Effect of T1‐mapping technique and diminished image resolution on quantification of infarct mass and its ability in predicting appropriate <scp>ICD</scp> therapy
Bibliographic record
Abstract
PURPOSE: Myocardial infarct (MI) may consist of an infarct core (IC) and a heterogeneous, semi-viable border zone (BZ). Patients with chronic MI in the left ventricular (LV) myocardium are at increased risk of developing ventricular arrhythmias, and may therefore qualify for implantable cardioverter defibrillator (ICD) therapy. Indices based on MI mass, as determined by cardiac magnetic resonance (CMR) imaging, are shown to be sensitive in predicting adverse ventricular arrhythmic events. However, several factors, such as imaging technique and spatial resolution affect the accuracy of MI mass quantification. The aim of this study was to compare the MI masses determined by T1-mapping CMR techniques to those of conventional late Gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) using inversion recovery fast gradient echo (IR-FGRE). We additionally aimed to investigate the effect of diminishing image resolution on quantification of the MI mass and its ability to predict appropriate ICD therapy. METHODS: Thirty-eight patients with known MI underwent acquisitions of three CMR imaging techniques: the multicontrast late enhancement (MCLE) and modified look-locker inversion recovery (MOLLI) T1-mapping techniques, and conventional inversion recovery fast gradient echo (IR-FGRE) about 20 min after double-dose injection of Gadolinium. We postprocessed images to quantify IC and BZ masses determined by each CMR technique using a full-width half-maximum (FWHM) approach in IR-FGRE images and a fuzzy c-means clustering algorithm for T1-mapping images. To determine the impact of spatial resolution in sensitivity of predicting ICD events, we artificially diminished resolution of MCLE images acquired from a separate group of 27 patients who had been followed up for ICD therapy and compared the MI masses estimated from the original and downsampled MCLE images. RESULTS: Twelve patients out of 27 (44%) received ICD therapy (i.e., one or more delivered shock) during the follow-up stage. Between each of the three imaging methods, IC masses were not significantly different. Conversely, BZ masses determined by MOLLI were larger compared to those determined by MCLE and IR-FGRE (P value = 0.0022 and 0.0003, respectively). The BZ masses determined by MCLE were not significantly different from those determined by IR-FGRE; however, BZ masses determined by the downsampled MCLE were significantly larger than those determined by IR-FGRE and original MCLE (P value = 0.0033 and 0.0003, respectively). The BZ mass estimated by original MCLE was larger in patients who had received ICD therapy compared to those who did not (P value = 0.044). However, when the spatial resolution of the MCLE images was diminished to that of MOLLI, BZ masses were not significantly different between patients with and without ICD therapy. CONCLUSIONS: While estimated IC masses were consistent among all three techniques, the estimated BZ masses were not consistent, especially when spatial resolution of images differed between the techniques. In particular, our study showed that diminished image resolution caused an increase in estimation of the BZ mass, likely due to partial volume effects, which led to a reduced sensitivity in the prediction of appropriate ICD therapy.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".