Abstract 4144487: Fully Ungated, Free-Breathing, 3-Dimentional T2* Mapping for Imaging Hemorrhagic Myocardial Infarction
Bibliographic record
Abstract
Introduction: T2* cardiac MRI (CMR) is the standard for detecting hemorrhagic myocardial infarction (MI). However, the conventional T2* CMR (2D breath-held, ECG-gated, multi-gradient-echo T2*) can suffer from limited spatial resolution and multiple motion artifacts. We developed a time-efficient, fully ungated, free breathing, 3D T2* mapping method for detecting and characterizing hemorrhagic MI (hMI). Methods: Our approach, developed using a low-rank tensor framework, was tested in a canine model with reperfused hMI. Animals (n=5) underwent CMR 3 days after reperfusion. Short-axis, conventional 2D and proposed 3D T2*-w images, and the corresponding LGE images were acquired in a 3T CMR system. T2* maps (8 echoes, 1.41-15.44 ms) were constructed using mono-exponential fitting. IMH extent was determined by measuring the weighted sum of the imaging slices with hypointense regions (based on ‘mean-2SD’ criterion) within the LGE positive territories. Image quality was assessed by two CMR experts using a Likert scale (1 – poor; and 5 - excellent). Results: Figure 1 shows representative conventional 2D, proposed 3D T2* images, along with LGE image for reference. T2* image scores were higher with the proposed than the conventional approach: 3.5 ± 0.5 (conventional) vs 3.8 ± 0.3 (proposed), p<0.05. IMH extent measured using the two approaches provided equivalent IMH extent (see Fig. 2) under stable imaging conditions. Conclusions: The proposed 3D T2* mapping can provide much needed improvement in image quality compared to conventional 2D T2* CMR for detection of hemorrhagic MI without the need for breath holding or cardiac gating, both of which are known problems in acute MI patients. Provided imaging conditions are favorable, we also found that the proposed and conventional methods yield equivalent estimates of IMH extent. Additional studies are needed to evaluate the benefits of the proposed approach in clinical setting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".