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Myocardial extracellular volume fraction in cardiac CT: comparison of deep learning reconstruction and iterative
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.
