Purpose To develop a deep learning-based method for fully automated quantification of left ventricular (LV) function from short-axis cine MR images and to evaluate its performance in a multivendor and multicenter setting. Materials and Methods This r...
Cardiovascular magnetic resonance imaging is the gold standard for cardiac function assessment. Quantification of clinical results (CR) requires precise segmentation. Clinicians statistically compare CRs to ensure reproducibility. Convolutional Neura...
The international journal of cardiovascular imaging
36648573
This study aimed to assess the image quality and accuracy of respiratory-gated real-time two-dimensional (2D) cine incorporating deep learning reconstruction (DLR) for the quantification of biventricular volumes and function compared with those of th...
The international journal of cardiovascular imaging
36826615
Reliability (repeatability or agreement) is assessed by different statistical tests including Pearson r or Spearman rho which is one of the common mistakes in reliability analysis. Pearson r or Spearman rho correlation coefficient only assesses the l...
Computer methods and programs in biomedicine
36773593
BACKGROUND AND OBJECTIVES: Parameter estimation and uncertainty quantification are crucial in computational cardiology, as they enable the construction of digital twins that faithfully replicate the behavior of physical patients. Many model parameter...
OBJECTIVE: The total examination time can be reduced if high-quality two-dimensional (2D) cine images can be collected post-contrast to minimize non-scanning time prior to late gadolinium-enhanced imaging. This study aimed to assess the equivalency o...