ArticleJournal of cardiovascular development and disease2024
Improvement of Quantification of Myocardial Synthetic ECV with Second-Generation Deep Learning Reconstruction.
Article in Journal of cardiovascular development and disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- CT-derived myocardial extracellular volume: current evidence, clinical utility, and the emerging role of photon-counting CT-a systematic review.European radiology experimental · 2026Pooled it
- Myocardial extracellular volume fraction in cardiac CT: comparison of deep learning reconstruction and iterative reconstruction.Journal of thoracic disease · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe utility of synthetic ECV, which does not require hematocrit values, has been reported; however, high-quality CT images are essential for accurate quantification. Second-generation Deep Learning Reconstruction (DLR) enables low-noise and high-resolution cardiac CT images. The aim of this study is to compare the differences among four reconstruction methods (hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and second-generation DLR) in the quantification of synthetic ECV.
methodsWe retrospectively analyzed 80 patients who underwent cardiac CT scans, including late contrast-enhanced CT (derivation cohort:
resultsSynthetic ECV and laboratory ECV showed a high correlation across all four reconstruction methods (R ≥ 0.95,
conclusionsSynthetic ECV using second-generation DLR demonstrated the lowest bias and LOA compared to laboratory ECV among the four reconstruction methods, suggesting that second-generation DLR enables more accurate quantification.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.