Detection threshold of single SPIO‐labeled cells with FIESTA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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 it