Simultaneous free-breathing T1, T2, and T1ρ mapping for myocardial fibrosis detection in non-ischemic cardiomyopathy: A comparative study with conventional techniques
Bibliographic record
Abstract
BACKGROUND: Quantitative myocardial mapping is critical for tissue characterization in non-ischemic cardiomyopathy (NICM). However, conventional techniques require separate breath-hold acquisitions, prolonging scan time and impairing co-registration. This study aimed to assess the feasibility and diagnostic performance of a novel free-breathing multimap (FBmultimap) sequence enabling simultaneous T1, T2, and T1ρ mapping in a single acquisition. METHODS: Onehundred-nine participants were prospectively enrolled, including 48 with hypertrophic cardiomyopathy (HCM), 28 with dilated cardiomyopathy (DCM), and 33 healthy controls. All underwent cardiac MRI with both FBmultimap and conventional mapping sequences (modified Look-Locker inversion recovery (MOLLI) T1, T2-prepared balanced steady-state free precession (bSSFP), and T1ρ-prepared bSSFP). Image quality was assessed using subjective (four-point Likert scale) and objective (edge sharpness) methods. Myocardial relaxation times were analyzed in the following two subgroups: (1) HCM and DCM vs. controls, and (2) late gadolinium enhancement (LGE)-positive and LGE-negative patients vs. controls. Combined diagnostic indices (T1 + T1ρ) were derived using logistic regression. Diagnostic performance was evaluated using receiver operating characteristic analysis across the following six models: FBmultimap (T1 + T1ρ), FBmultimap T1, FBmultimap T1ρ, conventional (T1 + T1ρ), MOLLI T1, and T1ρ-prepared bSSFP, with area under the curve (AUC) calculated. RESULTS: FBmultimap significantly reduced total scan time for T1 + T2 + T1ρ mapping to 66±6 s, compared with 195±10 s using conventional methods (p<0.001), while maintaining comparable image quality (all p>0.05). T1 and T1ρ values measured by FBmultimap were significantly elevated in HCM and DCM groups compared to controls, regardless of LGE status (all p<0.05), whereas T2 values showed no significant differences. FBmultimap (T1 + T1ρ) achieved higher AUCs for distinguishing LGE-positive (0.904) and LGE-negative (0.859) patients from controls than FBmultimap T1 (0.877 and 0.829), FBmultimap T1ρ (0.608 and 0.764), MOLLI T1 (0.770 and 0.671), T1ρ-prepared bSSFP (0.734 and 0.778), and the conventional (T1 + T1ρ) model (0.801 and 0.819). CONCLUSION: FBmultimap enables rapid, co-registered, free-breathing mapping of myocardial T1, T2, and T1ρ with high reproducibility and improved diagnostic performance over conventional single-parameter methods. It holds promise as a clinically applicable tool for myocardial fibrosis detection, risk stratification, and longitudinal monitoring in patients with HCM and DCM.
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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 | 0.003 | 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 | 0.001 |
| 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".