Multichannel joint image reconstruction allows for artifact-free focused small field of view magnetic particle imaging
Bibliographic record
Abstract
Abstract Magnetic Particle Imaging (MPI) directly detects superparamagnetic iron oxide (SPIO) labeled cells. We have used MPI to detect SPIO-labeled dendritic cells (DC) migrated to the popliteal lymph nodes (pLN) after injection into the hind footpads. However, in some cases the low pLN signal could not be resolved from nearby higher footpad signal where window leveling to pLN signal oversaturated the footpad signal. The same limitation occurs when SPIO is injected intravenously, accumulates in the liver, and prevents isolation of regions of interest with lower signals. Previous studies have reported on the issue of resolving a wide range of differing iron concentration. A small focused field of view (FOV), to exclude high sources of nearby signal cannot be performed with the standard reconstruction algorithm equipped on the MomentumTM MPI scanner because it is assumed that there is no signal at the edge of the FOV and these values are set to zero for each line along the transmit axis. However, when there is signal at the FOV edge, an inverted negative artifact is created. The multichannel joint reconstruction method uses an iterative reconstruction technique to recover edge information using information from an orthogonal axis, preventing this artifact and allowing the user to prescribe a small FOV on the region of interest. Here we describe the implementation of this method to isolate and quantify low regions of MPI signal from higher regions.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".