Detection threshold of single SPIO‐labeled cells with FIESTA
Bibliographic record
Abstract
MRI of superparamagnetic iron oxide (SPIO)-labeled cells has become a valuable tool for studying the in vivo trafficking of transplanted cells. Cellular detection with MRI is generally considered to be orders of magnitude less sensitive than other techniques, such as positron emission tomography (PET), single photon emission-computed tomography (SPECT), or optical fluorescence microscopy. However, an analytic description of the detection threshold for single SPIO-labeled cells and the parameters that govern detection has not been adequately provided. In the present work, the detection threshold for single SPIO-labeled cells and the effect of resolution and SNR were studied for a balanced steady-state free precession (SSFP) sequence (3D-FIESTA). Based on the results from both theoretical and experimental analyses, an expression that predicts the minimum detectable mass of SPIO (m(c)) required to detect a single cell against a uniform signal background was derived: m(c) = 5v/(K(fsl) x SNR), where v is the voxel volume, SNR is the image signal-to-noise ratio, and K(fsl) is an empirical constant measured to be 6.2 +/- 0.5 x 10(-5) microl/pgFe. Using this expression, it was shown that the sensitivity of MRI is not very different from that of PET, requiring femtomole quantities of SPIO iron for detection under typical micro-imaging conditions (100 microm isotropic resolution, SNR = 60). The results of this work will aid in the design of cellular imaging experiments by defining the lower limit of SPIO labeling required for single cell detection at any given resolution and SNR.
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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 | 0.000 | 0.001 |
| 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".