Cell Density Quantification with TurboSPI: R2* Mapping with Compensation\n for Off-Resonance Fat Modulation
Bibliographic record
Abstract
Tracking the migration of superparamagnetic iron oxide (SPIO) labeled immune\ncells in vivo is valuable for understanding the immunogenic response to cancer\nand therapies. Quantitative cell tracking using compressed sensing\nTurboSPI-based R2* mapping is a promising development to improve accuracy in\nlongitudinal studies on immune recruitment. The phase-encoded TurboSPI sequence\nprovides high fidelity relaxation data in the form of signal time-courses with\nhigh temporal resolution. However, early in vivo applications of this method\nrevealed that simple mono-exponential R2* fitting performs poorly due to the\ncontaminant fat signal in voxels surrounding regions of interest, such as flank\ntumors and lymph nodes adjacent to adipose tissue. This is especially\nproblematic if there is poor infiltration to the tumor such that immune cells\nremain near the periphery. The presence of an off-resonance fat isochromat\nresults in modulations in the signal time-course can be erroneously fit as R2*\nsignal decay, thereby overestimating the density of SPIO labeled cells. Simply\nexcluding any voxel with fat-typical modulations results in underestimates in\nvoxels that have mixed content. We propose using a more comprehensive\ndual-decay (R2f* and R2w*) Dixon-based signal model that accounts for the\npotential presence of fat in a voxel to better estimate SPIO induced\nde-phasing. In silico single voxel simulations illustrate how the proposed\nsignal model provides stable R2w* estimates that are invariant to fat content.\nThe proposed dual-decay model outperforms previous methods when applied to in\nvitro samples of SPIO labeled cells and oil prepared with oil content >15%.\nPreliminary in vivo results show that, compared to previous methods, the\ndual-decay Dixon model improves the balance of R2* specificity versus\nsensitivity, which in turn will result in more reliable analysis in future cell\ntracking studies.\n
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".