancing the therapeutic index. To demonstrate the possibility of gathering images of microvascular networks dynamically and beyond the limitation of medical imaging modalities, the susceptibility artifact was used as the contrast mechanism in magnetic resonance imaging (MRI) to detect magnetic micro-aggregations of iron-oxide nanoparticles (150 ± 20 μm in diameter) as they were injected in a 2D synthetic microvascular network. Magnetic entities cause susceptibility artifacts in the images by disrupting the MRI's homogeneous magnetic field in a much larger scale than their actual size. The position of the artifact reflects the position of the aggregations in the vascular system. The calculated positions of discrete-time scans were extracted and assembled to build up the distribution of the vascular network. The results suggest that this method could be used to gather images of blood vessels beyond the spatial resolution of clinical medical imaging modalities with a measured average error confirmed on a 2D reconstruction of the micro-vessels of approximately half of a pixel's size.
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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) | 0.000 | 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 | 0.000 | 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".