Myocardial late gadolinium enhancement using delayed 3D IR-FLASH in the pediatric population: feasibility and diagnostic performance compared to single-shot PSIR-bSSFP
Bibliographic record
Abstract
Abstract Background This study compares three-dimensional (3D) high-resolution (HR) late gadolinium enhancement (LGE; 3D HR-LGE) imaging using a respiratory navigated, electrocardiographically-gated inversion recovery gradient echo sequence with conventional LGE imaging using a single-shot phase-sensitive inversion recovery (PSIR) balanced steady-state free precession (bSSFP; PSIR-bSSFP) sequence for routine clinical use in the pediatric population. Methods Pediatric patients (0–18 years) who underwent clinical cardiovascular magnetic resonance (CMR) with both 3D HR-LGE and single-shot PSIR-bSSFP LGE between January 2018 and June 2020 were included. Image quality (0–4) and detection of LGE in the left ventricle (LV) (per 17 segments), in the right ventricle (RV) (per 3 segments), as endocardial fibroelastosis (EFE), at the hinge points, and at the papillary muscles was analyzed by two blinded readers for each sequence. Ratios of the mean signal intensity of LGE to normal myocardium (LGE:Myo) and to LV blood pool (LGE:Blood) were recorded. Data is presented as median (1st–3rd quartiles). Wilcoxon signed rank test and chi-square analyses were used as appropriate. Inter-rater agreement was analyzed using weighted κ-statistics. Results 102 patients were included with median age at CMR of 8 (1–13) years-old and 44% of exams performed under general anesthesia. LGE was detected in 55% of cases. 3D HR LGE compared to single-shot PSIR-bSSFP had longer scan time [4:30 (3:35–5:34) vs 1:11 (0:47–1:32) minutes, p < 0.001], higher image quality ratings [3 (3–4) vs 2 (2–3), p < 0.001], higher LGE:Myo [23.7 (16.9–31.2) vs 5.0 (2.9–9.0), p < 0.001], detected more segments of LGE in both the LV [4 (2–8) vs 3 (1–7), p = 0.045] and RV [1 (1–1) vs 1 (0–1), p < 0.001], and also detected more cases of LGE with 13/56 (23%) of patients with LGE only detectable by 3D HR LGE (p < 0.001). 3D HR LGE specifically detected a greater proportion of RV LGE (27/27 vs 17/27, p < 0.001), EFE (11/11 vs 5/11, p = 0.004), and papillary muscle LGE (14/15 vs 4/15, p < 0.001). Inter-rater agreement for the recorded variables ranged from 0.42 to 1.00. Conclusions 3D HR LGE achieves greater image quality and detects more LGE than conventional single-shot PSIR-bSSFP LGE imaging, and should be considered an alternative to conventional LGE sequences for routine clinical use in the pediatric population.
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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 | 0.006 |
| 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 | 0.001 |
| 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".