MPI region of interest (ROI) analysis and quantification of iron in different volumes
Bibliographic record
Abstract
Abstract MPI directly detects superparamagnetic iron oxides (SPIONs), which should enable precise, accurate, and linear quantification. However, selecting a region of interest (ROI) has strong effects on MPI quantification results. Ideally, ROI selection should be simple, user-independent, and widely applicable. In this work, we describe and compare four MPI ROI selection methods and assess their performance in vitro and in vivo. To explore the effect of ROI selection, ten ferucarbotran phantoms were imaged, each contained the same amount of iron but varied in volume. Three users tested the accuracy of the ROI methods for quantification of these samples. Lastly, the four ROI methods were applied to quantify ferucarbotran in vivo after intravenous, intramuscular, and subcutaneous injections in mice. We demonstrate that each ROI method has strengths. We conclude there is an important trade-off between ROI size and the accuracy of iron quantification, therefore the choice of ROI selection method for each study must be carefully informed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".