Contrast-agent free evaluation of cardiomyopathies with T1 mapping and the new fast strain-encoded (fSENC) magnetic resonance imaging
Bibliographic record
Abstract
Abstract Background Cardiomyopathies (CMP) may cause impairment of cardiac function and structure. Cardiac Magnetic Resonance Imaging (CMR) is used for analysis and risk stratification of CMP by Late Gadolinium Enhancement (LGE). However, T1 mapping (T1) and fast strain encoded (f-SENC) sequences allow contrast-free and faster exams. The aim of this study was to characterize CMP by T1 and f-SENC to develop a faster and safer CMR protocol (fast-CMR). Methods CMP scans from our CMR database were retrospectively analyzed. All patients were scanned at 1.5T/3T scanner. Study groups were divided as follows: Patients with normal findings, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), hypertensive heart disease (HHD) and cardiac amyloidosis. Global T1 times, longitudinal (GLS) and circumferential (GCS) strain using f-SENC of study groups were compared to healthy individuals (controls). Scan time and amount of gadolinium-based contrast agent (CA) in CMR-protocol with LGE were compared to fast-CMR. Results 174 patients and 31 controls were recruited. T1 times, GLS and GCS were similar between controls and normal individuals. T1 times were significantly increased (p<0.05), while GLS and GCS were significantly reduced (p<0.05) in all CMR study groups compared to controls (Table 1). Using fast-CMR 21 (±6) min of scan time were saved, about 47%, and 9 (±2) ml of CA were saved per patient. Conclusion Normal findings could be identified by fast-CMR without contrast agent. Fast CMR might also be a useful tool to identify different forms of CMP. Funding Acknowledgement Type of funding source: None
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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 | 0.001 |
| 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 | 0.000 | 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".