Impact of motion on T1 mapping acquired with inversion recovery fast spin echo and rapid spoiled gradient recalled‐echo pulse sequences for delayed gadolinium‐enhanced MRI of cartilage (dGEMRIC) in volunteers
Bibliographic record
Abstract
PURPOSE: To evaluate the impact of motion on T1 values acquired by using either inversion-recovery fast spin echo (IR-FSE) or three-dimensional (3D) spoiled gradient recalled-echo (SPGR) sequences for delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC) in volunteers. MATERIALS AND METHODS: Single-slice IR-FSE and 3D SPGR sequences were applied to perform dGEMRIC in five healthy volunteers. A mutual information-based approach was used to correct for image misregistration. Displacements were expressed as averaged Euclidean distances and angles. Averages of differences in goodness of fit (Deltachi(2)) tests and averages of relative differences in T1 values (DeltaT1) before and after motion correction were computed. RESULTS: Maximum Euclidean distance was 3.5 mm and 1.2 mm for IR-FSE and SPGR respectively. Mean +/- SD of Deltachi(2) were 10.18 +/- 8.4 for IR-FSE and -1.37 +/- 5.5 for SPGR. Mean +/- SD of DeltaT1 were 0.008 +/- 0.0048 for IR-FSE and -0.002 +/- 0.019 for FSPGR. Pairwise comparison of Deltachi(2) values showed a significant difference for IR-FSE, but not for 3D-SPGR. Significantly greater variability in T1 values was also noted for IR-FSE than for 3D-SPGR. CONCLUSION: Involuntary motion has a significant influence on T1 values acquired with IR-FSE, but not with 3D-SPGR in healthy volunteers.
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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 | 0.004 |
| 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".