Boosting the velocity detection limit of <scp>3D</scp> single‐cell tracking time‐lapse <scp>MRI</scp> by <scp>balanced SSFP</scp> imaging
Bibliographic record
Abstract
PURPOSE: Time-lapse MRI allows for the dynamic tracking of single iron-labeled cells. However, the time required for spatial encoding creates a temporal blur and, therefore, a limited ability to resolve moving cells. To study fast moving cells, such as rolling immune cells along the endothelium during inflammatory processes, advanced accelerated acquisition techniques are required. METHODS: Balanced SSFP (bSSFP) imaging is applied to phantom and in vivo murine brain time-lapse MRI measurements at 9.4 T. Its detection capability of moving iron-labeled cells is compared with conventional gradient echo imaging (GRE) for 2D Cartesian sampling and evaluated for fully sampled and accelerated reconstructions with compressed sensing for 3D interleaved radial sampling in bSSFP. RESULTS: Both phantom and in vivo time-lapse MRI measurements show that single cells can be followed dynamically using bSSFP. High temporal resolution of less than 2 min reduces geometric distortion. The velocity detection limit increases to 0.8 mm/min in vitro and previously hidden fast-moving cells are recovered. Interleaved 3D radial sampling enables 3D cell tracking and simultaneous imaging at varying acceleration factors. Fivefold acceleration with compressed sensing optimizes cell visibility, image quality, and temporal resolution. CONCLUSION: bSSFP time-lapse MRI improves single-cell tracking by enhancing temporal resolution. In vitro, the velocity detection limit is increased fourfold compared to conventional GRE. Interleaved 3D radial bSSFP offers whole-brain coverage at isotropic spatial resolution and retrospective reconstruction of both fully sampled and high temporal resolution images.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".