ArticleJournal of thoracic disease2026
Myocardial extracellular volume fraction in cardiac CT: comparison of deep learning reconstruction and iterative reconstruction.
Article in Journal of thoracic disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: With the expanding clinical application of cardiac computed tomography (CT) for myocardial tissue characterization, the accuracy of extracellular volume fraction (ECV) quantification is critically dependent on image quality. This study aimed to compare the performance of deep learning reconstruction (DLR), hybrid iterative reconstruction (HIR), and model-based iterative reconstruction (MBIR) in terms of image quality and the accuracy of ECV quantification in cardiac CT. Methods: Fifty patients with suspected cardiac disease who underwent both cardiac CT and magnetic resonance imaging (MRI) were prospectively enrolled. Non-contrast and delayed-phase CT images were reconstructed using HIR, MBIR, and DLR algorithms, respectively. Visual image noise, beam-hardening artifacts, and display of myocardium and blood pool were evaluated using a four-point scale. CT value, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of six segments of myocardium were measured and calculated. CT-ECV of six segments of myocardium was calculated in subtraction images. Pearson or Spearman correlation and Bland-Altman analysis were used to evaluate the correlation and consistency between CT-ECV and MRI-ECV in six segments of myocardium. Results: DLR significantly decreased image noise and beam-hardening artifacts, increasing SNR and CNR, providing superior visualization of the myocardium and blood pool compared to HIR and MBIR (P<0.001). In ECV quantification, DLR demonstrated a significantly lower mean absolute difference between CT-ECV and MRI-ECV across all six segments of myocardium (P<0.05). Compared with HIR and MBIR, DLR improved the correlation between CT-ECV and MRI-ECV (P<0.001). DLR showed the highest consistency in assessing ECV in all segments of myocardium (bias: -0.183) compared to HIR (bias: 0.390) and MBIR (bias: 0.540). Conclusions: Cardiac CT combined with DLR enhances the myocardial image quality and improves the accuracy of CT-ECV 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.