Simultaneous R<sub>2<sup>*</sup></sub>and quantitative susceptibility mapping measurement enables differentiation of thrombus hematocrit and age: an in vitro study at 3 T
Bibliographic record
Abstract
Background The efficacy of acute ischemic stroke treatment is affected by thrombus composition and age, yet no diagnostic method capable of quantitative thrombus characterization currently exists. This in vitro study evaluates the use of R2*, quantitative susceptibility mapping (QSM), and proton density fat fraction (FF) maps derived from a single gradient echo (GRE) MRI acquisition for characterizing clot of various hematocrit, as well as added calcified and lipidic components, throughout aging. Methods Two thrombus phantoms containing porcine clots (10–60% hematocrit, one with added calcium or lard) were scanned serially throughout 6 days of aging. Three-dimensional multi-echo GRE imaging was used to generate R2*, QSM, and FF maps, from which mean values for all clots at every time point were obtained. Receiver operating characteristic analysis was used to derive thresholds differentiating acute from chronic clot, and measured R2*and QSM were tested for their ability to estimate clot hematocrit. Results R2*and QSM varied minimally over the first 6 hours of aging (acute), and QSM was found to linearly relate to clot hematocrit. Beyond 6 hours (chronic), R2*and QSM increased considerably over time and hematocrit could be estimated from the R2*/QSM ratio. R2*and QSM thresholds of 22 s-1and 0.165 ppm differentiated acute from chronic clots with a sensitivity/specificity of 100%/100% and 85%/92%, respectively. QSM and FF maps definitively distinguished calcium and lipid, respectively, from clots of any hematocrit and age. Conclusions R2*, QSM, and FF from a single multi-echo GRE scan discriminated hematocrit and age, and distinguished calcification and lipid withinin vitro clot.
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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) | 0.000 | 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) | 0.001 | 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".