Quantitative Susceptibility Mapping for Staging Acute Cerebral Hemorrhages: Comparing the Conventional and <scp>Multiecho</scp> Complex Total Field Inversion magnetic resonance imaging <scp>MR</scp> Methods
Bibliographic record
Abstract
Background The perceived acuity of intracerebral hemorrhage (ICH) impacts the management of patients, both within emergent and outpatient/urgent settings. Morphology enabled dipole inversion (MEDI) quantitative susceptibility imaging (QSM) has improved characterization of ICH acuity, despite outstanding limitations in distinguishing blood products. Purpose/Hypothesis Using improved susceptibility quantification, novel postprocessing QSM method from multiecho complex total field inversion (mcTFI) may better discriminate between acute and subacute ICH, compared to MEDI. Study Type Retrospective cohort study. Subjects A total of 121 subjects enrolled following positive computerized tomography (CT) findings for ICH. Subjects were grouped based on time between admission and MR imaging: hyperacute (<24 hours), acute (1–3 days), early subacute (3–7 days), and late subacute (7–18 days). Field Strength/Sequence A multiecho gradient echo sequence at 3.0 T was paired with clinical noncontrast CT imaging. Assessment A quantitative index (CTindex) was derived based on relative intensities of blood on noncontrast CT. All images were co‐registered, from which QSM parameters within the ICH area were assessed across groups, as well as the correlation with CTindex. Statistical Tests Group differences were assessed using ANOVAs. Linear regressions between the CTindex, MEDI, and mcTFI measurements were used to assess their relationships. Statistical significance was set at P < 0.05. Results A total of 21 hyperacute, 72 acute, 21 early subacute, and 7 late‐subacute patients were included in this analysis. Significant changes in blood susceptibility were found over time for the MEDI and mcTFI, although mcTFI better differentiated the hyperacute/acute from subacute stages. CTindex values within the ICH were more strongly correlated with mcTFI QSM (r = 0.727) than MEDI (r = 0.412) QSM. Data conclusion McTFI susceptibility estimation demonstrated better correlation with ICH acuity as suggested by CT, providing an improved method to assess acuity of intracranial blood products in clinical settings to identify cases that may require acute intervention. Level of Evidence 4 Technical Efficacy Stage 2
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".