Discrepancy between regional left ventricular regional circumferential strain assessed by MR-tagging and by speckle tracking echocardiography
Bibliographic record
Abstract
Background: In recent years, myocardial strain imaging has gained an important place for the evaluation of cardiac patients. Global longitudinal strain assessed by speckle tracking echocardiography (STE) is now commonly used but circumferential strain remains less extensively studied. MR-tagging is recognized as the reference method for circum- ferential strain analysis, however no validation study between regional MR-tagging and regional STE has been performed up to now. Objective: To compare segmental circumferential strain values (Ecc) obtained by speckle tracking and by MR-tagging in patient with normal systolic function in order to define if both methods are interchangeable or not. Patients and methods: patients without significant regional nor global systolic dysfunction (LVEF > 55%) were studied by MR-tagging ( n =82) and by STE ( n =35). Left ventricular mid-level short axis slice was obtained by both methods and paired data were available in 16 patients. Segmental Ecc values were computed in six equidistant sectors using GE EchoPac software for STE and InTag post processing software for MR-tagging. Results: 1) Comparison between regions: Overall results showed that regional peak Ecc magnitude |Eccpeak| was not uniform with both methods but in an opposite way. MR-tagging demonstrated significantly lower septal |Eccpeak| as compared with postero-lateral |Eccpeak| (-16.5±3.6 vs -23.4±4.4, p <10 -4 ). Conversely, STE showed significantly higher septal |Eccpeak| as compared with postero-lateral |Eccpeak| (-22.3±6.4 vs -13.9±6.2, p <10 -4 ). 2) Comparison between both methods: In the subgroup of patients studied by both methods, septal |Eccpeak| was 29% lower by MR-tagging as compared with STE (-14.9±2.4 vs -20.9±6.5, p <.006) and postero-lateral |Eccpeak| was 39% lower by STE as compared with MR-tagging (-12.9±5.9 vs -21.0±2.9, p <.0003). 3) Intra and interobserver coefficients of variation were homogeneous (in the range 10%-14%) for all sectors with MR-tagging but were dramatically variable with STE (15% to 20% in the anterior-septal region but three times higher, in the range 35%-40%, in the postero-lateral territory). Conclusion: Regional distributions of Ecc is not uniform but opposite results are provided by MR-tagging and by STE. This finding demonstrates that both methods cannot be considered as interchangeable. These conflicting results raise the question of the validity of either MR tagging or speckle tracking for the quantification of regional circumferential strain. Some arguments, developed in the discussion would rather let believe that MR-tagging results should be more reliable.
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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".