Let’s talk about cardiac T1 mapping
Bibliographic record
Abstract
ABSTRACT Background Recent reports have shown that T1 mapping sequences agree in phantoms, but exhibit significant differences in vivo. To characterize these differences in the heart, one needs to consider the effects of magnetization transfer (MT) and the T2 relaxation time in the most commonly used cardiac T1 mapping sequences (MOLLI, ShMOLLI and SASHA). Methods Six explanted pig hearts were scanned weekly over a period of six weeks on a 3T system with the MOLLI, ShMOLLI, SASHA sequences and an inversion recovery sequence as reference. The T1 bias was computed as the difference between MOLLI, ShMOLLI, SASHA and the reference T1 values. We applied robust correlation statistics to assess the relationships between T1, T2 and MT. All data are publicly available at: http://neuropoly.pub/pigHeartsData . Results A systematic T1 bias was present for all sequences, with MOLLI and ShMOLLI underestimating T1 and SASHA slightly overestimating T1 compared to the reference. The correlation of T1 bias with T2 was weak and insignificant. However, MT showed significant associations with T1 bias for all sequences. Our analysis is also available at: http://neuropoly.pub/pigHeartsInteractive . Conclusion We investigated cardiac T1 mapping sequences in a setting that allowed us to explore their accuracy and their dependence on T2 and MT effects. The T2 effects were not significant, and could not explain the T1 bias of MOLLI, ShMOLLI, SASHA with respect to the reference. On the other hand, the T1 biases exhibited a strong correlation with MT. We conclude that inaccuracies in cardiac T1 mapping are primarily due to magnetization transfer.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".